Class: AWS.SageMaker
- Inherits:
-
AWS.Service
- Object
- AWS.Service
- AWS.SageMaker
- Identifier:
- sagemaker
- API Version:
- 2017-07-24
- Defined in:
- (unknown)
Overview
Constructs a service interface object. Each API operation is exposed as a function on service.
Service Description
Provides APIs for creating and managing SageMaker resources.
Other Resources:
Sending a Request Using SageMaker
var sagemaker = new AWS.SageMaker();
sagemaker.addAssociation(params, function (err, data) {
if (err) console.log(err, err.stack); // an error occurred
else console.log(data); // successful response
});
Locking the API Version
In order to ensure that the SageMaker object uses this specific API, you can
construct the object by passing the apiVersion
option to the constructor:
var sagemaker = new AWS.SageMaker({apiVersion: '2017-07-24'});
You can also set the API version globally in AWS.config.apiVersions
using
the sagemaker service identifier:
AWS.config.apiVersions = {
sagemaker: '2017-07-24',
// other service API versions
};
var sagemaker = new AWS.SageMaker();
Version:
-
2017-07-24
Waiter Resource States
This service supports a list of resource states that can be polled using the waitFor() method. The resource states are:
notebookInstanceInService, notebookInstanceStopped, notebookInstanceDeleted, trainingJobCompletedOrStopped, endpointInService, endpointDeleted, transformJobCompletedOrStopped, processingJobCompletedOrStopped, imageCreated, imageUpdated, imageDeleted, imageVersionCreated, imageVersionDeleted
Constructor Summary collapse
-
new AWS.SageMaker(options = {}) ⇒ Object
constructor
Constructs a service object.
Property Summary collapse
-
endpoint ⇒ AWS.Endpoint
readwrite
An Endpoint object representing the endpoint URL for service requests.
Properties inherited from AWS.Service
Method Summary collapse
-
addAssociation(params = {}, callback) ⇒ AWS.Request
Creates an association between the source and the destination.
-
addTags(params = {}, callback) ⇒ AWS.Request
Adds or overwrites one or more tags for the specified SageMaker resource.
-
associateTrialComponent(params = {}, callback) ⇒ AWS.Request
Associates a trial component with a trial.
-
batchDescribeModelPackage(params = {}, callback) ⇒ AWS.Request
This action batch describes a list of versioned model packages
.
-
createAction(params = {}, callback) ⇒ AWS.Request
Creates an action.
-
createAlgorithm(params = {}, callback) ⇒ AWS.Request
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
.
-
createApp(params = {}, callback) ⇒ AWS.Request
Creates a running app for the specified UserProfile.
-
createAppImageConfig(params = {}, callback) ⇒ AWS.Request
Creates a configuration for running a SageMaker image as a KernelGateway app.
-
createArtifact(params = {}, callback) ⇒ AWS.Request
Creates an artifact.
-
createAutoMLJob(params = {}, callback) ⇒ AWS.Request
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise.
-
createAutoMLJobV2(params = {}, callback) ⇒ AWS.Request
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise.
-
createCluster(params = {}, callback) ⇒ AWS.Request
Creates a SageMaker HyperPod cluster.
-
createCodeRepository(params = {}, callback) ⇒ AWS.Request
Creates a Git repository as a resource in your SageMaker account.
-
createCompilationJob(params = {}, callback) ⇒ AWS.Request
Starts a model compilation job.
-
createContext(params = {}, callback) ⇒ AWS.Request
Creates a context.
-
createDataQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates a definition for a job that monitors data quality and drift.
-
createDeviceFleet(params = {}, callback) ⇒ AWS.Request
Creates a device fleet.
.
-
createDomain(params = {}, callback) ⇒ AWS.Request
Creates a
Domain
. -
createEdgeDeploymentPlan(params = {}, callback) ⇒ AWS.Request
Creates an edge deployment plan, consisting of multiple stages.
-
createEdgeDeploymentStage(params = {}, callback) ⇒ AWS.Request
Creates a new stage in an existing edge deployment plan.
.
-
createEdgePackagingJob(params = {}, callback) ⇒ AWS.Request
Starts a SageMaker Edge Manager model packaging job.
-
createEndpoint(params = {}, callback) ⇒ AWS.Request
Creates an endpoint using the endpoint configuration specified in the request.
-
createEndpointConfig(params = {}, callback) ⇒ AWS.Request
Creates an endpoint configuration that SageMaker hosting services uses to deploy models.
-
createExperiment(params = {}, callback) ⇒ AWS.Request
Creates a SageMaker experiment.
-
createFeatureGroup(params = {}, callback) ⇒ AWS.Request
Create a new
FeatureGroup
. -
createFlowDefinition(params = {}, callback) ⇒ AWS.Request
Creates a flow definition.
.
-
createHub(params = {}, callback) ⇒ AWS.Request
Create a hub.
.
-
createHubContentReference(params = {}, callback) ⇒ AWS.Request
Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
.
-
createHumanTaskUi(params = {}, callback) ⇒ AWS.Request
Defines the settings you will use for the human review workflow user interface.
-
createHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Starts a hyperparameter tuning job.
-
createImage(params = {}, callback) ⇒ AWS.Request
Creates a custom SageMaker image.
-
createImageVersion(params = {}, callback) ⇒ AWS.Request
Creates a version of the SageMaker image specified by
ImageName
. -
createInferenceComponent(params = {}, callback) ⇒ AWS.Request
Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint.
-
createInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Creates an inference experiment using the configurations specified in the request.
-
createInferenceRecommendationsJob(params = {}, callback) ⇒ AWS.Request
Starts a recommendation job.
-
createLabelingJob(params = {}, callback) ⇒ AWS.Request
Creates a job that uses workers to label the data objects in your input dataset.
-
createMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.
-
createModel(params = {}, callback) ⇒ AWS.Request
Creates a model in SageMaker.
-
createModelBiasJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates the definition for a model bias job.
.
-
createModelCard(params = {}, callback) ⇒ AWS.Request
Creates an Amazon SageMaker Model Card.
For information about how to use model cards, see Amazon SageMaker Model Card.
. -
createModelCardExportJob(params = {}, callback) ⇒ AWS.Request
Creates an Amazon SageMaker Model Card export job.
.
-
createModelExplainabilityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates the definition for a model explainability job.
.
-
createModelPackage(params = {}, callback) ⇒ AWS.Request
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group.
-
createModelPackageGroup(params = {}, callback) ⇒ AWS.Request
Creates a model group.
-
createModelQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates a definition for a job that monitors model quality and drift.
-
createMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
.
-
createNotebookInstance(params = {}, callback) ⇒ AWS.Request
Creates an SageMaker notebook instance.
-
createNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Creates a lifecycle configuration that you can associate with a notebook instance.
-
createOptimizationJob(params = {}, callback) ⇒ AWS.Request
Creates a job that optimizes a model for inference performance.
-
createPipeline(params = {}, callback) ⇒ AWS.Request
Creates a pipeline using a JSON pipeline definition.
.
-
createPresignedDomainUrl(params = {}, callback) ⇒ AWS.Request
Creates a URL for a specified UserProfile in a Domain.
-
createPresignedMlflowTrackingServerUrl(params = {}, callback) ⇒ AWS.Request
Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server.
-
createPresignedNotebookInstanceUrl(params = {}, callback) ⇒ AWS.Request
Returns a URL that you can use to connect to the Jupyter server from a notebook instance.
-
createProcessingJob(params = {}, callback) ⇒ AWS.Request
Creates a processing job.
.
-
createProject(params = {}, callback) ⇒ AWS.Request
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
.
-
createSpace(params = {}, callback) ⇒ AWS.Request
Creates a private space or a space used for real time collaboration in a domain.
.
-
createStudioLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Creates a new Amazon SageMaker Studio Lifecycle Configuration.
.
-
createTrainingJob(params = {}, callback) ⇒ AWS.Request
Starts a model training job.
-
createTransformJob(params = {}, callback) ⇒ AWS.Request
Starts a transform job.
-
createTrial(params = {}, callback) ⇒ AWS.Request
Creates an SageMaker trial.
-
createTrialComponent(params = {}, callback) ⇒ AWS.Request
Creates a trial component, which is a stage of a machine learning trial.
-
createUserProfile(params = {}, callback) ⇒ AWS.Request
Creates a user profile.
-
createWorkforce(params = {}, callback) ⇒ AWS.Request
Use this operation to create a workforce.
-
createWorkteam(params = {}, callback) ⇒ AWS.Request
Creates a new work team for labeling your data.
-
deleteAction(params = {}, callback) ⇒ AWS.Request
Deletes an action.
.
-
deleteAlgorithm(params = {}, callback) ⇒ AWS.Request
Removes the specified algorithm from your account.
.
-
deleteApp(params = {}, callback) ⇒ AWS.Request
Used to stop and delete an app.
.
-
deleteAppImageConfig(params = {}, callback) ⇒ AWS.Request
Deletes an AppImageConfig.
.
-
deleteArtifact(params = {}, callback) ⇒ AWS.Request
Deletes an artifact.
-
deleteAssociation(params = {}, callback) ⇒ AWS.Request
Deletes an association.
.
-
deleteCluster(params = {}, callback) ⇒ AWS.Request
Delete a SageMaker HyperPod cluster.
.
-
deleteCodeRepository(params = {}, callback) ⇒ AWS.Request
Deletes the specified Git repository from your account.
.
-
deleteCompilationJob(params = {}, callback) ⇒ AWS.Request
Deletes the specified compilation job.
-
deleteContext(params = {}, callback) ⇒ AWS.Request
Deletes an context.
.
-
deleteDataQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes a data quality monitoring job definition.
.
-
deleteDeviceFleet(params = {}, callback) ⇒ AWS.Request
Deletes a fleet.
.
-
deleteDomain(params = {}, callback) ⇒ AWS.Request
Used to delete a domain.
-
deleteEdgeDeploymentPlan(params = {}, callback) ⇒ AWS.Request
Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
.
-
deleteEdgeDeploymentStage(params = {}, callback) ⇒ AWS.Request
Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
.
-
deleteEndpoint(params = {}, callback) ⇒ AWS.Request
Deletes an endpoint.
-
deleteEndpointConfig(params = {}, callback) ⇒ AWS.Request
Deletes an endpoint configuration.
-
deleteExperiment(params = {}, callback) ⇒ AWS.Request
Deletes an SageMaker experiment.
-
deleteFeatureGroup(params = {}, callback) ⇒ AWS.Request
Delete the
FeatureGroup
and any data that was written to theOnlineStore
of theFeatureGroup
. -
deleteFlowDefinition(params = {}, callback) ⇒ AWS.Request
Deletes the specified flow definition.
.
-
deleteHub(params = {}, callback) ⇒ AWS.Request
Delete a hub.
.
-
deleteHubContent(params = {}, callback) ⇒ AWS.Request
Delete the contents of a hub.
.
-
deleteHubContentReference(params = {}, callback) ⇒ AWS.Request
Delete a hub content reference in order to remove a model from a private hub.
.
-
deleteHumanTaskUi(params = {}, callback) ⇒ AWS.Request
Use this operation to delete a human task user interface (worker task template).
To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis.
-
deleteHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Deletes a hyperparameter tuning job.
-
deleteImage(params = {}, callback) ⇒ AWS.Request
Deletes a SageMaker image and all versions of the image.
-
deleteImageVersion(params = {}, callback) ⇒ AWS.Request
Deletes a version of a SageMaker image.
-
deleteInferenceComponent(params = {}, callback) ⇒ AWS.Request
Deletes an inference component.
.
-
deleteInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Deletes an inference experiment.
Note: This operation does not delete your endpoint, variants, or any underlying resources.- deleteMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Deletes an MLflow Tracking Server.
- deleteModel(params = {}, callback) ⇒ AWS.Request
Deletes a model.
- deleteModelBiasJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes an Amazon SageMaker model bias job definition.
.
- deleteModelCard(params = {}, callback) ⇒ AWS.Request
Deletes an Amazon SageMaker Model Card.
.
- deleteModelExplainabilityJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes an Amazon SageMaker model explainability job definition.
.
- deleteModelPackage(params = {}, callback) ⇒ AWS.Request
Deletes a model package.
A model package is used to create SageMaker models or list on Amazon Web Services Marketplace.
- deleteModelPackageGroup(params = {}, callback) ⇒ AWS.Request
Deletes the specified model group.
.
- deleteModelPackageGroupPolicy(params = {}, callback) ⇒ AWS.Request
Deletes a model group resource policy.
.
- deleteModelQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes the secified model quality monitoring job definition.
.
- deleteMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Deletes a monitoring schedule.
- deleteNotebookInstance(params = {}, callback) ⇒ AWS.Request
Deletes an SageMaker notebook instance.
- deleteNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Deletes a notebook instance lifecycle configuration.
.
- deleteOptimizationJob(params = {}, callback) ⇒ AWS.Request
Deletes an optimization job.
.
- deletePipeline(params = {}, callback) ⇒ AWS.Request
Deletes a pipeline if there are no running instances of the pipeline.
- deleteProject(params = {}, callback) ⇒ AWS.Request
Delete the specified project.
.
- deleteSpace(params = {}, callback) ⇒ AWS.Request
Used to delete a space.
.
- deleteStudioLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Deletes the Amazon SageMaker Studio Lifecycle Configuration.
- deleteTags(params = {}, callback) ⇒ AWS.Request
Deletes the specified tags from an SageMaker resource.
To list a resource's tags, use the
ListTags
API.- deleteTrial(params = {}, callback) ⇒ AWS.Request
Deletes the specified trial.
- deleteTrialComponent(params = {}, callback) ⇒ AWS.Request
Deletes the specified trial component.
- deleteUserProfile(params = {}, callback) ⇒ AWS.Request
Deletes a user profile.
- deleteWorkforce(params = {}, callback) ⇒ AWS.Request
Use this operation to delete a workforce.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce.
- deleteWorkteam(params = {}, callback) ⇒ AWS.Request
Deletes an existing work team.
- deregisterDevices(params = {}, callback) ⇒ AWS.Request
Deregisters the specified devices.
- describeAction(params = {}, callback) ⇒ AWS.Request
Describes an action.
.
- describeAlgorithm(params = {}, callback) ⇒ AWS.Request
Returns a description of the specified algorithm that is in your account.
.
- describeApp(params = {}, callback) ⇒ AWS.Request
Describes the app.
.
- describeAppImageConfig(params = {}, callback) ⇒ AWS.Request
Describes an AppImageConfig.
.
- describeArtifact(params = {}, callback) ⇒ AWS.Request
Describes an artifact.
.
- describeAutoMLJob(params = {}, callback) ⇒ AWS.Request
Returns information about an AutoML job created by calling CreateAutoMLJob.
Note: AutoML jobs created by calling CreateAutoMLJobV2 cannot be described byDescribeAutoMLJob
.- describeAutoMLJobV2(params = {}, callback) ⇒ AWS.Request
Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
.
- describeCluster(params = {}, callback) ⇒ AWS.Request
Retrieves information of a SageMaker HyperPod cluster.
.
- describeClusterNode(params = {}, callback) ⇒ AWS.Request
Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
.
- describeCodeRepository(params = {}, callback) ⇒ AWS.Request
Gets details about the specified Git repository.
.
- describeCompilationJob(params = {}, callback) ⇒ AWS.Request
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob.
- describeContext(params = {}, callback) ⇒ AWS.Request
Describes a context.
.
- describeDataQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Gets the details of a data quality monitoring job definition.
.
- describeDevice(params = {}, callback) ⇒ AWS.Request
Describes the device.
.
- describeDeviceFleet(params = {}, callback) ⇒ AWS.Request
A description of the fleet the device belongs to.
.
- describeDomain(params = {}, callback) ⇒ AWS.Request
The description of the domain.
.
- describeEdgeDeploymentPlan(params = {}, callback) ⇒ AWS.Request
Describes an edge deployment plan with deployment status per stage.
.
- describeEdgePackagingJob(params = {}, callback) ⇒ AWS.Request
A description of edge packaging jobs.
.
- describeEndpoint(params = {}, callback) ⇒ AWS.Request
Returns the description of an endpoint.
.
- describeEndpointConfig(params = {}, callback) ⇒ AWS.Request
Returns the description of an endpoint configuration created using the
CreateEndpointConfig
API..
- describeExperiment(params = {}, callback) ⇒ AWS.Request
Provides a list of an experiment's properties.
.
- describeFeatureGroup(params = {}, callback) ⇒ AWS.Request
Use this operation to describe a
FeatureGroup
.- describeFeatureMetadata(params = {}, callback) ⇒ AWS.Request
Shows the metadata for a feature within a feature group.
.
- describeFlowDefinition(params = {}, callback) ⇒ AWS.Request
Returns information about the specified flow definition.
.
- describeHub(params = {}, callback) ⇒ AWS.Request
Describes a hub.
.
- describeHubContent(params = {}, callback) ⇒ AWS.Request
Describe the content of a hub.
.
- describeHumanTaskUi(params = {}, callback) ⇒ AWS.Request
Returns information about the requested human task user interface (worker task template).
.
- describeHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Returns a description of a hyperparameter tuning job, depending on the fields selected.
- describeImage(params = {}, callback) ⇒ AWS.Request
Describes a SageMaker image.
.
- describeImageVersion(params = {}, callback) ⇒ AWS.Request
Describes a version of a SageMaker image.
.
- describeInferenceComponent(params = {}, callback) ⇒ AWS.Request
Returns information about an inference component.
.
- describeInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Returns details about an inference experiment.
.
- describeInferenceRecommendationsJob(params = {}, callback) ⇒ AWS.Request
Provides the results of the Inference Recommender job.
- describeLabelingJob(params = {}, callback) ⇒ AWS.Request
Gets information about a labeling job.
.
- describeLineageGroup(params = {}, callback) ⇒ AWS.Request
Provides a list of properties for the requested lineage group.
- describeMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Returns information about an MLflow Tracking Server.
.
- describeModel(params = {}, callback) ⇒ AWS.Request
Describes a model that you created using the
CreateModel
API..
- describeModelBiasJobDefinition(params = {}, callback) ⇒ AWS.Request
Returns a description of a model bias job definition.
.
- describeModelCard(params = {}, callback) ⇒ AWS.Request
Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
.
- describeModelCardExportJob(params = {}, callback) ⇒ AWS.Request
Describes an Amazon SageMaker Model Card export job.
.
- describeModelExplainabilityJobDefinition(params = {}, callback) ⇒ AWS.Request
Returns a description of a model explainability job definition.
.
- describeModelPackage(params = {}, callback) ⇒ AWS.Request
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API.
To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
.- describeModelPackageGroup(params = {}, callback) ⇒ AWS.Request
Gets a description for the specified model group.
.
- describeModelQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Returns a description of a model quality job definition.
.
- describeMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Describes the schedule for a monitoring job.
.
- describeNotebookInstance(params = {}, callback) ⇒ AWS.Request
Returns information about a notebook instance.
.
- describeNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Returns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
.- describeOptimizationJob(params = {}, callback) ⇒ AWS.Request
Provides the properties of the specified optimization job.
.
- describePipeline(params = {}, callback) ⇒ AWS.Request
Describes the details of a pipeline.
.
- describePipelineDefinitionForExecution(params = {}, callback) ⇒ AWS.Request
Describes the details of an execution's pipeline definition.
.
- describePipelineExecution(params = {}, callback) ⇒ AWS.Request
Describes the details of a pipeline execution.
.
- describeProcessingJob(params = {}, callback) ⇒ AWS.Request
Returns a description of a processing job.
.
- describeProject(params = {}, callback) ⇒ AWS.Request
Describes the details of a project.
.
- describeSpace(params = {}, callback) ⇒ AWS.Request
Describes the space.
.
- describeStudioLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Describes the Amazon SageMaker Studio Lifecycle Configuration.
.
- describeSubscribedWorkteam(params = {}, callback) ⇒ AWS.Request
Gets information about a work team provided by a vendor.
- describeTrainingJob(params = {}, callback) ⇒ AWS.Request
Returns information about a training job.
- describeTransformJob(params = {}, callback) ⇒ AWS.Request
Returns information about a transform job.
.
- describeTrial(params = {}, callback) ⇒ AWS.Request
Provides a list of a trial's properties.
.
- describeTrialComponent(params = {}, callback) ⇒ AWS.Request
Provides a list of a trials component's properties.
.
- describeUserProfile(params = {}, callback) ⇒ AWS.Request
Describes a user profile.
- describeWorkforce(params = {}, callback) ⇒ AWS.Request
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs).
- describeWorkteam(params = {}, callback) ⇒ AWS.Request
Gets information about a specific work team.
- disableSagemakerServicecatalogPortfolio(params = {}, callback) ⇒ AWS.Request
Disables using Service Catalog in SageMaker.
- disassociateTrialComponent(params = {}, callback) ⇒ AWS.Request
Disassociates a trial component from a trial.
- enableSagemakerServicecatalogPortfolio(params = {}, callback) ⇒ AWS.Request
Enables using Service Catalog in SageMaker.
- getDeviceFleetReport(params = {}, callback) ⇒ AWS.Request
Describes a fleet.
.
- getLineageGroupPolicy(params = {}, callback) ⇒ AWS.Request
The resource policy for the lineage group.
.
- getModelPackageGroupPolicy(params = {}, callback) ⇒ AWS.Request
Gets a resource policy that manages access for a model group.
- getSagemakerServicecatalogPortfolioStatus(params = {}, callback) ⇒ AWS.Request
Gets the status of Service Catalog in SageMaker.
- getScalingConfigurationRecommendation(params = {}, callback) ⇒ AWS.Request
Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job.
- getSearchSuggestions(params = {}, callback) ⇒ AWS.Request
An auto-complete API for the search functionality in the SageMaker console.
- importHubContent(params = {}, callback) ⇒ AWS.Request
Import hub content.
.
- listActions(params = {}, callback) ⇒ AWS.Request
Lists the actions in your account and their properties.
.
- listAlgorithms(params = {}, callback) ⇒ AWS.Request
Lists the machine learning algorithms that have been created.
.
- listAliases(params = {}, callback) ⇒ AWS.Request
Lists the aliases of a specified image or image version.
.
- listAppImageConfigs(params = {}, callback) ⇒ AWS.Request
Lists the AppImageConfigs in your account and their properties.
- listApps(params = {}, callback) ⇒ AWS.Request
Lists apps.
.
- listArtifacts(params = {}, callback) ⇒ AWS.Request
Lists the artifacts in your account and their properties.
.
- listAssociations(params = {}, callback) ⇒ AWS.Request
Lists the associations in your account and their properties.
.
- listAutoMLJobs(params = {}, callback) ⇒ AWS.Request
Request a list of jobs.
.
- listCandidatesForAutoMLJob(params = {}, callback) ⇒ AWS.Request
List the candidates created for the job.
.
- listClusterNodes(params = {}, callback) ⇒ AWS.Request
Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
.
- listClusters(params = {}, callback) ⇒ AWS.Request
Retrieves the list of SageMaker HyperPod clusters.
.
- listCodeRepositories(params = {}, callback) ⇒ AWS.Request
Gets a list of the Git repositories in your account.
.
- listCompilationJobs(params = {}, callback) ⇒ AWS.Request
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob.
- listContexts(params = {}, callback) ⇒ AWS.Request
Lists the contexts in your account and their properties.
.
- listDataQualityJobDefinitions(params = {}, callback) ⇒ AWS.Request
Lists the data quality job definitions in your account.
.
- listDeviceFleets(params = {}, callback) ⇒ AWS.Request
Returns a list of devices in the fleet.
.
- listDevices(params = {}, callback) ⇒ AWS.Request
A list of devices.
.
- listDomains(params = {}, callback) ⇒ AWS.Request
Lists the domains.
.
- listEdgeDeploymentPlans(params = {}, callback) ⇒ AWS.Request
Lists all edge deployment plans.
.
- listEdgePackagingJobs(params = {}, callback) ⇒ AWS.Request
Returns a list of edge packaging jobs.
.
- listEndpointConfigs(params = {}, callback) ⇒ AWS.Request
Lists endpoint configurations.
.
- listEndpoints(params = {}, callback) ⇒ AWS.Request
Lists endpoints.
.
- listExperiments(params = {}, callback) ⇒ AWS.Request
Lists all the experiments in your account.
- listFeatureGroups(params = {}, callback) ⇒ AWS.Request
List
FeatureGroup
s based on given filter and order..
- listFlowDefinitions(params = {}, callback) ⇒ AWS.Request
Returns information about the flow definitions in your account.
.
- listHubContents(params = {}, callback) ⇒ AWS.Request
List the contents of a hub.
.
- listHubContentVersions(params = {}, callback) ⇒ AWS.Request
List hub content versions.
.
- listHubs(params = {}, callback) ⇒ AWS.Request
List all existing hubs.
.
- listHumanTaskUis(params = {}, callback) ⇒ AWS.Request
Returns information about the human task user interfaces in your account.
.
- listHyperParameterTuningJobs(params = {}, callback) ⇒ AWS.Request
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
.
- listImages(params = {}, callback) ⇒ AWS.Request
Lists the images in your account and their properties.
- listImageVersions(params = {}, callback) ⇒ AWS.Request
Lists the versions of a specified image and their properties.
- listInferenceComponents(params = {}, callback) ⇒ AWS.Request
Lists the inference components in your account and their properties.
.
- listInferenceExperiments(params = {}, callback) ⇒ AWS.Request
Returns the list of all inference experiments.
.
- listInferenceRecommendationsJobs(params = {}, callback) ⇒ AWS.Request
Lists recommendation jobs that satisfy various filters.
.
- listInferenceRecommendationsJobSteps(params = {}, callback) ⇒ AWS.Request
Returns a list of the subtasks for an Inference Recommender job.
The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
.- listLabelingJobs(params = {}, callback) ⇒ AWS.Request
Gets a list of labeling jobs.
.
- listLabelingJobsForWorkteam(params = {}, callback) ⇒ AWS.Request
Gets a list of labeling jobs assigned to a specified work team.
.
- listLineageGroups(params = {}, callback) ⇒ AWS.Request
A list of lineage groups shared with your Amazon Web Services account.
- listMlflowTrackingServers(params = {}, callback) ⇒ AWS.Request
Lists all MLflow Tracking Servers.
.
- listModelBiasJobDefinitions(params = {}, callback) ⇒ AWS.Request
Lists model bias jobs definitions that satisfy various filters.
.
- listModelCardExportJobs(params = {}, callback) ⇒ AWS.Request
List the export jobs for the Amazon SageMaker Model Card.
.
- listModelCards(params = {}, callback) ⇒ AWS.Request
List existing model cards.
.
- listModelCardVersions(params = {}, callback) ⇒ AWS.Request
List existing versions of an Amazon SageMaker Model Card.
.
- listModelExplainabilityJobDefinitions(params = {}, callback) ⇒ AWS.Request
Lists model explainability job definitions that satisfy various filters.
.
- listModelMetadata(params = {}, callback) ⇒ AWS.Request
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
.
- listModelPackageGroups(params = {}, callback) ⇒ AWS.Request
Gets a list of the model groups in your Amazon Web Services account.
.
- listModelPackages(params = {}, callback) ⇒ AWS.Request
Lists the model packages that have been created.
.
- listModelQualityJobDefinitions(params = {}, callback) ⇒ AWS.Request
Gets a list of model quality monitoring job definitions in your account.
.
- listModels(params = {}, callback) ⇒ AWS.Request
Lists models created with the
CreateModel
API..
- listMonitoringAlertHistory(params = {}, callback) ⇒ AWS.Request
Gets a list of past alerts in a model monitoring schedule.
.
- listMonitoringAlerts(params = {}, callback) ⇒ AWS.Request
Gets the alerts for a single monitoring schedule.
.
- listMonitoringExecutions(params = {}, callback) ⇒ AWS.Request
Returns list of all monitoring job executions.
.
- listMonitoringSchedules(params = {}, callback) ⇒ AWS.Request
Returns list of all monitoring schedules.
.
- listNotebookInstanceLifecycleConfigs(params = {}, callback) ⇒ AWS.Request
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
.
- listNotebookInstances(params = {}, callback) ⇒ AWS.Request
Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
- listOptimizationJobs(params = {}, callback) ⇒ AWS.Request
Lists the optimization jobs in your account and their properties.
.
- listPipelineExecutions(params = {}, callback) ⇒ AWS.Request
Gets a list of the pipeline executions.
.
- listPipelineExecutionSteps(params = {}, callback) ⇒ AWS.Request
Gets a list of
PipeLineExecutionStep
objects..
- listPipelineParametersForExecution(params = {}, callback) ⇒ AWS.Request
Gets a list of parameters for a pipeline execution.
.
- listPipelines(params = {}, callback) ⇒ AWS.Request
Gets a list of pipelines.
.
- listProcessingJobs(params = {}, callback) ⇒ AWS.Request
Lists processing jobs that satisfy various filters.
.
- listProjects(params = {}, callback) ⇒ AWS.Request
Gets a list of the projects in an Amazon Web Services account.
.
- listResourceCatalogs(params = {}, callback) ⇒ AWS.Request
Lists Amazon SageMaker Catalogs based on given filters and orders.
- listSpaces(params = {}, callback) ⇒ AWS.Request
Lists spaces.
.
- listStageDevices(params = {}, callback) ⇒ AWS.Request
Lists devices allocated to the stage, containing detailed device information and deployment status.
.
- listStudioLifecycleConfigs(params = {}, callback) ⇒ AWS.Request
Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.
.
- listSubscribedWorkteams(params = {}, callback) ⇒ AWS.Request
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace.
- listTags(params = {}, callback) ⇒ AWS.Request
Returns the tags for the specified SageMaker resource.
.
- listTrainingJobs(params = {}, callback) ⇒ AWS.Request
Lists training jobs.
Note: WhenStatusEquals
andMaxResults
are set at the same time, theMaxResults
number of training jobs are first retrieved ignoring theStatusEquals
parameter and then they are filtered by theStatusEquals
parameter, which is returned as a response.- listTrainingJobsForHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
.
- listTransformJobs(params = {}, callback) ⇒ AWS.Request
Lists transform jobs.
.
- listTrialComponents(params = {}, callback) ⇒ AWS.Request
Lists the trial components in your account.
- listTrials(params = {}, callback) ⇒ AWS.Request
Lists the trials in your account.
- listUserProfiles(params = {}, callback) ⇒ AWS.Request
Lists user profiles.
.
- listWorkforces(params = {}, callback) ⇒ AWS.Request
Use this operation to list all private and vendor workforces in an Amazon Web Services Region.
- listWorkteams(params = {}, callback) ⇒ AWS.Request
Gets a list of private work teams that you have defined in a region.
- putModelPackageGroupPolicy(params = {}, callback) ⇒ AWS.Request
Adds a resouce policy to control access to a model group.
- queryLineage(params = {}, callback) ⇒ AWS.Request
Use this action to inspect your lineage and discover relationships between entities.
- registerDevices(params = {}, callback) ⇒ AWS.Request
Register devices.
.
- renderUiTemplate(params = {}, callback) ⇒ AWS.Request
Renders the UI template so that you can preview the worker's experience.
- retryPipelineExecution(params = {}, callback) ⇒ AWS.Request
Retry the execution of the pipeline.
.
- search(params = {}, callback) ⇒ AWS.Request
Finds SageMaker resources that match a search query.
- sendPipelineExecutionStepFailure(params = {}, callback) ⇒ AWS.Request
Notifies the pipeline that the execution of a callback step failed, along with a message describing why.
- sendPipelineExecutionStepSuccess(params = {}, callback) ⇒ AWS.Request
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters.
- startEdgeDeploymentStage(params = {}, callback) ⇒ AWS.Request
Starts a stage in an edge deployment plan.
.
- startInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Starts an inference experiment.
.
- startMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Programmatically start an MLflow Tracking Server.
.
- startMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Starts a previously stopped monitoring schedule.
Note: By default, when you successfully create a new schedule, the status of a monitoring schedule isscheduled
.- startNotebookInstance(params = {}, callback) ⇒ AWS.Request
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
- startPipelineExecution(params = {}, callback) ⇒ AWS.Request
Starts a pipeline execution.
.
- stopAutoMLJob(params = {}, callback) ⇒ AWS.Request
A method for forcing a running job to shut down.
.
- stopCompilationJob(params = {}, callback) ⇒ AWS.Request
Stops a model compilation job.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal.
- stopEdgeDeploymentStage(params = {}, callback) ⇒ AWS.Request
Stops a stage in an edge deployment plan.
.
- stopEdgePackagingJob(params = {}, callback) ⇒ AWS.Request
Request to stop an edge packaging job.
.
- stopHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3).
- stopInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Stops an inference experiment.
.
- stopInferenceRecommendationsJob(params = {}, callback) ⇒ AWS.Request
Stops an Inference Recommender job.
.
- stopLabelingJob(params = {}, callback) ⇒ AWS.Request
Stops a running labeling job.
- stopMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Programmatically stop an MLflow Tracking Server.
.
- stopMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Stops a previously started monitoring schedule.
.
- stopNotebookInstance(params = {}, callback) ⇒ AWS.Request
Terminates the ML compute instance.
- stopOptimizationJob(params = {}, callback) ⇒ AWS.Request
Ends a running inference optimization job.
.
- stopPipelineExecution(params = {}, callback) ⇒ AWS.Request
Stops a pipeline execution.
Callback Step
A pipeline execution won't stop while a callback step is running.
- stopProcessingJob(params = {}, callback) ⇒ AWS.Request
Stops a processing job.
.
- stopTrainingJob(params = {}, callback) ⇒ AWS.Request
Stops a training job.
- stopTransformJob(params = {}, callback) ⇒ AWS.Request
Stops a batch transform job.
When Amazon SageMaker receives a
StopTransformJob
request, the status of the job changes toStopping
.- updateAction(params = {}, callback) ⇒ AWS.Request
Updates an action.
.
- updateAppImageConfig(params = {}, callback) ⇒ AWS.Request
Updates the properties of an AppImageConfig.
.
- updateArtifact(params = {}, callback) ⇒ AWS.Request
Updates an artifact.
.
- updateCluster(params = {}, callback) ⇒ AWS.Request
Updates a SageMaker HyperPod cluster.
.
- updateClusterSoftware(params = {}, callback) ⇒ AWS.Request
Updates the platform software of a SageMaker HyperPod cluster for security patching.
- updateCodeRepository(params = {}, callback) ⇒ AWS.Request
Updates the specified Git repository with the specified values.
.
- updateContext(params = {}, callback) ⇒ AWS.Request
Updates a context.
.
- updateDeviceFleet(params = {}, callback) ⇒ AWS.Request
Updates a fleet of devices.
.
- updateDevices(params = {}, callback) ⇒ AWS.Request
Updates one or more devices in a fleet.
.
- updateDomain(params = {}, callback) ⇒ AWS.Request
Updates the default settings for new user profiles in the domain.
.
- updateEndpoint(params = {}, callback) ⇒ AWS.Request
Deploys the
EndpointConfig
specified in the request to a new fleet of instances.- updateEndpointWeightsAndCapacities(params = {}, callback) ⇒ AWS.Request
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.
- updateExperiment(params = {}, callback) ⇒ AWS.Request
Adds, updates, or removes the description of an experiment.
- updateFeatureGroup(params = {}, callback) ⇒ AWS.Request
Updates the feature group by either adding features or updating the online store configuration.
- updateFeatureMetadata(params = {}, callback) ⇒ AWS.Request
Updates the description and parameters of the feature group.
.
- updateHub(params = {}, callback) ⇒ AWS.Request
Update a hub.
.
- updateImage(params = {}, callback) ⇒ AWS.Request
Updates the properties of a SageMaker image.
- updateImageVersion(params = {}, callback) ⇒ AWS.Request
Updates the properties of a SageMaker image version.
.
- updateInferenceComponent(params = {}, callback) ⇒ AWS.Request
Updates an inference component.
.
- updateInferenceComponentRuntimeConfig(params = {}, callback) ⇒ AWS.Request
Runtime settings for a model that is deployed with an inference component.
.
- updateInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Updates an inference experiment that you created.
- updateMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Updates properties of an existing MLflow Tracking Server.
.
- updateModelCard(params = {}, callback) ⇒ AWS.Request
Update an Amazon SageMaker Model Card.
You cannot update both model card content and model card status in a single call.
- updateModelPackage(params = {}, callback) ⇒ AWS.Request
Updates a versioned model.
.
- updateMonitoringAlert(params = {}, callback) ⇒ AWS.Request
Update the parameters of a model monitor alert.
.
- updateMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Updates a previously created schedule.
.
- updateNotebookInstance(params = {}, callback) ⇒ AWS.Request
Updates a notebook instance.
- updateNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
.
- updatePipeline(params = {}, callback) ⇒ AWS.Request
Updates a pipeline.
.
- updatePipelineExecution(params = {}, callback) ⇒ AWS.Request
Updates a pipeline execution.
.
- updateProject(params = {}, callback) ⇒ AWS.Request
Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.
Note: You must not update a project that is in use.- updateSpace(params = {}, callback) ⇒ AWS.Request
Updates the settings of a space.
.
- updateTrainingJob(params = {}, callback) ⇒ AWS.Request
Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
.
- updateTrial(params = {}, callback) ⇒ AWS.Request
Updates the display name of a trial.
.
- updateTrialComponent(params = {}, callback) ⇒ AWS.Request
Updates one or more properties of a trial component.
.
- updateUserProfile(params = {}, callback) ⇒ AWS.Request
Updates a user profile.
.
- updateWorkforce(params = {}, callback) ⇒ AWS.Request
Use this operation to update your workforce.
- updateWorkteam(params = {}, callback) ⇒ AWS.Request
Updates an existing work team with new member definitions or description.
.
- waitFor(state, params = {}, callback) ⇒ AWS.Request
Waits for a given SageMaker resource.
Methods inherited from AWS.Service
makeRequest, makeUnauthenticatedRequest, setupRequestListeners, defineService
Constructor Details
new AWS.SageMaker(options = {}) ⇒ Object
Constructs a service object. This object has one method for each API operation.
Examples:
Constructing a SageMaker object
var sagemaker = new AWS.SageMaker({apiVersion: '2017-07-24'});
Options Hash (options):
-
params
(map)
—
An optional map of parameters to bind to every request sent by this service object. For more information on bound parameters, see "Working with Services" in the Getting Started Guide.
-
endpoint
(String|AWS.Endpoint)
—
The endpoint URI to send requests to. The default endpoint is built from the configured
region
. The endpoint should be a string like'https://{service}.{region}.amazonaws.com'
or an Endpoint object. -
accessKeyId
(String)
—
your AWS access key ID.
-
secretAccessKey
(String)
—
your AWS secret access key.
-
sessionToken
(AWS.Credentials)
—
the optional AWS session token to sign requests with.
-
credentials
(AWS.Credentials)
—
the AWS credentials to sign requests with. You can either specify this object, or specify the accessKeyId and secretAccessKey options directly.
-
credentialProvider
(AWS.CredentialProviderChain)
—
the provider chain used to resolve credentials if no static
credentials
property is set. -
region
(String)
—
the region to send service requests to. See AWS.SageMaker.region for more information.
-
maxRetries
(Integer)
—
the maximum amount of retries to attempt with a request. See AWS.SageMaker.maxRetries for more information.
-
maxRedirects
(Integer)
—
the maximum amount of redirects to follow with a request. See AWS.SageMaker.maxRedirects for more information.
-
sslEnabled
(Boolean)
—
whether to enable SSL for requests.
-
paramValidation
(Boolean|map)
—
whether input parameters should be validated against the operation description before sending the request. Defaults to true. Pass a map to enable any of the following specific validation features:
- min [Boolean] — Validates that a value meets the min
constraint. This is enabled by default when paramValidation is set
to
true
. - max [Boolean] — Validates that a value meets the max constraint.
- pattern [Boolean] — Validates that a string value matches a regular expression.
- enum [Boolean] — Validates that a string value matches one of the allowable enum values.
- min [Boolean] — Validates that a value meets the min
constraint. This is enabled by default when paramValidation is set
to
-
computeChecksums
(Boolean)
—
whether to compute checksums for payload bodies when the service accepts it (currently supported in S3 only)
-
convertResponseTypes
(Boolean)
—
whether types are converted when parsing response data. Currently only supported for JSON based services. Turning this off may improve performance on large response payloads. Defaults to
true
. -
correctClockSkew
(Boolean)
—
whether to apply a clock skew correction and retry requests that fail because of an skewed client clock. Defaults to
false
. -
s3ForcePathStyle
(Boolean)
—
whether to force path style URLs for S3 objects.
-
s3BucketEndpoint
(Boolean)
—
whether the provided endpoint addresses an individual bucket (false if it addresses the root API endpoint). Note that setting this configuration option requires an
endpoint
to be provided explicitly to the service constructor. -
s3DisableBodySigning
(Boolean)
—
whether S3 body signing should be disabled when using signature version
v4
. Body signing can only be disabled when using https. Defaults totrue
. -
s3UsEast1RegionalEndpoint
('legacy'|'regional')
—
when region is set to 'us-east-1', whether to send s3 request to global endpoints or 'us-east-1' regional endpoints. This config is only applicable to S3 client. Defaults to
legacy
-
s3UseArnRegion
(Boolean)
—
whether to override the request region with the region inferred from requested resource's ARN. Only available for S3 buckets Defaults to
true
-
retryDelayOptions
(map)
—
A set of options to configure the retry delay on retryable errors. Currently supported options are:
- base [Integer] — The base number of milliseconds to use in the exponential backoff for operation retries. Defaults to 100 ms for all services except DynamoDB, where it defaults to 50ms.
- customBackoff [function] — A custom function that accepts a
retry count and error and returns the amount of time to delay in
milliseconds. If the result is a non-zero negative value, no further
retry attempts will be made. The
base
option will be ignored if this option is supplied. The function is only called for retryable errors.
-
httpOptions
(map)
—
A set of options to pass to the low-level HTTP request. Currently supported options are:
- proxy [String] — the URL to proxy requests through
- agent [http.Agent, https.Agent] — the Agent object to perform
HTTP requests with. Used for connection pooling. Defaults to the global
agent (
http.globalAgent
) for non-SSL connections. Note that for SSL connections, a special Agent object is used in order to enable peer certificate verification. This feature is only available in the Node.js environment. - connectTimeout [Integer] — Sets the socket to timeout after
failing to establish a connection with the server after
connectTimeout
milliseconds. This timeout has no effect once a socket connection has been established. - timeout [Integer] — Sets the socket to timeout after timeout milliseconds of inactivity on the socket. Defaults to two minutes (120000).
- xhrAsync [Boolean] — Whether the SDK will send asynchronous HTTP requests. Used in the browser environment only. Set to false to send requests synchronously. Defaults to true (async on).
- xhrWithCredentials [Boolean] — Sets the "withCredentials" property of an XMLHttpRequest object. Used in the browser environment only. Defaults to false.
-
apiVersion
(String, Date)
—
a String in YYYY-MM-DD format (or a date) that represents the latest possible API version that can be used in all services (unless overridden by
apiVersions
). Specify 'latest' to use the latest possible version. -
apiVersions
(map<String, String|Date>)
—
a map of service identifiers (the lowercase service class name) with the API version to use when instantiating a service. Specify 'latest' for each individual that can use the latest available version.
-
logger
(#write, #log)
—
an object that responds to .write() (like a stream) or .log() (like the console object) in order to log information about requests
-
systemClockOffset
(Number)
—
an offset value in milliseconds to apply to all signing times. Use this to compensate for clock skew when your system may be out of sync with the service time. Note that this configuration option can only be applied to the global
AWS.config
object and cannot be overridden in service-specific configuration. Defaults to 0 milliseconds. -
signatureVersion
(String)
—
the signature version to sign requests with (overriding the API configuration). Possible values are: 'v2', 'v3', 'v4'.
-
signatureCache
(Boolean)
—
whether the signature to sign requests with (overriding the API configuration) is cached. Only applies to the signature version 'v4'. Defaults to
true
. -
dynamoDbCrc32
(Boolean)
—
whether to validate the CRC32 checksum of HTTP response bodies returned by DynamoDB. Default:
true
. -
useAccelerateEndpoint
(Boolean)
—
Whether to use the S3 Transfer Acceleration endpoint with the S3 service. Default:
false
. -
clientSideMonitoring
(Boolean)
—
whether to collect and publish this client's performance metrics of all its API requests.
-
endpointDiscoveryEnabled
(Boolean|undefined)
—
whether to call operations with endpoints given by service dynamically. Setting this
-
endpointCacheSize
(Number)
—
the size of the global cache storing endpoints from endpoint discovery operations. Once endpoint cache is created, updating this setting cannot change existing cache size. Defaults to 1000
-
hostPrefixEnabled
(Boolean)
—
whether to marshal request parameters to the prefix of hostname. Defaults to
true
. -
stsRegionalEndpoints
('legacy'|'regional')
—
whether to send sts request to global endpoints or regional endpoints. Defaults to 'legacy'.
-
useFipsEndpoint
(Boolean)
—
Enables FIPS compatible endpoints. Defaults to
false
. -
useDualstackEndpoint
(Boolean)
—
Enables IPv6 dualstack endpoint. Defaults to
false
.
Property Details
Method Details
addAssociation(params = {}, callback) ⇒ AWS.Request
Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.
Service Reference:
Examples:
Calling the addAssociation operation
var params = { DestinationArn: 'STRING_VALUE', /* required */ SourceArn: 'STRING_VALUE', /* required */ AssociationType: ContributedTo | AssociatedWith | DerivedFrom | Produced | SameAs }; sagemaker.addAssociation(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SourceArn
— (String
)The ARN of the source.
DestinationArn
— (String
)The Amazon Resource Name (ARN) of the destination.
AssociationType
— (String
)The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.
-
ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.
-
AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.
-
DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.
-
Produced - The source generated the destination. For example, a training job produced a model artifact.
"ContributedTo"
"AssociatedWith"
"DerivedFrom"
"Produced"
"SameAs"
-
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:SourceArn
— (String
)The ARN of the source.
DestinationArn
— (String
)The Amazon Resource Name (ARN) of the destination.
-
(AWS.Response)
—
Returns:
addTags(params = {}, callback) ⇒ AWS.Request
Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.
Note: Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in theTags
parameter of CreateHyperParameterTuningJobNote: Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in theTags
parameter of CreateDomain or CreateUserProfile.Service Reference:
Examples:
Calling the addTags operation
var params = { ResourceArn: 'STRING_VALUE', /* required */ Tags: [ /* required */ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.addTags(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ResourceArn
— (String
)The Amazon Resource Name (ARN) of the resource that you want to tag.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Tags
— (Array<map>
)A list of tags associated with the SageMaker resource.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
-
(AWS.Response)
—
Returns:
associateTrialComponent(params = {}, callback) ⇒ AWS.Request
Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
Service Reference:
Examples:
Calling the associateTrialComponent operation
var params = { TrialComponentName: 'STRING_VALUE', /* required */ TrialName: 'STRING_VALUE' /* required */ }; sagemaker.associateTrialComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialComponentName
— (String
)The name of the component to associated with the trial.
TrialName
— (String
)The name of the trial to associate with.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialComponentArn
— (String
)The Amazon Resource Name (ARN) of the trial component.
TrialArn
— (String
)The Amazon Resource Name (ARN) of the trial.
-
(AWS.Response)
—
Returns:
batchDescribeModelPackage(params = {}, callback) ⇒ AWS.Request
This action batch describes a list of versioned model packages
Service Reference:
Examples:
Calling the batchDescribeModelPackage operation
var params = { ModelPackageArnList: [ /* required */ 'STRING_VALUE', /* more items */ ] }; sagemaker.batchDescribeModelPackage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageArnList
— (Array<String>
)The list of Amazon Resource Name (ARN) of the model package groups.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageSummaries
— (map<map>
)The summaries for the model package versions
ModelPackageGroupName
— required — (String
)The group name for the model package
ModelPackageVersion
— (Integer
)The version number of a versioned model.
ModelPackageArn
— required — (String
)The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription
— (String
)The description of the model package.
CreationTime
— required — (Date
)The creation time of the mortgage package summary.
InferenceSpecification
— required — (map
)Defines how to perform inference generation after a training job is run.
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
ModelPackageStatus
— required — (String
)The status of the mortgage package.
Possible values include:"Pending"
"InProgress"
"Completed"
"Failed"
"Deleting"
ModelApprovalStatus
— (String
)The approval status of the model.
Possible values include:"Approved"
"Rejected"
"PendingManualApproval"
BatchDescribeModelPackageErrorMap
— (map<map>
)A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.
ErrorCode
— required — (String
)ErrorResponse
— required — (String
)
-
(AWS.Response)
—
Returns:
createAction(params = {}, callback) ⇒ AWS.Request
Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.
Service Reference:
Examples:
Calling the createAction operation
var params = { ActionName: 'STRING_VALUE', /* required */ ActionType: 'STRING_VALUE', /* required */ Source: { /* required */ SourceUri: 'STRING_VALUE', /* required */ SourceId: 'STRING_VALUE', SourceType: 'STRING_VALUE' }, Description: 'STRING_VALUE', MetadataProperties: { CommitId: 'STRING_VALUE', GeneratedBy: 'STRING_VALUE', ProjectId: 'STRING_VALUE', Repository: 'STRING_VALUE' }, Properties: { '<StringParameterValue>': 'STRING_VALUE', /* '<StringParameterValue>': ... */ }, Status: Unknown | InProgress | Completed | Failed | Stopping | Stopped, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createAction(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ActionName
— (String
)The name of the action. Must be unique to your account in an Amazon Web Services Region.
Source
— (map
)The source type, ID, and URI.
SourceUri
— required — (String
)The URI of the source.
SourceType
— (String
)The type of the source.
SourceId
— (String
)The ID of the source.
ActionType
— (String
)The action type.
Description
— (String
)The description of the action.
Status
— (String
)The status of the action.
Possible values include:"Unknown"
"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
Properties
— (map<String>
)A list of properties to add to the action.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
Tags
— (Array<map>
)A list of tags to apply to the action.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ActionArn
— (String
)The Amazon Resource Name (ARN) of the action.
-
(AWS.Response)
—
Returns:
createAlgorithm(params = {}, callback) ⇒ AWS.Request
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
Service Reference:
Examples:
Calling the createAlgorithm operation
var params = { AlgorithmName: 'STRING_VALUE', /* required */ TrainingSpecification: { /* required */ SupportedTrainingInstanceTypes: [ /* required */ ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge, /* more items */ ], TrainingChannels: [ /* required */ { Name: 'STRING_VALUE', /* required */ SupportedContentTypes: [ /* required */ 'STRING_VALUE', /* more items */ ], SupportedInputModes: [ /* required */ Pipe | File | FastFile, /* more items */ ], Description: 'STRING_VALUE', IsRequired: true || false, SupportedCompressionTypes: [ None | Gzip, /* more items */ ] }, /* more items */ ], TrainingImage: 'STRING_VALUE', /* required */ AdditionalS3DataSource: { S3DataType: S3Object | S3Prefix, /* required */ S3Uri: 'STRING_VALUE', /* required */ CompressionType: None | Gzip }, MetricDefinitions: [ { Name: 'STRING_VALUE', /* required */ Regex: 'STRING_VALUE' /* required */ }, /* more items */ ], SupportedHyperParameters: [ { Name: 'STRING_VALUE', /* required */ Type: Integer | Continuous | Categorical | FreeText, /* required */ DefaultValue: 'STRING_VALUE', Description: 'STRING_VALUE', IsRequired: true || false, IsTunable: true || false, Range: { CategoricalParameterRangeSpecification: { Values: [ /* required */ 'STRING_VALUE', /* more items */ ] }, ContinuousParameterRangeSpecification: { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE' /* required */ }, IntegerParameterRangeSpecification: { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE' /* required */ } } }, /* more items */ ], SupportedTuningJobObjectiveMetrics: [ { MetricName: 'STRING_VALUE', /* required */ Type: Maximize | Minimize /* required */ }, /* more items */ ], SupportsDistributedTraining: true || false, TrainingImageDigest: 'STRING_VALUE' }, AlgorithmDescription: 'STRING_VALUE', CertifyForMarketplace: true || false, InferenceSpecification: { Containers: [ /* required */ { Image: 'STRING_VALUE', /* required */ AdditionalS3DataSource: { S3DataType: S3Object | S3Prefix, /* required */ S3Uri: 'STRING_VALUE', /* required */ CompressionType: None | Gzip }, ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Framework: 'STRING_VALUE', FrameworkVersion: 'STRING_VALUE', ImageDigest: 'STRING_VALUE', ModelDataSource: { S3DataSource: { CompressionType: None | Gzip, /* required */ S3DataType: S3Prefix | S3Object, /* required */ S3Uri: 'STRING_VALUE', /* required */ HubAccessConfig: { HubContentArn: 'STRING_VALUE' /* required */ }, ModelAccessConfig: { AcceptEula: true || false /* required */ } } }, ModelDataUrl: 'STRING_VALUE', ModelInput: { DataInputConfig: 'STRING_VALUE' /* required */ }, NearestModelName: 'STRING_VALUE', ProductId: 'STRING_VALUE' }, /* more items */ ], SupportedContentTypes: [ 'STRING_VALUE', /* more items */ ], SupportedRealtimeInferenceInstanceTypes: [ ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.12xlarge | ml.m5d.24xlarge | ml.c4.large | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.large | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.12xlarge | ml.r5.24xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.12xlarge | ml.r5d.24xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.dl1.24xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.p4d.24xlarge | ml.c7g.large | ml.c7g.xlarge | ml.c7g.2xlarge | ml.c7g.4xlarge | ml.c7g.8xlarge | ml.c7g.12xlarge | ml.c7g.16xlarge | ml.m6g.large | ml.m6g.xlarge | ml.m6g.2xlarge | ml.m6g.4xlarge | ml.m6g.8xlarge | ml.m6g.12xlarge | ml.m6g.16xlarge | ml.m6gd.large | ml.m6gd.xlarge | ml.m6gd.2xlarge | ml.m6gd.4xlarge | ml.m6gd.8xlarge | ml.m6gd.12xlarge | ml.m6gd.16xlarge | ml.c6g.large | ml.c6g.xlarge | ml.c6g.2xlarge | ml.c6g.4xlarge | ml.c6g.8xlarge | ml.c6g.12xlarge | ml.c6g.16xlarge | ml.c6gd.large | ml.c6gd.xlarge | ml.c6gd.2xlarge | ml.c6gd.4xlarge | ml.c6gd.8xlarge | ml.c6gd.12xlarge | ml.c6gd.16xlarge | ml.c6gn.large | ml.c6gn.xlarge | ml.c6gn.2xlarge | ml.c6gn.4xlarge | ml.c6gn.8xlarge | ml.c6gn.12xlarge | ml.c6gn.16xlarge | ml.r6g.large | ml.r6g.xlarge | ml.r6g.2xlarge | ml.r6g.4xlarge | ml.r6g.8xlarge | ml.r6g.12xlarge | ml.r6g.16xlarge | ml.r6gd.large | ml.r6gd.xlarge | ml.r6gd.2xlarge | ml.r6gd.4xlarge | ml.r6gd.8xlarge | ml.r6gd.12xlarge | ml.r6gd.16xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.inf2.xlarge | ml.inf2.8xlarge | ml.inf2.24xlarge | ml.inf2.48xlarge | ml.p5.48xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge, /* more items */ ], SupportedResponseMIMETypes: [ 'STRING_VALUE', /* more items */ ], SupportedTransformInstanceTypes: [ ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge, /* more items */ ] }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], ValidationSpecification: { ValidationProfiles: [ /* required */ { ProfileName: 'STRING_VALUE', /* required */ TrainingJobDefinition: { /* required */ InputDataConfig: [ /* required */ { ChannelName: 'STRING_VALUE', /* required */ DataSource: { /* required */ FileSystemDataSource: { DirectoryPath: 'STRING_VALUE', /* required */ FileSystemAccessMode: rw | ro, /* required */ FileSystemId: 'STRING_VALUE', /* required */ FileSystemType: EFS | FSxLustre /* required */ }, S3DataSource: { S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE', /* required */ AttributeNames: [ 'STRING_VALUE', /* more items */ ], InstanceGroupNames: [ 'STRING_VALUE', /* more items */ ], S3DataDistributionType: FullyReplicated | ShardedByS3Key } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', InputMode: Pipe | File | FastFile, RecordWrapperType: None | RecordIO, ShuffleConfig: { Seed: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ CompressionType: GZIP | NONE, KmsKeyId: 'STRING_VALUE' }, ResourceConfig: { /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ InstanceCount: 'NUMBER_VALUE', InstanceGroups: [ { InstanceCount: 'NUMBER_VALUE', /* required */ InstanceGroupName: 'STRING_VALUE', /* required */ InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge /* required */ }, /* more items */ ], InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge, KeepAlivePeriodInSeconds: 'NUMBER_VALUE', VolumeKmsKeyId: 'STRING_VALUE' }, StoppingCondition: { /* required */ MaxPendingTimeInSeconds: 'NUMBER_VALUE', MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, TrainingInputMode: Pipe | File | FastFile, /* required */ HyperParameters: { '<HyperParameterKey>': 'STRING_VALUE', /* '<HyperParameterKey>': ... */ } }, TransformJobDefinition: { TransformInput: { /* required */ DataSource: { /* required */ S3DataSource: { /* required */ S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE' /* required */ } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', SplitType: None | Line | RecordIO | TFRecord }, TransformOutput: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ Accept: 'STRING_VALUE', AssembleWith: None | Line, KmsKeyId: 'STRING_VALUE' }, TransformResources: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge, /* required */ VolumeKmsKeyId: 'STRING_VALUE' }, BatchStrategy: MultiRecord | SingleRecord, Environment: { '<TransformEnvironmentKey>': 'STRING_VALUE', /* '<TransformEnvironmentKey>': ... */ }, MaxConcurrentTransforms: 'NUMBER_VALUE', MaxPayloadInMB: 'NUMBER_VALUE' } }, /* more items */ ], ValidationRole: 'STRING_VALUE' /* required */ } }; sagemaker.createAlgorithm(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AlgorithmName
— (String
)The name of the algorithm.
AlgorithmDescription
— (String
)A description of the algorithm.
TrainingSpecification
— (map
)Specifies details about training jobs run by this algorithm, including the following:
-
The Amazon ECR path of the container and the version digest of the algorithm.
-
The hyperparameters that the algorithm supports.
-
The instance types that the algorithm supports for training.
-
Whether the algorithm supports distributed training.
-
The metrics that the algorithm emits to Amazon CloudWatch.
-
Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.
-
The input channels that the algorithm supports for training data. For example, an algorithm might support
train
,validation
, andtest
channels.
TrainingImage
— required — (String
)The Amazon ECR registry path of the Docker image that contains the training algorithm.
TrainingImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
SupportedHyperParameters
— (Array<map>
)A list of the
HyperParameterSpecification
objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>Name
— required — (String
)The name of this hyperparameter. The name must be unique.
Description
— (String
)A brief description of the hyperparameter.
Type
— required — (String
)The type of this hyperparameter. The valid types are
Possible values include:Integer
,Continuous
,Categorical
, andFreeText
."Integer"
"Continuous"
"Categorical"
"FreeText"
Range
— (map
)The allowed range for this hyperparameter.
IntegerParameterRangeSpecification
— (map
)A
IntegerParameterRangeSpecification
object that defines the possible values for an integer hyperparameter.MinValue
— required — (String
)The minimum integer value allowed.
MaxValue
— required — (String
)The maximum integer value allowed.
ContinuousParameterRangeSpecification
— (map
)A
ContinuousParameterRangeSpecification
object that defines the possible values for a continuous hyperparameter.MinValue
— required — (String
)The minimum floating-point value allowed.
MaxValue
— required — (String
)The maximum floating-point value allowed.
CategoricalParameterRangeSpecification
— (map
)A
CategoricalParameterRangeSpecification
object that defines the possible values for a categorical hyperparameter.Values
— required — (Array<String>
)The allowed categories for the hyperparameter.
IsTunable
— (Boolean
)Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
IsRequired
— (Boolean
)Indicates whether this hyperparameter is required.
DefaultValue
— (String
)The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
SupportedTrainingInstanceTypes
— required — (Array<String>
)A list of the instance types that this algorithm can use for training.
SupportsDistributedTraining
— (Boolean
)Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
MetricDefinitions
— (Array<map>
)A list of
MetricDefinition
objects, which are used for parsing metrics generated by the algorithm.Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
TrainingChannels
— required — (Array<map>
)A list of
ChannelSpecification
objects, which specify the input sources to be used by the algorithm.Name
— required — (String
)The name of the channel.
Description
— (String
)A brief description of the channel.
IsRequired
— (Boolean
)Indicates whether the channel is required by the algorithm.
SupportedContentTypes
— required — (Array<String>
)The supported MIME types for the data.
SupportedCompressionTypes
— (Array<String>
)The allowed compression types, if data compression is used.
SupportedInputModes
— required — (Array<String>
)The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
SupportedTuningJobObjectiveMetrics
— (Array<map>
)A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
AdditionalS3DataSource
— (map
)The additional data source used during the training job.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
-
InferenceSpecification
— (map
)Specifies details about inference jobs that the algorithm runs, including the following:
-
The Amazon ECR paths of containers that contain the inference code and model artifacts.
-
The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.
-
The input and output content formats that the algorithm supports for inference.
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
-
ValidationSpecification
— (map
)Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.
ValidationRole
— required — (String
)The IAM roles that SageMaker uses to run the training jobs.
ValidationProfiles
— required — (Array<map>
)An array of
AlgorithmValidationProfile
objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.ProfileName
— required — (String
)The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
TrainingJobDefinition
— required — (map
)The
TrainingJobDefinition
object that describes the training job that SageMaker runs to validate your algorithm.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
HyperParameters
— (map<String>
)The hyperparameters used for the training job.
InputDataConfig
— required — (Array<map>
)An array of
Channel
objects, each of which specifies an input source.ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
OutputDataConfig
— required — (map
)the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— required — (map
)The resources, including the ML compute instances and ML storage volumes, to use for model training.
InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
StoppingCondition
— required — (map
)Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
TransformJobDefinition
— (map
)The
TransformJobDefinition
object that describes the transform job that SageMaker runs to validate your algorithm.MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB
— (Integer
)The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy
— (String
)A string that determines the number of records included in a single mini-batch.
SingleRecord
means only one record is used per mini-batch.MultiRecord
means a mini-batch is set to contain as many records that can fit within theMaxPayloadInMB
limit."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— required — (map
)A description of the input source and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— required — (map
)Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
TransformResources
— required — (map
)Identifies the ML compute instances for the transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. The default value is
1
, and the maximum is100
. For distributed transform jobs, specify a value greater than1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
CertifyForMarketplace
— (Boolean
)Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AlgorithmArn
— (String
)The Amazon Resource Name (ARN) of the new algorithm.
-
(AWS.Response)
—
Returns:
createApp(params = {}, callback) ⇒ AWS.Request
Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
Service Reference:
Examples:
Calling the createApp operation
var params = { AppName: 'STRING_VALUE', /* required */ AppType: JupyterServer | KernelGateway | DetailedProfiler | TensorBoard | CodeEditor | JupyterLab | RStudioServerPro | RSessionGateway | Canvas, /* required */ DomainId: 'STRING_VALUE', /* required */ ResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, SpaceName: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], UserProfileName: 'STRING_VALUE' }; sagemaker.createApp(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The user profile name. If this value is not set, then
SpaceName
must be set.SpaceName
— (String
)The name of the space. If this value is not set, then
UserProfileName
must be set.AppType
— (String
)The type of app.
Possible values include:"JupyterServer"
"KernelGateway"
"DetailedProfiler"
"TensorBoard"
"CodeEditor"
"JupyterLab"
"RStudioServerPro"
"RSessionGateway"
"Canvas"
AppName
— (String
)The name of the app.
Tags
— (Array<map>
)Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ResourceSpec
— (map
)The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
Note: The value ofInstanceType
passed as part of theResourceSpec
in theCreateApp
call overrides the value passed as part of theResourceSpec
configured for the user profile or the domain. IfInstanceType
is not specified in any of those threeResourceSpec
values for aKernelGateway
app, theCreateApp
call fails with a request validation error.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AppArn
— (String
)The Amazon Resource Name (ARN) of the app.
-
(AWS.Response)
—
Returns:
createAppImageConfig(params = {}, callback) ⇒ AWS.Request
Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image.
Service Reference:
Examples:
Calling the createAppImageConfig operation
var params = { AppImageConfigName: 'STRING_VALUE', /* required */ CodeEditorAppImageConfig: { ContainerConfig: { ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], ContainerEnvironmentVariables: { '<NonEmptyString256>': 'STRING_VALUE', /* '<NonEmptyString256>': ... */ } }, FileSystemConfig: { DefaultGid: 'NUMBER_VALUE', DefaultUid: 'NUMBER_VALUE', MountPath: 'STRING_VALUE' } }, JupyterLabAppImageConfig: { ContainerConfig: { ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], ContainerEnvironmentVariables: { '<NonEmptyString256>': 'STRING_VALUE', /* '<NonEmptyString256>': ... */ } }, FileSystemConfig: { DefaultGid: 'NUMBER_VALUE', DefaultUid: 'NUMBER_VALUE', MountPath: 'STRING_VALUE' } }, KernelGatewayImageConfig: { KernelSpecs: [ /* required */ { Name: 'STRING_VALUE', /* required */ DisplayName: 'STRING_VALUE' }, /* more items */ ], FileSystemConfig: { DefaultGid: 'NUMBER_VALUE', DefaultUid: 'NUMBER_VALUE', MountPath: 'STRING_VALUE' } }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createAppImageConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AppImageConfigName
— (String
)The name of the AppImageConfig. Must be unique to your account.
Tags
— (Array<map>
)A list of tags to apply to the AppImageConfig.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
KernelGatewayImageConfig
— (map
)The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.
KernelSpecs
— required — (Array<map>
)The specification of the Jupyter kernels in the image.
Name
— required — (String
)The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName
— (String
)The display name of the kernel.
FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
JupyterLabAppImageConfig
— (map
)The
JupyterLabAppImageConfig
. You can only specify one image kernel in theAppImageConfig
API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in JupyterLab.FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
ContainerConfig
— (map
)The configuration used to run the application image container.
ContainerArguments
— (Array<String>
)The arguments for the container when you're running the application.
ContainerEntrypoint
— (Array<String>
)The entrypoint used to run the application in the container.
ContainerEnvironmentVariables
— (map<String>
)The environment variables to set in the container
CodeEditorAppImageConfig
— (map
)The
CodeEditorAppImageConfig
. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in Code Editor.FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
ContainerConfig
— (map
)The configuration used to run the application image container.
ContainerArguments
— (Array<String>
)The arguments for the container when you're running the application.
ContainerEntrypoint
— (Array<String>
)The entrypoint used to run the application in the container.
ContainerEnvironmentVariables
— (map<String>
)The environment variables to set in the container
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AppImageConfigArn
— (String
)The ARN of the AppImageConfig.
-
(AWS.Response)
—
Returns:
createArtifact(params = {}, callback) ⇒ AWS.Request
Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.
Service Reference:
Examples:
Calling the createArtifact operation
var params = { ArtifactType: 'STRING_VALUE', /* required */ Source: { /* required */ SourceUri: 'STRING_VALUE', /* required */ SourceTypes: [ { SourceIdType: MD5Hash | S3ETag | S3Version | Custom, /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }, ArtifactName: 'STRING_VALUE', MetadataProperties: { CommitId: 'STRING_VALUE', GeneratedBy: 'STRING_VALUE', ProjectId: 'STRING_VALUE', Repository: 'STRING_VALUE' }, Properties: { '<StringParameterValue>': 'STRING_VALUE', /* '<StringParameterValue>': ... */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createArtifact(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ArtifactName
— (String
)The name of the artifact. Must be unique to your account in an Amazon Web Services Region.
Source
— (map
)The ID, ID type, and URI of the source.
SourceUri
— required — (String
)The URI of the source.
SourceTypes
— (Array<map>
)A list of source types.
SourceIdType
— required — (String
)The type of ID.
Possible values include:"MD5Hash"
"S3ETag"
"S3Version"
"Custom"
Value
— required — (String
)The ID.
ArtifactType
— (String
)The artifact type.
Properties
— (map<String>
)A list of properties to add to the artifact.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
Tags
— (Array<map>
)A list of tags to apply to the artifact.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ArtifactArn
— (String
)The Amazon Resource Name (ARN) of the artifact.
-
(AWS.Response)
—
Returns:
createAutoMLJob(params = {}, callback) ⇒ AWS.Request
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.
For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide.
Note: We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility.CreateAutoMLJobV2
can manage tabular problem types identical to those of its previous versionCreateAutoMLJob
, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate aCreateAutoMLJob
toCreateAutoMLJobV2
in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.
Service Reference:
Examples:
Calling the createAutoMLJob operation
var params = { AutoMLJobName: 'STRING_VALUE', /* required */ InputDataConfig: [ /* required */ { TargetAttributeName: 'STRING_VALUE', /* required */ ChannelType: training | validation, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', DataSource: { S3DataSource: { /* required */ S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE' /* required */ } }, SampleWeightAttributeName: 'STRING_VALUE' }, /* more items */ ], OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ AutoMLJobConfig: { CandidateGenerationConfig: { AlgorithmsConfig: [ { AutoMLAlgorithms: [ /* required */ xgboost | linear-learner | mlp | lightgbm | catboost | randomforest | extra-trees | nn-torch | fastai | cnn-qr | deepar | prophet | npts | arima | ets, /* more items */ ] }, /* more items */ ], FeatureSpecificationS3Uri: 'STRING_VALUE' }, CompletionCriteria: { MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE', MaxCandidates: 'NUMBER_VALUE', MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE' }, DataSplitConfig: { ValidationFraction: 'NUMBER_VALUE' }, Mode: AUTO | ENSEMBLING | HYPERPARAMETER_TUNING, SecurityConfig: { EnableInterContainerTrafficEncryption: true || false, VolumeKmsKeyId: 'STRING_VALUE', VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } } }, AutoMLJobObjective: { MetricName: Accuracy | MSE | F1 | F1macro | AUC | RMSE | BalancedAccuracy | R2 | Recall | RecallMacro | Precision | PrecisionMacro | MAE | MAPE | MASE | WAPE | AverageWeightedQuantileLoss /* required */ }, GenerateCandidateDefinitionsOnly: true || false, ModelDeployConfig: { AutoGenerateEndpointName: true || false, EndpointName: 'STRING_VALUE' }, ProblemType: BinaryClassification | MulticlassClassification | Regression, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createAutoMLJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AutoMLJobName
— (String
)Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
InputDataConfig
— (Array<map>
)An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to
InputDataConfig
supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.DataSource
— (map
)The data source for an AutoML channel.
S3DataSource
— required — (map
)The Amazon S3 location of the input data.
S3DataType
— required — (String
)The data type.
-
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.The
S3Prefix
should have the following format:s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
-
If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.A
ManifestFile
should have the format shown below:[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",
... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
-
If you choose
AugmentedManifestFile
,S3Uri
identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
is available for V2 API jobs only (for example, for jobs created by callingCreateAutoMLJobV2
).Here is a minimal, single-record example of an
AugmentedManifestFile
:{"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",
"label-metadata": {"class-name": "cat"
}For more information on
AugmentedManifestFile
, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
-
S3Uri
— required — (String
)The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
CompressionType
— (String
)You can use
Possible values include:Gzip
orNone
. The default value isNone
."None"
"Gzip"
TargetAttributeName
— required — (String
)The name of the target variable in supervised learning, usually represented by 'y'.
ContentType
— (String
)The content type of the data from the input source. You can use
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
.ChannelType
— (String
)The channel type (optional) is an
Possible values include:enum
string. The default value istraining
. Channels for training and validation must share the sameContentType
andTargetAttributeName
. For information on specifying training and validation channel types, see How to specify training and validation datasets."training"
"validation"
SampleWeightAttributeName
— (String
)If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
OutputDataConfig
— (map
)Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
KmsKeyId
— (String
)The Key Management Service encryption key ID.
S3OutputPath
— required — (String
)The Amazon S3 output path. Must be 512 characters or less.
ProblemType
— (String
)Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.
Possible values include:"BinaryClassification"
"MulticlassClassification"
"Regression"
AutoMLJobObjective
— (map
)Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.
MetricName
— required — (String
)The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
-
For tabular problem types:
-
List of available metrics:
-
Regression:
MAE
,MSE
,R2
,RMSE
-
Binary classification:
Accuracy
,AUC
,BalancedAccuracy
,F1
,Precision
,Recall
-
Multiclass classification:
Accuracy
,BalancedAccuracy
,F1macro
,PrecisionMacro
,RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression.
-
-
Default objective metrics:
-
Regression:
MSE
. -
Binary classification:
F1
. -
Multiclass classification:
Accuracy
.
-
-
-
For image or text classification problem types:
-
List of available metrics:
Accuracy
For a description of each metric, see Autopilot metrics for text and image classification.
-
Default objective metrics:
Accuracy
-
-
For time-series forecasting problem types:
-
List of available metrics:
RMSE
,wQL
,Average wQL
,MASE
,MAPE
,WAPE
For a description of each metric, see Autopilot metrics for time-series forecasting.
-
Default objective metrics:
AverageWeightedQuantileLoss
-
-
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the
AutoMLJobObjective
field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
-
AutoMLJobConfig
— (map
)A collection of settings used to configure an AutoML job.
CompletionCriteria
— (map
)How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
SecurityConfig
— (map
)The security configuration for traffic encryption or Amazon VPC settings.
VolumeKmsKeyId
— (String
)The key used to encrypt stored data.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to use traffic encryption between the container layers.
VpcConfig
— (map
)The VPC configuration.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
CandidateGenerationConfig
— (map
)The configuration for generating a candidate for an AutoML job (optional).
FeatureSpecificationS3Uri
— (String
)A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input
FeatureAttributeNames
(optional) in JSON format as shown below:{ "FeatureAttributeNames":["col1", "col2", ...] }
.You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Note: These column keys may not include the target column.In ensembling mode, Autopilot only supports the following data types:
numeric
,categorical
,text
, anddatetime
. In HPO mode, Autopilot can supportnumeric
,categorical
,text
,datetime
, andsequence
.If only
FeatureDataTypes
is provided, the column keys (col1
,col2
,..) should be a subset of the column names in the input data.If both
FeatureDataTypes
andFeatureAttributeNames
are provided, then the column keys should be a subset of the column names provided inFeatureAttributeNames
.The key name
FeatureAttributeNames
is fixed. The values listed in["col1", "col2", ...]
are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.AlgorithmsConfig
— (Array<map>
)Stores the configuration information for the selection of algorithms trained on tabular data.
The list of available algorithms to choose from depends on the training mode set in
TabularJobConfig.Mode
.-
AlgorithmsConfig
should not be set if the training mode is set onAUTO
. -
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for the given training mode. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for the given training mode.
For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.
AutoMLAlgorithms
— required — (Array<String>
)The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
-
For the tabular problem type
TabularJobConfig
:Note: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING
orHYPERPARAMETER_TUNING
). Choose a minimum of 1 algorithm.-
In
ENSEMBLING
mode:-
"catboost"
-
"extra-trees"
-
"fastai"
-
"lightgbm"
-
"linear-learner"
-
"nn-torch"
-
"randomforest"
-
"xgboost"
-
-
In
HYPERPARAMETER_TUNING
mode:-
"linear-learner"
-
"mlp"
-
"xgboost"
-
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
:-
Choose your algorithms from this list.
-
"cnn-qr"
-
"deepar"
-
"prophet"
-
"arima"
-
"npts"
-
"ets"
-
-
-
-
DataSplitConfig
— (map
)The configuration for splitting the input training dataset.
Type: AutoMLDataSplitConfig
ValidationFraction
— (Float
)The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
Mode
— (String
)The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting
AUTO
. InAUTO
mode, Autopilot choosesENSEMBLING
for datasets smaller than 100 MB, andHYPERPARAMETER_TUNING
for larger ones.The
ENSEMBLING
mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported byENSEMBLING
mode.The
Possible values include:HYPERPARAMETER_TUNING
(HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported byHYPERPARAMETER_TUNING
mode."AUTO"
"ENSEMBLING"
"HYPERPARAMETER_TUNING"
RoleArn
— (String
)The ARN of the role that is used to access the data.
GenerateCandidateDefinitionsOnly
— (Boolean
)Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ModelDeployConfig
— (map
)Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
AutoGenerateEndpointName
— (Boolean
)Set to
True
to automatically generate an endpoint name for a one-click Autopilot model deployment; set toFalse
otherwise. The default value isFalse
.Note: If you setAutoGenerateEndpointName
toTrue
, do not specify theEndpointName
; otherwise a 400 error is thrown.EndpointName
— (String
)Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note: Specify theEndpointName
if and only if you setAutoGenerateEndpointName
toFalse
; otherwise a 400 error is thrown.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AutoMLJobArn
— (String
)The unique ARN assigned to the AutoML job when it is created.
-
(AWS.Response)
—
Returns:
createAutoMLJobV2(params = {}, callback) ⇒ AWS.Request
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.
For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide.
AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.
Note: CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.CreateAutoMLJobV2
can manage tabular problem types identical to those of its previous versionCreateAutoMLJob
, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate aCreateAutoMLJob
toCreateAutoMLJobV2
in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.For the list of available problem types supported by
CreateAutoMLJobV2
, see AutoMLProblemTypeConfig.You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
Service Reference:
Examples:
Calling the createAutoMLJobV2 operation
var params = { AutoMLJobInputDataConfig: [ /* required */ { ChannelType: training | validation, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', DataSource: { S3DataSource: { /* required */ S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE' /* required */ } } }, /* more items */ ], AutoMLJobName: 'STRING_VALUE', /* required */ AutoMLProblemTypeConfig: { /* required */ ImageClassificationJobConfig: { CompletionCriteria: { MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE', MaxCandidates: 'NUMBER_VALUE', MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE' } }, TabularJobConfig: { TargetAttributeName: 'STRING_VALUE', /* required */ CandidateGenerationConfig: { AlgorithmsConfig: [ { AutoMLAlgorithms: [ /* required */ xgboost | linear-learner | mlp | lightgbm | catboost | randomforest | extra-trees | nn-torch | fastai | cnn-qr | deepar | prophet | npts | arima | ets, /* more items */ ] }, /* more items */ ] }, CompletionCriteria: { MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE', MaxCandidates: 'NUMBER_VALUE', MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE' }, FeatureSpecificationS3Uri: 'STRING_VALUE', GenerateCandidateDefinitionsOnly: true || false, Mode: AUTO | ENSEMBLING | HYPERPARAMETER_TUNING, ProblemType: BinaryClassification | MulticlassClassification | Regression, SampleWeightAttributeName: 'STRING_VALUE' }, TextClassificationJobConfig: { ContentColumn: 'STRING_VALUE', /* required */ TargetLabelColumn: 'STRING_VALUE', /* required */ CompletionCriteria: { MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE', MaxCandidates: 'NUMBER_VALUE', MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE' } }, TextGenerationJobConfig: { BaseModelName: 'STRING_VALUE', CompletionCriteria: { MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE', MaxCandidates: 'NUMBER_VALUE', MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE' }, ModelAccessConfig: { AcceptEula: true || false /* required */ }, TextGenerationHyperParameters: { '<TextGenerationHyperParameterKey>': 'STRING_VALUE', /* '<TextGenerationHyperParameterKey>': ... */ } }, TimeSeriesForecastingJobConfig: { ForecastFrequency: 'STRING_VALUE', /* required */ ForecastHorizon: 'NUMBER_VALUE', /* required */ TimeSeriesConfig: { /* required */ ItemIdentifierAttributeName: 'STRING_VALUE', /* required */ TargetAttributeName: 'STRING_VALUE', /* required */ TimestampAttributeName: 'STRING_VALUE', /* required */ GroupingAttributeNames: [ 'STRING_VALUE', /* more items */ ] }, CandidateGenerationConfig: { AlgorithmsConfig: [ { AutoMLAlgorithms: [ /* required */ xgboost | linear-learner | mlp | lightgbm | catboost | randomforest | extra-trees | nn-torch | fastai | cnn-qr | deepar | prophet | npts | arima | ets, /* more items */ ] }, /* more items */ ] }, CompletionCriteria: { MaxAutoMLJobRuntimeInSeconds: 'NUMBER_VALUE', MaxCandidates: 'NUMBER_VALUE', MaxRuntimePerTrainingJobInSeconds: 'NUMBER_VALUE' }, FeatureSpecificationS3Uri: 'STRING_VALUE', ForecastQuantiles: [ 'STRING_VALUE', /* more items */ ], HolidayConfig: [ { CountryCode: 'STRING_VALUE' }, /* more items */ ], Transformations: { Aggregation: { '<TransformationAttributeName>': sum | avg | first | min | max, /* '<TransformationAttributeName>': ... */ }, Filling: { '<TransformationAttributeName>': { '<FillingType>': 'STRING_VALUE', /* '<FillingType>': ... */ }, /* '<TransformationAttributeName>': ... */ } } } }, OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ AutoMLComputeConfig: { EmrServerlessComputeConfig: { ExecutionRoleARN: 'STRING_VALUE' /* required */ } }, AutoMLJobObjective: { MetricName: Accuracy | MSE | F1 | F1macro | AUC | RMSE | BalancedAccuracy | R2 | Recall | RecallMacro | Precision | PrecisionMacro | MAE | MAPE | MASE | WAPE | AverageWeightedQuantileLoss /* required */ }, DataSplitConfig: { ValidationFraction: 'NUMBER_VALUE' }, ModelDeployConfig: { AutoGenerateEndpointName: true || false, EndpointName: 'STRING_VALUE' }, SecurityConfig: { EnableInterContainerTrafficEncryption: true || false, VolumeKmsKeyId: 'STRING_VALUE', VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createAutoMLJobV2(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AutoMLJobName
— (String
)Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
AutoMLJobInputDataConfig
— (Array<map>
)An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the
CreateAutoMLJob
input parameters. The supported formats depend on the problem type:-
For tabular problem types:
S3Prefix
,ManifestFile
. -
For image classification:
S3Prefix
,ManifestFile
,AugmentedManifestFile
. -
For text classification:
S3Prefix
. -
For time-series forecasting:
S3Prefix
. -
For text generation (LLMs fine-tuning):
S3Prefix
.
ChannelType
— (String
)The type of channel. Defines whether the data are used for training or validation. The default value is
training
. Channels fortraining
andvalidation
must share the sameContentType
Note: The type of channel defaults toPossible values include:training
for the time-series forecasting problem type."training"
"validation"
ContentType
— (String
)The content type of the data from the input source. The following are the allowed content types for different problems:
-
For tabular problem types:
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
. -
For image classification:
image/png
,image/jpeg
, orimage/*
. The default value isimage/*
. -
For text classification:
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
. -
For time-series forecasting:
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
. -
For text generation (LLMs fine-tuning):
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
.
-
CompressionType
— (String
)The allowed compression types depend on the input format and problem type. We allow the compression type
Possible values include:Gzip
forS3Prefix
inputs on tabular data only. For all other inputs, the compression type should beNone
. If no compression type is provided, we default toNone
."None"
"Gzip"
DataSource
— (map
)The data source for an AutoML channel (Required).
S3DataSource
— required — (map
)The Amazon S3 location of the input data.
S3DataType
— required — (String
)The data type.
-
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.The
S3Prefix
should have the following format:s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
-
If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.A
ManifestFile
should have the format shown below:[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",
... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
-
If you choose
AugmentedManifestFile
,S3Uri
identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
is available for V2 API jobs only (for example, for jobs created by callingCreateAutoMLJobV2
).Here is a minimal, single-record example of an
AugmentedManifestFile
:{"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",
"label-metadata": {"class-name": "cat"
}For more information on
AugmentedManifestFile
, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
-
S3Uri
— required — (String
)The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
-
OutputDataConfig
— (map
)Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
KmsKeyId
— (String
)The Key Management Service encryption key ID.
S3OutputPath
— required — (String
)The Amazon S3 output path. Must be 512 characters or less.
AutoMLProblemTypeConfig
— (map
)Defines the configuration settings of one of the supported problem types.
ImageClassificationJobConfig
— (map
)Settings used to configure an AutoML job V2 for the image classification problem type.
CompletionCriteria
— (map
)How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
TextClassificationJobConfig
— (map
)Settings used to configure an AutoML job V2 for the text classification problem type.
CompletionCriteria
— (map
)How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ContentColumn
— required — (String
)The name of the column used to provide the sentences to be classified. It should not be the same as the target column.
TargetLabelColumn
— required — (String
)The name of the column used to provide the class labels. It should not be same as the content column.
TimeSeriesForecastingJobConfig
— (map
)Settings used to configure an AutoML job V2 for the time-series forecasting problem type.
FeatureSpecificationS3Uri
— (String
)A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in
TimeSeriesConfig
. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared inTimeSeriesConfig
. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared inTimeSeriesConfig
.You can input
FeatureAttributeNames
(optional) in JSON format as shown below:{ "FeatureAttributeNames":["col1", "col2", ...] }
.You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Autopilot supports the following data types:
numeric
,categorical
,text
, anddatetime
.Note: These column keys must not include any column set inTimeSeriesConfig
.CompletionCriteria
— (map
)How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ForecastFrequency
— required — (String
)The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example,
1D
indicates every day and15min
indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of1H
instead of60min
.The valid values for each frequency are the following:
-
Minute - 1-59
-
Hour - 1-23
-
Day - 1-6
-
Week - 1-4
-
Month - 1-11
-
Year - 1
-
ForecastHorizon
— required — (Integer
)The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
ForecastQuantiles
— (Array<String>
)The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from
0.01
(p1) to0.99
(p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. WhenForecastQuantiles
is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.Transformations
— (map
)The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
Filling
— (map<map<String>>
)A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.
The supported filling methods and their corresponding options are:
-
frontfill
:none
(Supported only for target column) -
middlefill
:zero
,value
,median
,mean
,min
,max
-
backfill
:zero
,value
,median
,mean
,min
,max
-
futurefill
:zero
,value
,median
,mean
,min
,max
To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example
"backfill" : "value"
), and define the filling value in an additional parameter prefixed with "_value". For example, to setbackfill
to a value of2
, you must include two parameters:"backfill": "value"
and"backfill_value":"2"
.-
Aggregation
— (map<String>
)A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.
The supported aggregation methods are
sum
(default),avg
,first
,min
,max
.Note: Aggregation is only supported for the target column.
TimeSeriesConfig
— required — (map
)The collection of components that defines the time-series.
TargetAttributeName
— required — (String
)The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.
TimestampAttributeName
— required — (String
)The name of the column indicating a point in time at which the target value of a given item is recorded.
ItemIdentifierAttributeName
— required — (String
)The name of the column that represents the set of item identifiers for which you want to predict the target value.
GroupingAttributeNames
— (Array<String>
)A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.
HolidayConfig
— (Array<map>
)The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.
CountryCode
— (String
)The country code for the holiday calendar.
For the list of public holiday calendars supported by AutoML job V2, see Country Codes. Use the country code corresponding to the country of your choice.
CandidateGenerationConfig
— (map
)Stores the configuration information for how model candidates are generated using an AutoML job V2.
AlgorithmsConfig
— (Array<map>
)Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig
stores the customized selection of algorithms to train on your data.-
For the tabular problem type
TabularJobConfig
, the list of available algorithms to choose from depends on the training mode set inAutoMLJobConfig.Mode
.-
AlgorithmsConfig
should not be set when the training modeAutoMLJobConfig.Mode
is set toAUTO
. -
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for the given training mode. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
, choose your algorithms from the list provided in AlgorithmConfig.For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
-
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting.
-
AutoMLAlgorithms
— required — (Array<String>
)The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
-
For the tabular problem type
TabularJobConfig
:Note: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING
orHYPERPARAMETER_TUNING
). Choose a minimum of 1 algorithm.-
In
ENSEMBLING
mode:-
"catboost"
-
"extra-trees"
-
"fastai"
-
"lightgbm"
-
"linear-learner"
-
"nn-torch"
-
"randomforest"
-
"xgboost"
-
-
In
HYPERPARAMETER_TUNING
mode:-
"linear-learner"
-
"mlp"
-
"xgboost"
-
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
:-
Choose your algorithms from this list.
-
"cnn-qr"
-
"deepar"
-
"prophet"
-
"arima"
-
"npts"
-
"ets"
-
-
-
-
TabularJobConfig
— (map
)Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).
CandidateGenerationConfig
— (map
)The configuration information of how model candidates are generated.
AlgorithmsConfig
— (Array<map>
)Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig
stores the customized selection of algorithms to train on your data.-
For the tabular problem type
TabularJobConfig
, the list of available algorithms to choose from depends on the training mode set inAutoMLJobConfig.Mode
.-
AlgorithmsConfig
should not be set when the training modeAutoMLJobConfig.Mode
is set toAUTO
. -
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for the given training mode. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
, choose your algorithms from the list provided in AlgorithmConfig.For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
-
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting.
-
AutoMLAlgorithms
— required — (Array<String>
)The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
-
For the tabular problem type
TabularJobConfig
:Note: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING
orHYPERPARAMETER_TUNING
). Choose a minimum of 1 algorithm.-
In
ENSEMBLING
mode:-
"catboost"
-
"extra-trees"
-
"fastai"
-
"lightgbm"
-
"linear-learner"
-
"nn-torch"
-
"randomforest"
-
"xgboost"
-
-
In
HYPERPARAMETER_TUNING
mode:-
"linear-learner"
-
"mlp"
-
"xgboost"
-
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
:-
Choose your algorithms from this list.
-
"cnn-qr"
-
"deepar"
-
"prophet"
-
"arima"
-
"npts"
-
"ets"
-
-
-
-
CompletionCriteria
— (map
)How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
FeatureSpecificationS3Uri
— (String
)A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input
FeatureAttributeNames
(optional) in JSON format as shown below:{ "FeatureAttributeNames":["col1", "col2", ...] }
.You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Note: These column keys may not include the target column.In ensembling mode, Autopilot only supports the following data types:
numeric
,categorical
,text
, anddatetime
. In HPO mode, Autopilot can supportnumeric
,categorical
,text
,datetime
, andsequence
.If only
FeatureDataTypes
is provided, the column keys (col1
,col2
,..) should be a subset of the column names in the input data.If both
FeatureDataTypes
andFeatureAttributeNames
are provided, then the column keys should be a subset of the column names provided inFeatureAttributeNames
.The key name
FeatureAttributeNames
is fixed. The values listed in["col1", "col2", ...]
are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.Mode
— (String
)The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting
AUTO
. InAUTO
mode, Autopilot choosesENSEMBLING
for datasets smaller than 100 MB, andHYPERPARAMETER_TUNING
for larger ones.The
ENSEMBLING
mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported byENSEMBLING
mode.The
Possible values include:HYPERPARAMETER_TUNING
(HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported byHYPERPARAMETER_TUNING
mode."AUTO"
"ENSEMBLING"
"HYPERPARAMETER_TUNING"
GenerateCandidateDefinitionsOnly
— (Boolean
)Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
ProblemType
— (String
)The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.
Note: You must either specify the type of supervised learning problem inPossible values include:ProblemType
and provide the AutoMLJobObjective metric, or none at all."BinaryClassification"
"MulticlassClassification"
"Regression"
TargetAttributeName
— required — (String
)The name of the target variable in supervised learning, usually represented by 'y'.
SampleWeightAttributeName
— (String
)If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
TextGenerationJobConfig
— (map
)Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.
Note: The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.CompletionCriteria
— (map
)How long a fine-tuning job is allowed to run. For
TextGenerationJobConfig
problem types, theMaxRuntimePerTrainingJobInSeconds
attribute ofAutoMLJobCompletionCriteria
defaults to 72h (259200s).MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
BaseModelName
— (String
)The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no
BaseModelName
is provided, the default model used is Falcon7BInstruct.TextGenerationHyperParameters
— (map<String>
)The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.
-
"epochCount"
: The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10". -
"batchSize"
: The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64". -
"learningRate"
: The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1". -
"learningRateWarmupSteps"
: The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".
Here is an example where all four hyperparameters are configured.
{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }
-
ModelAccessConfig
— (map
)The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
.-
If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.
-
If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.
AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
-
RoleArn
— (String
)The ARN of the role that is used to access the data.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
SecurityConfig
— (map
)The security configuration for traffic encryption or Amazon VPC settings.
VolumeKmsKeyId
— (String
)The key used to encrypt stored data.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to use traffic encryption between the container layers.
VpcConfig
— (map
)The VPC configuration.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
AutoMLJobObjective
— (map
)Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.
Note:- For tabular problem types: You must either provide both the
AutoMLJobObjective
and indicate the type of supervised learning problem inAutoMLProblemTypeConfig
(TabularJobConfig.ProblemType
), or none at all. - For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the
AutoMLJobObjective
field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
MetricName
— required — (String
)The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
-
For tabular problem types:
-
List of available metrics:
-
Regression:
MAE
,MSE
,R2
,RMSE
-
Binary classification:
Accuracy
,AUC
,BalancedAccuracy
,F1
,Precision
,Recall
-
Multiclass classification:
Accuracy
,BalancedAccuracy
,F1macro
,PrecisionMacro
,RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression.
-
-
Default objective metrics:
-
Regression:
MSE
. -
Binary classification:
F1
. -
Multiclass classification:
Accuracy
.
-
-
-
For image or text classification problem types:
-
List of available metrics:
Accuracy
For a description of each metric, see Autopilot metrics for text and image classification.
-
Default objective metrics:
Accuracy
-
-
For time-series forecasting problem types:
-
List of available metrics:
RMSE
,wQL
,Average wQL
,MASE
,MAPE
,WAPE
For a description of each metric, see Autopilot metrics for time-series forecasting.
-
Default objective metrics:
AverageWeightedQuantileLoss
-
-
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the
AutoMLJobObjective
field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
-
- For tabular problem types: You must either provide both the
ModelDeployConfig
— (map
)Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
AutoGenerateEndpointName
— (Boolean
)Set to
True
to automatically generate an endpoint name for a one-click Autopilot model deployment; set toFalse
otherwise. The default value isFalse
.Note: If you setAutoGenerateEndpointName
toTrue
, do not specify theEndpointName
; otherwise a 400 error is thrown.EndpointName
— (String
)Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note: Specify theEndpointName
if and only if you setAutoGenerateEndpointName
toFalse
; otherwise a 400 error is thrown.
DataSplitConfig
— (map
)This structure specifies how to split the data into train and validation datasets.
The validation and training datasets must contain the same headers. For jobs created by calling
CreateAutoMLJob
, the validation dataset must be less than 2 GB in size.Note: This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.ValidationFraction
— (Float
)The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
AutoMLComputeConfig
— (map
)Specifies the compute configuration for the AutoML job V2.
EmrServerlessComputeConfig
— (map
)The configuration for using EMR Serverless to run the AutoML job V2.
To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an
EmrServerlessComputeConfig
object, which includes anExecutionRoleARN
attribute, to theAutoMLComputeConfig
of the AutoML job V2 input request.By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you.
EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.
ExecutionRoleARN
— required — (String
)The ARN of the IAM role granting the AutoML job V2 the necessary permissions access policies to list, connect to, or manage EMR Serverless jobs. For detailed information about the required permissions of this role, see "How to configure AutoML to initiate a remote job on EMR Serverless for large datasets" in Create a regression or classification job for tabular data using the AutoML API or Create an AutoML job for time-series forecasting using the API.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AutoMLJobArn
— (String
)The unique ARN assigned to the AutoMLJob when it is created.
-
(AWS.Response)
—
Returns:
createCluster(params = {}, callback) ⇒ AWS.Request
Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.
Service Reference:
Examples:
Calling the createCluster operation
var params = { ClusterName: 'STRING_VALUE', /* required */ InstanceGroups: [ /* required */ { ExecutionRole: 'STRING_VALUE', /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceGroupName: 'STRING_VALUE', /* required */ InstanceType: ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.c5n.large | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge, /* required */ LifeCycleConfig: { /* required */ OnCreate: 'STRING_VALUE', /* required */ SourceS3Uri: 'STRING_VALUE' /* required */ }, InstanceStorageConfigs: [ { EbsVolumeConfig: { VolumeSizeInGB: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], ThreadsPerCore: 'NUMBER_VALUE' }, /* more items */ ], Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }; sagemaker.createCluster(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ClusterName
— (String
)The name for the new SageMaker HyperPod cluster.
InstanceGroups
— (Array<map>
)The instance groups to be created in the SageMaker HyperPod cluster.
InstanceCount
— required — (Integer
)Specifies the number of instances to add to the instance group of a SageMaker HyperPod cluster.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.c5n.large"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
LifeCycleConfig
— required — (map
)Specifies the LifeCycle configuration for the instance group.
SourceS3Uri
— required — (String
)An Amazon S3 bucket path where your lifecycle scripts are stored.
Make sure that the S3 bucket path starts with
s3://sagemaker-
. The IAM role for SageMaker HyperPod has the managedAmazonSageMakerClusterInstanceRolePolicy
attached, which allows access to S3 buckets with the specific prefixsagemaker-
.OnCreate
— required — (String
)The file name of the entrypoint script of lifecycle scripts under
SourceS3Uri
. This entrypoint script runs during cluster creation.
ExecutionRole
— required — (String
)Specifies an IAM execution role to be assumed by the instance group.
ThreadsPerCore
— (Integer
)Specifies the value for Threads per core. For instance types that support multithreading, you can specify
1
for disabling multithreading and2
for enabling multithreading. For instance types that doesn't support multithreading, specify1
. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.InstanceStorageConfigs
— (Array<map>
)Specifies the additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.
EbsVolumeConfig
— (map
)Defines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to
/opt/sagemaker
.VolumeSizeInGB
— required — (Integer
)The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to
/opt/sagemaker
.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
Tags
— (Array<map>
)Custom tags for managing the SageMaker HyperPod cluster as an Amazon Web Services resource. You can add tags to your cluster in the same way you add them in other Amazon Web Services services that support tagging. To learn more about tagging Amazon Web Services resources in general, see Tagging Amazon Web Services Resources User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ClusterArn
— (String
)The Amazon Resource Name (ARN) of the cluster.
-
(AWS.Response)
—
Returns:
createCodeRepository(params = {}, callback) ⇒ AWS.Request
Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
Service Reference:
Examples:
Calling the createCodeRepository operation
var params = { CodeRepositoryName: 'STRING_VALUE', /* required */ GitConfig: { /* required */ RepositoryUrl: 'STRING_VALUE', /* required */ Branch: 'STRING_VALUE', SecretArn: 'STRING_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createCodeRepository(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CodeRepositoryName
— (String
)The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
GitConfig
— (map
)Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
RepositoryUrl
— required — (String
)The URL where the Git repository is located.
Branch
— (String
)The default branch for the Git repository.
SecretArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of
AWSCURRENT
and must be in the following format:{"username": UserName, "password": Password}
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CodeRepositoryArn
— (String
)The Amazon Resource Name (ARN) of the new repository.
-
(AWS.Response)
—
Returns:
createCompilationJob(params = {}, callback) ⇒ AWS.Request
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
-
A name for the compilation job
-
Information about the input model artifacts
-
The output location for the compiled model and the device (target) that the model runs on
-
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
You can also provide a
Tag
to track the model compilation job's resource use and costs. The response body contains theCompilationJobArn
for the compiled job.To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
Service Reference:
Examples:
Calling the createCompilationJob operation
var params = { CompilationJobName: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputLocation: 'STRING_VALUE', /* required */ CompilerOptions: 'STRING_VALUE', KmsKeyId: 'STRING_VALUE', TargetDevice: lambda | ml_m4 | ml_m5 | ml_m6g | ml_c4 | ml_c5 | ml_c6g | ml_p2 | ml_p3 | ml_g4dn | ml_inf1 | ml_inf2 | ml_trn1 | ml_eia2 | jetson_tx1 | jetson_tx2 | jetson_nano | jetson_xavier | rasp3b | rasp4b | imx8qm | deeplens | rk3399 | rk3288 | aisage | sbe_c | qcs605 | qcs603 | sitara_am57x | amba_cv2 | amba_cv22 | amba_cv25 | x86_win32 | x86_win64 | coreml | jacinto_tda4vm | imx8mplus, TargetPlatform: { Arch: X86_64 | X86 | ARM64 | ARM_EABI | ARM_EABIHF, /* required */ Os: ANDROID | LINUX, /* required */ Accelerator: INTEL_GRAPHICS | MALI | NVIDIA | NNA } }, RoleArn: 'STRING_VALUE', /* required */ StoppingCondition: { /* required */ MaxPendingTimeInSeconds: 'NUMBER_VALUE', MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, InputConfig: { Framework: TENSORFLOW | KERAS | MXNET | ONNX | PYTORCH | XGBOOST | TFLITE | DARKNET | SKLEARN, /* required */ S3Uri: 'STRING_VALUE', /* required */ DataInputConfig: 'STRING_VALUE', FrameworkVersion: 'STRING_VALUE' }, ModelPackageVersionArn: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }; sagemaker.createCompilationJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CompilationJobName
— (String
)A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
During model compilation, Amazon SageMaker needs your permission to:
-
Read input data from an S3 bucket
-
Write model artifacts to an S3 bucket
-
Write logs to Amazon CloudWatch Logs
-
Publish metrics to Amazon CloudWatch
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the
iam:PassRole
permission. For more information, see Amazon SageMaker Roles.-
ModelPackageVersionArn
— (String
)The Amazon Resource Name (ARN) of a versioned model package. Provide either a
ModelPackageVersionArn
or anInputConfig
object in the request syntax. The presence of both objects in theCreateCompilationJob
request will return an exception.InputConfig
— (map
)Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
S3Uri
— required — (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
DataInputConfig
— (String
)Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are
Framework
specific.-
TensorFlow
: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"input":[1,1024,1024,3]}
-
If using the CLI,
{\"input\":[1,1024,1024,3]}
-
-
Examples for two inputs:
-
If using the console,
{"data1": [1,28,28,1], "data2":[1,28,28,1]}
-
If using the CLI,
{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
-
-
-
KERAS
: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format,DataInputConfig
should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"input_1":[1,3,224,224]}
-
If using the CLI,
{\"input_1\":[1,3,224,224]}
-
-
Examples for two inputs:
-
If using the console,
{"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
-
If using the CLI,
{\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
-
-
-
MXNET/ONNX/DARKNET
: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"data":[1,3,1024,1024]}
-
If using the CLI,
{\"data\":[1,3,1024,1024]}
-
-
Examples for two inputs:
-
If using the console,
{"var1": [1,1,28,28], "var2":[1,1,28,28]}
-
If using the CLI,
{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
-
-
-
PyTorch
: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.-
Examples for one input in dictionary format:
-
If using the console,
{"input0":[1,3,224,224]}
-
If using the CLI,
{\"input0\":[1,3,224,224]}
-
-
Example for one input in list format:
[[1,3,224,224]]
-
Examples for two inputs in dictionary format:
-
If using the console,
{"input0":[1,3,224,224], "input1":[1,3,224,224]}
-
If using the CLI,
{\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
-
-
Example for two inputs in list format:
[[1,3,224,224], [1,3,224,224]]
-
-
XGBOOST
: input data name and shape are not needed.
DataInputConfig
supports the following parameters forCoreML
TargetDevice
(ML Model format):-
shape
: Input shape, for example{"input_1": {"shape": [1,224,224,3]}}
. In addition to static input shapes, CoreML converter supports Flexible input shapes:-
Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example:
{"input_1": {"shape": ["1..10", 224, 224, 3]}}
-
Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example:
{"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
-
-
default_shape
: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example{"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
-
type
: Input type. Allowed values:Image
andTensor
. By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such asbias
andscale
. -
bias
: If the input type is an Image, you need to provide the bias vector. -
scale
: If the input type is an Image, you need to provide a scale factor.
CoreML
ClassifierConfig
parameters can be specified using OutputConfigCompilerOptions
. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:-
Tensor type input:
-
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
-
-
Tensor type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
-
-
Image type input:
-
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
-
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
-
-
Image type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
-
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
-
Depending on the model format,
DataInputConfig
requires the following parameters forml_eia2
OutputConfig:TargetDevice.-
For TensorFlow models saved in the SavedModel format, specify the input names from
signature_def_key
and the input model shapes forDataInputConfig
. Specify thesignature_def_key
inOutputConfig:CompilerOptions
if the model does not use TensorFlow's default signature def key. For example:-
"DataInputConfig": {"inputs": [1, 224, 224, 3]}
-
"CompilerOptions": {"signature_def_key": "serving_custom"}
-
-
For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
DataInputConfig
and the output tensor names foroutput_names
inOutputConfig:CompilerOptions
. For example:-
"DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
-
"CompilerOptions": {"output_names": ["output_tensor:0"]}
-
-
Framework
— required — (String
)Identifies the framework in which the model was trained. For example: TENSORFLOW.
Possible values include:"TENSORFLOW"
"KERAS"
"MXNET"
"ONNX"
"PYTORCH"
"XGBOOST"
"TFLITE"
"DARKNET"
"SKLEARN"
FrameworkVersion
— (String
)Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.
For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.
OutputConfig
— (map
)Provides information about the output location for the compiled model and the target device the model runs on.
S3OutputLocation
— required — (String
)Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.TargetDevice
— (String
)Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of
TargetPlatform
.Note: CurrentlyPossible values include:ml_trn1
is available only in US East (N. Virginia) Region, andml_inf2
is available only in US East (Ohio) Region."lambda"
"ml_m4"
"ml_m5"
"ml_m6g"
"ml_c4"
"ml_c5"
"ml_c6g"
"ml_p2"
"ml_p3"
"ml_g4dn"
"ml_inf1"
"ml_inf2"
"ml_trn1"
"ml_eia2"
"jetson_tx1"
"jetson_tx2"
"jetson_nano"
"jetson_xavier"
"rasp3b"
"rasp4b"
"imx8qm"
"deeplens"
"rk3399"
"rk3288"
"aisage"
"sbe_c"
"qcs605"
"qcs603"
"sitara_am57x"
"amba_cv2"
"amba_cv22"
"amba_cv25"
"x86_win32"
"x86_win64"
"coreml"
"jacinto_tda4vm"
"imx8mplus"
TargetPlatform
— (map
)Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of
TargetDevice
.The following examples show how to configure the
TargetPlatform
andCompilerOptions
JSON strings for popular target platforms:-
Raspberry Pi 3 Model B+
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
"CompilerOptions": {'mattr': ['+neon']}
-
Jetson TX2
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
-
EC2 m5.2xlarge instance OS
"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'mcpu': 'skylake-avx512'}
-
RK3399
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
-
ARMv7 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
"CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
-
ARMv8 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
"CompilerOptions": {'ANDROID_PLATFORM': 29}
Os
— required — (String
)Specifies a target platform OS.
-
LINUX
: Linux-based operating systems. -
ANDROID
: Android operating systems. Android API level can be specified using theANDROID_PLATFORM
compiler option. For example,"CompilerOptions": {'ANDROID_PLATFORM': 28}
"ANDROID"
"LINUX"
-
Arch
— required — (String
)Specifies a target platform architecture.
-
X86_64
: 64-bit version of the x86 instruction set. -
X86
: 32-bit version of the x86 instruction set. -
ARM64
: ARMv8 64-bit CPU. -
ARM_EABIHF
: ARMv7 32-bit, Hard Float. -
ARM_EABI
: ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
"X86_64"
"X86"
"ARM64"
"ARM_EABI"
"ARM_EABIHF"
-
Accelerator
— (String
)Specifies a target platform accelerator (optional).
-
NVIDIA
: Nvidia graphics processing unit. It also requiresgpu-code
,trt-ver
,cuda-ver
compiler options -
MALI
: ARM Mali graphics processor -
INTEL_GRAPHICS
: Integrated Intel graphics
"INTEL_GRAPHICS"
"MALI"
"NVIDIA"
"NNA"
-
-
CompilerOptions
— (String
)Specifies additional parameters for compiler options in JSON format. The compiler options are
TargetPlatform
specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specifyCompilerOptions.
-
DTYPE
: Specifies the data type for the input. When compiling forml_*
(except forml_inf
) instances using PyTorch framework, provide the data type (dtype) of the model's input."float32"
is used if"DTYPE"
is not specified. Options for data type are:-
float32: Use either
"float"
or"float32"
. -
int64: Use either
"int64"
or"long"
.
For example,
{"dtype" : "float32"}
. -
-
CPU
: Compilation for CPU supports the following compiler options.-
mcpu
: CPU micro-architecture. For example,{'mcpu': 'skylake-avx512'}
-
mattr
: CPU flags. For example,{'mattr': ['+neon', '+vfpv4']}
-
-
ARM
: Details of ARM CPU compilations.-
NEON
: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.For example, add
{'mattr': ['+neon']}
to the compiler options if compiling for ARM 32-bit platform with the NEON support.
-
-
NVIDIA
: Compilation for NVIDIA GPU supports the following compiler options.-
gpu_code
: Specifies the targeted architecture. -
trt-ver
: Specifies the TensorRT versions in x.y.z. format. -
cuda-ver
: Specifies the CUDA version in x.y format.
For example,
{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
-
-
ANDROID
: Compilation for the Android OS supports the following compiler options:-
ANDROID_PLATFORM
: Specifies the Android API levels. Available levels range from 21 to 29. For example,{'ANDROID_PLATFORM': 28}
. -
mattr
: Add{'mattr': ['+neon']}
to compiler options if compiling for ARM 32-bit platform with NEON support.
-
-
INFERENTIA
: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example,"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""
.For information about supported compiler options, see Neuron Compiler CLI Reference Guide.
-
CoreML
: Compilation for the CoreML OutputConfigTargetDevice
supports the following compiler options:-
class_labels
: Specifies the classification labels file name inside input tar.gz file. For example,{"class_labels": "imagenet_labels_1000.txt"}
. Labels inside the txt file should be separated by newlines.
-
-
EIA
: Compilation for the Elastic Inference Accelerator supports the following compiler options:-
precision_mode
: Specifies the precision of compiled artifacts. Supported values are"FP16"
and"FP32"
. Default is"FP32"
. -
signature_def_key
: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key. -
output_names
: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either:signature_def_key
oroutput_names
.
For example:
{"precision_mode": "FP32", "output_names": ["output:0"]}
-
-
KmsKeyId
— (String
)The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
VpcConfig
— (map
)A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs. IDs have the form of
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.
StoppingCondition
— (map
)Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CompilationJobArn
— (String
)If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:
-
CompilationJobArn
: The Amazon Resource Name (ARN) of the compiled job.
-
-
(AWS.Response)
—
Returns:
createContext(params = {}, callback) ⇒ AWS.Request
Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.
Service Reference:
Examples:
Calling the createContext operation
var params = { ContextName: 'STRING_VALUE', /* required */ ContextType: 'STRING_VALUE', /* required */ Source: { /* required */ SourceUri: 'STRING_VALUE', /* required */ SourceId: 'STRING_VALUE', SourceType: 'STRING_VALUE' }, Description: 'STRING_VALUE', Properties: { '<StringParameterValue>': 'STRING_VALUE', /* '<StringParameterValue>': ... */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createContext(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ContextName
— (String
)The name of the context. Must be unique to your account in an Amazon Web Services Region.
Source
— (map
)The source type, ID, and URI.
SourceUri
— required — (String
)The URI of the source.
SourceType
— (String
)The type of the source.
SourceId
— (String
)The ID of the source.
ContextType
— (String
)The context type.
Description
— (String
)The description of the context.
Properties
— (map<String>
)A list of properties to add to the context.
Tags
— (Array<map>
)A list of tags to apply to the context.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ContextArn
— (String
)The Amazon Resource Name (ARN) of the context.
-
(AWS.Response)
—
Returns:
createDataQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
Service Reference:
Examples:
Calling the createDataQualityJobDefinition operation
var params = { DataQualityAppSpecification: { /* required */ ImageUri: 'STRING_VALUE', /* required */ ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ }, PostAnalyticsProcessorSourceUri: 'STRING_VALUE', RecordPreprocessorSourceUri: 'STRING_VALUE' }, DataQualityJobInput: { /* required */ BatchTransformInput: { DataCapturedDestinationS3Uri: 'STRING_VALUE', /* required */ DatasetFormat: { /* required */ Csv: { Header: true || false }, Json: { Line: true || false }, Parquet: { } }, LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' }, EndpointInput: { EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' } }, DataQualityJobOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, JobDefinitionName: 'STRING_VALUE', /* required */ JobResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, RoleArn: 'STRING_VALUE', /* required */ DataQualityBaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' }, StatisticsResource: { S3Uri: 'STRING_VALUE' } }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createDataQualityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name for the monitoring job definition.
DataQualityBaselineConfig
— (map
)Configures the constraints and baselines for the monitoring job.
BaseliningJobName
— (String
)The name of the job that performs baselining for the data quality monitoring job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
StatisticsResource
— (map
)The statistics resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the statistics resource.
DataQualityAppSpecification
— (map
)Specifies the container that runs the monitoring job.
ImageUri
— required — (String
)The container image that the data quality monitoring job runs.
ContainerEntrypoint
— (Array<String>
)The entrypoint for a container used to run a monitoring job.
ContainerArguments
— (Array<String>
)The arguments to send to the container that the monitoring job runs.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
Environment
— (map<String>
)Sets the environment variables in the container that the monitoring job runs.
DataQualityJobInput
— (map
)A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.
EndpointInput
— (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
DataQualityJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
NetworkConfig
— (map
)Specifies networking configuration for the monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Tags
— (Array<map>
)(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the job definition.
-
(AWS.Response)
—
Returns:
createDeviceFleet(params = {}, callback) ⇒ AWS.Request
Creates a device fleet.
Service Reference:
Examples:
Calling the createDeviceFleet operation
var params = { DeviceFleetName: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputLocation: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', PresetDeploymentConfig: 'STRING_VALUE', PresetDeploymentType: GreengrassV2Component }, Description: 'STRING_VALUE', EnableIotRoleAlias: true || false, RoleArn: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createDeviceFleet(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DeviceFleetName
— (String
)The name of the fleet that the device belongs to.
RoleArn
— (String
)The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
Description
— (String
)A description of the fleet.
OutputConfig
— (map
)The output configuration for storing sample data collected by the fleet.
S3OutputLocation
— required — (String
)The Amazon Simple Storage (S3) bucker URI.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
PresetDeploymentType
— (String
)The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
Possible values include:"GreengrassV2Component"
PresetDeploymentConfig
— (String
)The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
-
ComponentName
(optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job. -
ComponentDescription
(optional) - Description of the component. -
ComponentVersion
(optional) - The version of the component.Note: Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification. -
PlatformOS
(optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux. -
PlatformArchitecture
(optional) - The processor architecture for the platform.Supported architectures Windows include: Windows32_x86, Windows64_x64.
Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
-
Tags
— (Array<map>
)Creates tags for the specified fleet.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
EnableIotRoleAlias
— (Boolean
)Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-
{DeviceFleetName}
".For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
createDomain(params = {}, callback) ⇒ AWS.Request
Creates a
Domain
. A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.EFS storage
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the
AppNetworkAccessType
parameter.AppNetworkAccessType
corresponds to the network access type that you choose when you onboard to the domain. The following options are available:-
PublicInternetOnly
- Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value. -
VpcOnly
- All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.When internet access is disabled, you won't be able to run a Amazon SageMaker Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.
For more information, see Connect Amazon SageMaker Studio Notebooks to Resources in a VPC.
Service Reference:
Examples:
Calling the createDomain operation
var params = { AuthMode: SSO | IAM, /* required */ DefaultUserSettings: { /* required */ AutoMountHomeEFS: Enabled | Disabled | DefaultAsDomain, CanvasAppSettings: { DirectDeploySettings: { Status: ENABLED | DISABLED }, EmrServerlessSettings: { ExecutionRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, GenerativeAiSettings: { AmazonBedrockRoleArn: 'STRING_VALUE' }, IdentityProviderOAuthSettings: [ { DataSourceName: SalesforceGenie | Snowflake, SecretArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, /* more items */ ], KendraSettings: { Status: ENABLED | DISABLED }, ModelRegisterSettings: { CrossAccountModelRegisterRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, TimeSeriesForecastingSettings: { AmazonForecastRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, WorkspaceSettings: { S3ArtifactPath: 'STRING_VALUE', S3KmsKeyId: 'STRING_VALUE' } }, CodeEditorAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE', LifecycleManagement: ENABLED | DISABLED, MaxIdleTimeoutInMinutes: 'NUMBER_VALUE', MinIdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, CustomFileSystemConfigs: [ { EFSFileSystemConfig: { FileSystemId: 'STRING_VALUE', /* required */ FileSystemPath: 'STRING_VALUE' } }, /* more items */ ], CustomPosixUserConfig: { Gid: 'NUMBER_VALUE', /* required */ Uid: 'NUMBER_VALUE' /* required */ }, DefaultLandingUri: 'STRING_VALUE', ExecutionRole: 'STRING_VALUE', JupyterLabAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE', LifecycleManagement: ENABLED | DISABLED, MaxIdleTimeoutInMinutes: 'NUMBER_VALUE', MinIdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, EmrSettings: { AssumableRoleArns: [ 'STRING_VALUE', /* more items */ ], ExecutionRoleArns: [ 'STRING_VALUE', /* more items */ ] }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, JupyterServerAppSettings: { CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, KernelGatewayAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, RSessionAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } }, RStudioServerProAppSettings: { AccessStatus: ENABLED | DISABLED, UserGroup: R_STUDIO_ADMIN | R_STUDIO_USER }, SecurityGroups: [ 'STRING_VALUE', /* more items */ ], SharingSettings: { NotebookOutputOption: Allowed | Disabled, S3KmsKeyId: 'STRING_VALUE', S3OutputPath: 'STRING_VALUE' }, SpaceStorageSettings: { DefaultEbsStorageSettings: { DefaultEbsVolumeSizeInGb: 'NUMBER_VALUE', /* required */ MaximumEbsVolumeSizeInGb: 'NUMBER_VALUE' /* required */ } }, StudioWebPortal: ENABLED | DISABLED, StudioWebPortalSettings: { HiddenAppTypes: [ JupyterServer | KernelGateway | DetailedProfiler | TensorBoard | CodeEditor | JupyterLab | RStudioServerPro | RSessionGateway | Canvas, /* more items */ ], HiddenMlTools: [ DataWrangler | FeatureStore | EmrClusters | AutoMl | Experiments | Training | ModelEvaluation | Pipelines | Models | JumpStart | InferenceRecommender | Endpoints | Projects | InferenceOptimization, /* more items */ ] }, TensorBoardAppSettings: { DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } } }, DomainName: 'STRING_VALUE', /* required */ SubnetIds: [ /* required */ 'STRING_VALUE', /* more items */ ], VpcId: 'STRING_VALUE', /* required */ AppNetworkAccessType: PublicInternetOnly | VpcOnly, AppSecurityGroupManagement: Service | Customer, DefaultSpaceSettings: { CustomFileSystemConfigs: [ { EFSFileSystemConfig: { FileSystemId: 'STRING_VALUE', /* required */ FileSystemPath: 'STRING_VALUE' } }, /* more items */ ], CustomPosixUserConfig: { Gid: 'NUMBER_VALUE', /* required */ Uid: 'NUMBER_VALUE' /* required */ }, ExecutionRole: 'STRING_VALUE', JupyterLabAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE', LifecycleManagement: ENABLED | DISABLED, MaxIdleTimeoutInMinutes: 'NUMBER_VALUE', MinIdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, EmrSettings: { AssumableRoleArns: [ 'STRING_VALUE', /* more items */ ], ExecutionRoleArns: [ 'STRING_VALUE', /* more items */ ] }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, JupyterServerAppSettings: { CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, KernelGatewayAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, SecurityGroups: [ 'STRING_VALUE', /* more items */ ], SpaceStorageSettings: { DefaultEbsStorageSettings: { DefaultEbsVolumeSizeInGb: 'NUMBER_VALUE', /* required */ MaximumEbsVolumeSizeInGb: 'NUMBER_VALUE' /* required */ } } }, DomainSettings: { AmazonQSettings: { QProfileArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, DockerSettings: { EnableDockerAccess: ENABLED | DISABLED, VpcOnlyTrustedAccounts: [ 'STRING_VALUE', /* more items */ ] }, ExecutionRoleIdentityConfig: USER_PROFILE_NAME | DISABLED, RStudioServerProDomainSettings: { DomainExecutionRoleArn: 'STRING_VALUE', /* required */ DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, RStudioConnectUrl: 'STRING_VALUE', RStudioPackageManagerUrl: 'STRING_VALUE' }, SecurityGroupIds: [ 'STRING_VALUE', /* more items */ ] }, HomeEfsFileSystemKmsKeyId: 'STRING_VALUE', KmsKeyId: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createDomain(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainName
— (String
)A name for the domain.
AuthMode
— (String
)The mode of authentication that members use to access the domain.
Possible values include:"SSO"
"IAM"
DefaultUserSettings
— (map
)The default settings to use to create a user profile when
UserSettings
isn't specified in the call to theCreateUserProfile
API.SecurityGroups
is aggregated when specified in both calls. For all other settings inUserSettings
, the values specified inCreateUserProfile
take precedence over those specified inCreateDomain
.ExecutionRole
— (String
)The execution role for the user.
SecurityGroups
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the
CreateDomain.AppNetworkAccessType
parameter is set toPublicInternetOnly
.Required when the
CreateDomain.AppNetworkAccessType
parameter is set toVpcOnly
, unless specified as part of theDefaultUserSettings
for the domain.Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SharingSettings
— (map
)Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption
— (String
)Whether to include the notebook cell output when sharing the notebook. The default is
Possible values include:Disabled
."Allowed"
"Disabled"
S3OutputPath
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon S3 bucket used to store the shared notebook snapshots.S3KmsKeyId
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings
— (map
)The Jupyter server's app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The kernel gateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
TensorBoardAppSettings
— (map
)The TensorBoard app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings
— (map
)A collection of settings that configure user interaction with the
RStudioServerPro
app.AccessStatus
— (String
)Indicates whether the current user has access to the
Possible values include:RStudioServerPro
app."ENABLED"
"DISABLED"
UserGroup
— (String
)The level of permissions that the user has within the
Possible values include:RStudioServerPro
app. This value defaults toUser
. TheAdmin
value allows the user access to the RStudio Administrative Dashboard."R_STUDIO_ADMIN"
"R_STUDIO_USER"
RSessionAppSettings
— (map
)A collection of settings that configure the
RSessionGateway
app.DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a RSession app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
CanvasAppSettings
— (map
)The Canvas app settings.
TimeSeriesForecastingSettings
— (map
)Time series forecast settings for the SageMaker Canvas application.
Status
— (String
)Describes whether time series forecasting is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
AmazonForecastRoleArn
— (String
)The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the
UserProfile
that launches the Canvas application. If an execution role is not specified in theUserProfile
, Canvas uses the execution role specified in the Domain that owns theUserProfile
. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached andforecast.amazonaws.com
added in the trust relationship as a service principal.
ModelRegisterSettings
— (map
)The model registry settings for the SageMaker Canvas application.
Status
— (String
)Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CrossAccountModelRegisterRoleArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings
— (map
)The workspace settings for the SageMaker Canvas application.
S3ArtifactPath
— (String
)The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings
— (Array<map>
)The settings for connecting to an external data source with OAuth.
DataSourceName
— (String
)The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Possible values include:"SalesforceGenie"
"Snowflake"
Status
— (String
)Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
SecretArn
— (String
)The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings
— (map
)The model deployment settings for the SageMaker Canvas application.
Status
— (String
)Describes whether model deployment permissions are enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
KendraSettings
— (map
)The settings for document querying.
Status
— (String
)Describes whether the document querying feature is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
GenerativeAiSettings
— (map
)The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn
— (String
)The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes
bedrock.amazonaws.com
as a service principal.
EmrServerlessSettings
— (map
)The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status
— (String
)Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CodeEditorAppSettings
— (map
)The Code Editor application settings.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a Code Editor app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a JupyterLab app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set
LifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings
— (map
)The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
ExecutionRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
SpaceStorageSettings
— (map
)The storage settings for a space.
DefaultEbsStorageSettings
— (map
)The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb
— required — (Integer
)The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb
— required — (Integer
)The maximum size of the EBS storage volume for a space.
DefaultLandingUri
— (String
)The default experience that the user is directed to when accessing the domain. The supported values are:
-
studio::
: Indicates that Studio is the default experience. This value can only be passed ifStudioWebPortal
is set toENABLED
. -
app:JupyterServer:
: Indicates that Studio Classic is the default experience.
-
StudioWebPortal
— (String
)Whether the user can access Studio. If this value is set to
Possible values include:DISABLED
, the user cannot access Studio, even if that is the default experience for the domain."ENABLED"
"DISABLED"
CustomPosixUserConfig
— (map
)Details about the POSIX identity that is used for file system operations.
Uid
— required — (Integer
)The POSIX user ID.
Gid
— required — (Integer
)The POSIX group ID.
CustomFileSystemConfigs
— (Array<map>
)The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystemConfig
— (map
)The settings for a custom Amazon EFS file system.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
FileSystemPath
— (String
)The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings
— (map
)Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools
— (Array<String>
)The machine learning tools that are hidden from the Studio left navigation pane.
HiddenAppTypes
— (Array<String>
)The Applications supported in Studio that are hidden from the Studio left navigation pane.
AutoMountHomeEFS
— (String
)Indicates whether auto-mounting of an EFS volume is supported for the user profile. The
Possible values include:DefaultAsDomain
value is only supported for user profiles. Do not use theDefaultAsDomain
value when setting this parameter for a domain."Enabled"
"Disabled"
"DefaultAsDomain"
DomainSettings
— (map
)A collection of
Domain
settings.SecurityGroupIds
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud that the
Domain
uses for communication between Domain-level apps and user apps.RStudioServerProDomainSettings
— (map
)A collection of settings that configure the
RStudioServerPro
Domain-level app.DomainExecutionRoleArn
— required — (String
)The ARN of the execution role for the
RStudioServerPro
Domain-level app.RStudioConnectUrl
— (String
)A URL pointing to an RStudio Connect server.
RStudioPackageManagerUrl
— (String
)A URL pointing to an RStudio Package Manager server.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
ExecutionRoleIdentityConfig
— (String
)The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key.
Possible values include:"USER_PROFILE_NAME"
"DISABLED"
DockerSettings
— (map
)A collection of settings that configure the domain's Docker interaction.
EnableDockerAccess
— (String
)Indicates whether the domain can access Docker.
Possible values include:"ENABLED"
"DISABLED"
VpcOnlyTrustedAccounts
— (Array<String>
)The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
AmazonQSettings
— (map
)A collection of settings that configure the Amazon Q experience within the domain. The
AuthMode
that you use to create the domain must beSSO
.Status
— (String
)Whether Amazon Q has been enabled within the domain.
Possible values include:"ENABLED"
"DISABLED"
QProfileArn
— (String
)The ARN of the Amazon Q profile used within the domain.
SubnetIds
— (Array<String>
)The VPC subnets that the domain uses for communication.
VpcId
— (String
)The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Tags
— (Array<map>
)Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the
Search
API.Tags that you specify for the Domain are also added to all Apps that the Domain launches.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
AppNetworkAccessType
— (String
)Specifies the VPC used for non-EFS traffic. The default value is
PublicInternetOnly
.-
PublicInternetOnly
- Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access -
VpcOnly
- All traffic is through the specified VPC and subnets
"PublicInternetOnly"
"VpcOnly"
-
HomeEfsFileSystemKmsKeyId
— (String
)Use
KmsKeyId
.KmsKeyId
— (String
)SageMaker uses Amazon Web Services KMS to encrypt EFS and EBS volumes attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.
AppSecurityGroupManagement
— (String
)The entity that creates and manages the required security groups for inter-app communication in
Possible values include:VPCOnly
mode. Required whenCreateDomain.AppNetworkAccessType
isVPCOnly
andDomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn
is provided. If setting up the domain for use with RStudio, this value must be set toService
."Service"
"Customer"
DefaultSpaceSettings
— (map
)The default settings used to create a space.
ExecutionRole
— (String
)The ARN of the execution role for the space.
SecurityGroups
— (Array<String>
)The security group IDs for the Amazon VPC that the space uses for communication.
JupyterServerAppSettings
— (map
)The JupyterServer app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The KernelGateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a JupyterLab app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set
LifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings
— (map
)The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
ExecutionRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
SpaceStorageSettings
— (map
)The default storage settings for a space.
DefaultEbsStorageSettings
— (map
)The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb
— required — (Integer
)The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb
— required — (Integer
)The maximum size of the EBS storage volume for a space.
CustomPosixUserConfig
— (map
)Details about the POSIX identity that is used for file system operations.
Uid
— required — (Integer
)The POSIX user ID.
Gid
— required — (Integer
)The POSIX group ID.
CustomFileSystemConfigs
— (Array<map>
)The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystemConfig
— (map
)The settings for a custom Amazon EFS file system.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
FileSystemPath
— (String
)The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DomainArn
— (String
)The Amazon Resource Name (ARN) of the created domain.
Url
— (String
)The URL to the created domain.
-
(AWS.Response)
—
Returns:
createEdgeDeploymentPlan(params = {}, callback) ⇒ AWS.Request
Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
Service Reference:
Examples:
Calling the createEdgeDeploymentPlan operation
var params = { DeviceFleetName: 'STRING_VALUE', /* required */ EdgeDeploymentPlanName: 'STRING_VALUE', /* required */ ModelConfigs: [ /* required */ { EdgePackagingJobName: 'STRING_VALUE', /* required */ ModelHandle: 'STRING_VALUE' /* required */ }, /* more items */ ], Stages: [ { DeviceSelectionConfig: { /* required */ DeviceSubsetType: PERCENTAGE | SELECTION | NAMECONTAINS, /* required */ DeviceNameContains: 'STRING_VALUE', DeviceNames: [ 'STRING_VALUE', /* more items */ ], Percentage: 'NUMBER_VALUE' }, StageName: 'STRING_VALUE', /* required */ DeploymentConfig: { FailureHandlingPolicy: ROLLBACK_ON_FAILURE | DO_NOTHING /* required */ } }, /* more items */ ], Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createEdgeDeploymentPlan(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgeDeploymentPlanName
— (String
)The name of the edge deployment plan.
ModelConfigs
— (Array<map>
)List of models associated with the edge deployment plan.
ModelHandle
— required — (String
)The name the device application uses to reference this model.
EdgePackagingJobName
— required — (String
)The edge packaging job associated with this deployment.
DeviceFleetName
— (String
)The device fleet used for this edge deployment plan.
Stages
— (Array<map>
)List of stages of the edge deployment plan. The number of stages is limited to 10 per deployment.
StageName
— required — (String
)The name of the stage.
DeviceSelectionConfig
— required — (map
)Configuration of the devices in the stage.
DeviceSubsetType
— required — (String
)Type of device subsets to deploy to the current stage.
Possible values include:"PERCENTAGE"
"SELECTION"
"NAMECONTAINS"
Percentage
— (Integer
)Percentage of devices in the fleet to deploy to the current stage.
DeviceNames
— (Array<String>
)List of devices chosen to deploy.
DeviceNameContains
— (String
)A filter to select devices with names containing this name.
DeploymentConfig
— (map
)Configuration of the deployment details.
FailureHandlingPolicy
— required — (String
)Toggle that determines whether to rollback to previous configuration if the current deployment fails. By default this is turned on. You may turn this off if you want to investigate the errors yourself.
Possible values include:"ROLLBACK_ON_FAILURE"
"DO_NOTHING"
Tags
— (Array<map>
)List of tags with which to tag the edge deployment plan.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EdgeDeploymentPlanArn
— (String
)The ARN of the edge deployment plan.
-
(AWS.Response)
—
Returns:
createEdgeDeploymentStage(params = {}, callback) ⇒ AWS.Request
Creates a new stage in an existing edge deployment plan.
Service Reference:
Examples:
Calling the createEdgeDeploymentStage operation
var params = { EdgeDeploymentPlanName: 'STRING_VALUE', /* required */ Stages: [ /* required */ { DeviceSelectionConfig: { /* required */ DeviceSubsetType: PERCENTAGE | SELECTION | NAMECONTAINS, /* required */ DeviceNameContains: 'STRING_VALUE', DeviceNames: [ 'STRING_VALUE', /* more items */ ], Percentage: 'NUMBER_VALUE' }, StageName: 'STRING_VALUE', /* required */ DeploymentConfig: { FailureHandlingPolicy: ROLLBACK_ON_FAILURE | DO_NOTHING /* required */ } }, /* more items */ ] }; sagemaker.createEdgeDeploymentStage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgeDeploymentPlanName
— (String
)The name of the edge deployment plan.
Stages
— (Array<map>
)List of stages to be added to the edge deployment plan.
StageName
— required — (String
)The name of the stage.
DeviceSelectionConfig
— required — (map
)Configuration of the devices in the stage.
DeviceSubsetType
— required — (String
)Type of device subsets to deploy to the current stage.
Possible values include:"PERCENTAGE"
"SELECTION"
"NAMECONTAINS"
Percentage
— (Integer
)Percentage of devices in the fleet to deploy to the current stage.
DeviceNames
— (Array<String>
)List of devices chosen to deploy.
DeviceNameContains
— (String
)A filter to select devices with names containing this name.
DeploymentConfig
— (map
)Configuration of the deployment details.
FailureHandlingPolicy
— required — (String
)Toggle that determines whether to rollback to previous configuration if the current deployment fails. By default this is turned on. You may turn this off if you want to investigate the errors yourself.
Possible values include:"ROLLBACK_ON_FAILURE"
"DO_NOTHING"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
createEdgePackagingJob(params = {}, callback) ⇒ AWS.Request
Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
Service Reference:
Examples:
Calling the createEdgePackagingJob operation
var params = { CompilationJobName: 'STRING_VALUE', /* required */ EdgePackagingJobName: 'STRING_VALUE', /* required */ ModelName: 'STRING_VALUE', /* required */ ModelVersion: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputLocation: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', PresetDeploymentConfig: 'STRING_VALUE', PresetDeploymentType: GreengrassV2Component }, RoleArn: 'STRING_VALUE', /* required */ ResourceKey: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createEdgePackagingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgePackagingJobName
— (String
)The name of the edge packaging job.
CompilationJobName
— (String
)The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.
ModelName
— (String
)The name of the model.
ModelVersion
— (String
)The version of the model.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.
OutputConfig
— (map
)Provides information about the output location for the packaged model.
S3OutputLocation
— required — (String
)The Amazon Simple Storage (S3) bucker URI.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
PresetDeploymentType
— (String
)The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
Possible values include:"GreengrassV2Component"
PresetDeploymentConfig
— (String
)The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
-
ComponentName
(optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job. -
ComponentDescription
(optional) - Description of the component. -
ComponentVersion
(optional) - The version of the component.Note: Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification. -
PlatformOS
(optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux. -
PlatformArchitecture
(optional) - The processor architecture for the platform.Supported architectures Windows include: Windows32_x86, Windows64_x64.
Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
-
ResourceKey
— (String
)The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.
Tags
— (Array<map>
)Creates tags for the packaging job.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
createEndpoint(params = {}, callback) ⇒ AWS.Request
Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API to deploy models using SageMaker hosting services.
Note: You must not delete anEndpointConfig
that is in use by an endpoint that is live or while theUpdateEndpoint
orCreateEndpoint
operations are being performed on the endpoint. To update an endpoint, you must create a newEndpointConfig
.The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
Note: When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supportingEventually Consistent Reads
, the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.When SageMaker receives the request, it sets the endpoint status to
Creating
. After it creates the endpoint, it sets the status toInService
. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
Note: To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.- Option 1: For a full SageMaker access, search and attach the
AmazonSageMakerFullAccess
policy. - Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
"Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
"Resource": [
"arn:aws:sagemaker:region:account-id:endpoint/endpointName"
"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
]
For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.
Service Reference:
Examples:
Calling the createEndpoint operation
var params = { EndpointConfigName: 'STRING_VALUE', /* required */ EndpointName: 'STRING_VALUE', /* required */ DeploymentConfig: { AutoRollbackConfiguration: { Alarms: [ { AlarmName: 'STRING_VALUE' }, /* more items */ ] }, BlueGreenUpdatePolicy: { TrafficRoutingConfiguration: { /* required */ Type: ALL_AT_ONCE | CANARY | LINEAR, /* required */ WaitIntervalInSeconds: 'NUMBER_VALUE', /* required */ CanarySize: { Type: INSTANCE_COUNT | CAPACITY_PERCENT, /* required */ Value: 'NUMBER_VALUE' /* required */ }, LinearStepSize: { Type: INSTANCE_COUNT | CAPACITY_PERCENT, /* required */ Value: 'NUMBER_VALUE' /* required */ } }, MaximumExecutionTimeoutInSeconds: 'NUMBER_VALUE', TerminationWaitInSeconds: 'NUMBER_VALUE' }, RollingUpdatePolicy: { MaximumBatchSize: { /* required */ Type: INSTANCE_COUNT | CAPACITY_PERCENT, /* required */ Value: 'NUMBER_VALUE' /* required */ }, WaitIntervalInSeconds: 'NUMBER_VALUE', /* required */ MaximumExecutionTimeoutInSeconds: 'NUMBER_VALUE', RollbackMaximumBatchSize: { Type: INSTANCE_COUNT | CAPACITY_PERCENT, /* required */ Value: 'NUMBER_VALUE' /* required */ } } }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createEndpoint(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (String
)The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in
CreateEndpoint
, but the case is preserved and must be matched in InvokeEndpoint.EndpointConfigName
— (String
)The name of an endpoint configuration. For more information, see CreateEndpointConfig.
DeploymentConfig
— (map
)The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.
BlueGreenUpdatePolicy
— (map
)Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
TrafficRoutingConfiguration
— required — (map
)Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.
Type
— required — (String
)Traffic routing strategy type.
-
ALL_AT_ONCE
: Endpoint traffic shifts to the new fleet in a single step. -
CANARY
: Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic. -
LINEAR
: Endpoint traffic shifts to the new fleet in n steps of a configurable size.
"ALL_AT_ONCE"
"CANARY"
"LINEAR"
-
WaitIntervalInSeconds
— required — (Integer
)The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.
CanarySize
— (map
)Batch size for the first step to turn on traffic on the new endpoint fleet.
Value
must be less than or equal to 50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
LinearStepSize
— (map
)Batch size for each step to turn on traffic on the new endpoint fleet.
Value
must be 10-50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
TerminationWaitInSeconds
— (Integer
)Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.
MaximumExecutionTimeoutInSeconds
— (Integer
)Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in
TerminationWaitInSeconds
andWaitIntervalInSeconds
.
RollingUpdatePolicy
— (map
)Specifies a rolling deployment strategy for updating a SageMaker endpoint.
MaximumBatchSize
— required — (map
)Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.
Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
WaitIntervalInSeconds
— required — (Integer
)The length of the baking period, during which SageMaker monitors alarms for each batch on the new fleet.
MaximumExecutionTimeoutInSeconds
— (Integer
)The time limit for the total deployment. Exceeding this limit causes a timeout.
RollbackMaximumBatchSize
— (map
)Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.
Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
AutoRollbackConfiguration
— (map
)Automatic rollback configuration for handling endpoint deployment failures and recovery.
Alarms
— (Array<map>
)List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.
AlarmName
— (String
)The name of a CloudWatch alarm in your account.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointArn
— (String
)The Amazon Resource Name (ARN) of the endpoint.
-
(AWS.Response)
—
Returns:
createEndpointConfig(params = {}, callback) ⇒ AWS.Request
Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the
CreateModel
API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API.Note: Use this API if you want to use SageMaker hosting services to deploy models into production.In the request, you define a
ProductionVariant
, for each model that you want to deploy. EachProductionVariant
parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.If you are hosting multiple models, you also assign a
VariantWeight
to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.Note: When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supportingEventually Consistent Reads
, the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.Service Reference:
Examples:
Calling the createEndpointConfig operation
var params = { EndpointConfigName: 'STRING_VALUE', /* required */ ProductionVariants: [ /* required */ { VariantName: 'STRING_VALUE', /* required */ AcceleratorType: ml.eia1.medium | ml.eia1.large | ml.eia1.xlarge | ml.eia2.medium | ml.eia2.large | ml.eia2.xlarge, ContainerStartupHealthCheckTimeoutInSeconds: 'NUMBER_VALUE', CoreDumpConfig: { DestinationS3Uri: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, EnableSSMAccess: true || false, InferenceAmiVersion: al2-ami-sagemaker-inference-gpu-2, InitialInstanceCount: 'NUMBER_VALUE', InitialVariantWeight: 'NUMBER_VALUE', InstanceType: ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.12xlarge | ml.m5d.24xlarge | ml.c4.large | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.large | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.12xlarge | ml.r5.24xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.12xlarge | ml.r5d.24xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.dl1.24xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.p4d.24xlarge | ml.c7g.large | ml.c7g.xlarge | ml.c7g.2xlarge | ml.c7g.4xlarge | ml.c7g.8xlarge | ml.c7g.12xlarge | ml.c7g.16xlarge | ml.m6g.large | ml.m6g.xlarge | ml.m6g.2xlarge | ml.m6g.4xlarge | ml.m6g.8xlarge | ml.m6g.12xlarge | ml.m6g.16xlarge | ml.m6gd.large | ml.m6gd.xlarge | ml.m6gd.2xlarge | ml.m6gd.4xlarge | ml.m6gd.8xlarge | ml.m6gd.12xlarge | ml.m6gd.16xlarge | ml.c6g.large | ml.c6g.xlarge | ml.c6g.2xlarge | ml.c6g.4xlarge | ml.c6g.8xlarge | ml.c6g.12xlarge | ml.c6g.16xlarge | ml.c6gd.large | ml.c6gd.xlarge | ml.c6gd.2xlarge | ml.c6gd.4xlarge | ml.c6gd.8xlarge | ml.c6gd.12xlarge | ml.c6gd.16xlarge | ml.c6gn.large | ml.c6gn.xlarge | ml.c6gn.2xlarge | ml.c6gn.4xlarge | ml.c6gn.8xlarge | ml.c6gn.12xlarge | ml.c6gn.16xlarge | ml.r6g.large | ml.r6g.xlarge | ml.r6g.2xlarge | ml.r6g.4xlarge | ml.r6g.8xlarge | ml.r6g.12xlarge | ml.r6g.16xlarge | ml.r6gd.large | ml.r6gd.xlarge | ml.r6gd.2xlarge | ml.r6gd.4xlarge | ml.r6gd.8xlarge | ml.r6gd.12xlarge | ml.r6gd.16xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.inf2.xlarge | ml.inf2.8xlarge | ml.inf2.24xlarge | ml.inf2.48xlarge | ml.p5.48xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge, ManagedInstanceScaling: { MaxInstanceCount: 'NUMBER_VALUE', MinInstanceCount: 'NUMBER_VALUE', Status: ENABLED | DISABLED }, ModelDataDownloadTimeoutInSeconds: 'NUMBER_VALUE', ModelName: 'STRING_VALUE', RoutingConfig: { RoutingStrategy: LEAST_OUTSTANDING_REQUESTS | RANDOM /* required */ }, ServerlessConfig: { MaxConcurrency: 'NUMBER_VALUE', /* required */ MemorySizeInMB: 'NUMBER_VALUE', /* required */ ProvisionedConcurrency: 'NUMBER_VALUE' }, VolumeSizeInGB: 'NUMBER_VALUE' }, /* more items */ ], AsyncInferenceConfig: { OutputConfig: { /* required */ KmsKeyId: 'STRING_VALUE', NotificationConfig: { ErrorTopic: 'STRING_VALUE', IncludeInferenceResponseIn: [ SUCCESS_NOTIFICATION_TOPIC | ERROR_NOTIFICATION_TOPIC, /* more items */ ], SuccessTopic: 'STRING_VALUE' }, S3FailurePath: 'STRING_VALUE', S3OutputPath: 'STRING_VALUE' }, ClientConfig: { MaxConcurrentInvocationsPerInstance: 'NUMBER_VALUE' } }, DataCaptureConfig: { CaptureOptions: [ /* required */ { CaptureMode: Input | Output | InputAndOutput /* required */ }, /* more items */ ], DestinationS3Uri: 'STRING_VALUE', /* required */ InitialSamplingPercentage: 'NUMBER_VALUE', /* required */ CaptureContentTypeHeader: { CsvContentTypes: [ 'STRING_VALUE', /* more items */ ], JsonContentTypes: [ 'STRING_VALUE', /* more items */ ] }, EnableCapture: true || false, KmsKeyId: 'STRING_VALUE' }, EnableNetworkIsolation: true || false, ExecutionRoleArn: 'STRING_VALUE', ExplainerConfig: { ClarifyExplainerConfig: { ShapConfig: { /* required */ ShapBaselineConfig: { /* required */ MimeType: 'STRING_VALUE', ShapBaseline: 'STRING_VALUE', ShapBaselineUri: 'STRING_VALUE' }, NumberOfSamples: 'NUMBER_VALUE', Seed: 'NUMBER_VALUE', TextConfig: { Granularity: token | sentence | paragraph, /* required */ Language: af | sq | ar | hy | eu | bn | bg | ca | zh | hr | cs | da | nl | en | et | fi | fr | de | el | gu | he | hi | hu | is | id | ga | it | kn | ky | lv | lt | lb | mk | ml | mr | ne | nb | fa | pl | pt | ro | ru | sa | sr | tn | si | sk | sl | es | sv | tl | ta | tt | te | tr | uk | ur | yo | lij | xx /* required */ }, UseLogit: true || false }, EnableExplanations: 'STRING_VALUE', InferenceConfig: { ContentTemplate: 'STRING_VALUE', FeatureHeaders: [ 'STRING_VALUE', /* more items */ ], FeatureTypes: [ numerical | categorical | text, /* more items */ ], FeaturesAttribute: 'STRING_VALUE', LabelAttribute: 'STRING_VALUE', LabelHeaders: [ 'STRING_VALUE', /* more items */ ], LabelIndex: 'NUMBER_VALUE', MaxPayloadInMB: 'NUMBER_VALUE', MaxRecordCount: 'NUMBER_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityIndex: 'NUMBER_VALUE' } } }, KmsKeyId: 'STRING_VALUE', ShadowProductionVariants: [ { VariantName: 'STRING_VALUE', /* required */ AcceleratorType: ml.eia1.medium | ml.eia1.large | ml.eia1.xlarge | ml.eia2.medium | ml.eia2.large | ml.eia2.xlarge, ContainerStartupHealthCheckTimeoutInSeconds: 'NUMBER_VALUE', CoreDumpConfig: { DestinationS3Uri: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, EnableSSMAccess: true || false, InferenceAmiVersion: al2-ami-sagemaker-inference-gpu-2, InitialInstanceCount: 'NUMBER_VALUE', InitialVariantWeight: 'NUMBER_VALUE', InstanceType: ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.12xlarge | ml.m5d.24xlarge | ml.c4.large | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.large | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.12xlarge | ml.r5.24xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.12xlarge | ml.r5d.24xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.dl1.24xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.p4d.24xlarge | ml.c7g.large | ml.c7g.xlarge | ml.c7g.2xlarge | ml.c7g.4xlarge | ml.c7g.8xlarge | ml.c7g.12xlarge | ml.c7g.16xlarge | ml.m6g.large | ml.m6g.xlarge | ml.m6g.2xlarge | ml.m6g.4xlarge | ml.m6g.8xlarge | ml.m6g.12xlarge | ml.m6g.16xlarge | ml.m6gd.large | ml.m6gd.xlarge | ml.m6gd.2xlarge | ml.m6gd.4xlarge | ml.m6gd.8xlarge | ml.m6gd.12xlarge | ml.m6gd.16xlarge | ml.c6g.large | ml.c6g.xlarge | ml.c6g.2xlarge | ml.c6g.4xlarge | ml.c6g.8xlarge | ml.c6g.12xlarge | ml.c6g.16xlarge | ml.c6gd.large | ml.c6gd.xlarge | ml.c6gd.2xlarge | ml.c6gd.4xlarge | ml.c6gd.8xlarge | ml.c6gd.12xlarge | ml.c6gd.16xlarge | ml.c6gn.large | ml.c6gn.xlarge | ml.c6gn.2xlarge | ml.c6gn.4xlarge | ml.c6gn.8xlarge | ml.c6gn.12xlarge | ml.c6gn.16xlarge | ml.r6g.large | ml.r6g.xlarge | ml.r6g.2xlarge | ml.r6g.4xlarge | ml.r6g.8xlarge | ml.r6g.12xlarge | ml.r6g.16xlarge | ml.r6gd.large | ml.r6gd.xlarge | ml.r6gd.2xlarge | ml.r6gd.4xlarge | ml.r6gd.8xlarge | ml.r6gd.12xlarge | ml.r6gd.16xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.inf2.xlarge | ml.inf2.8xlarge | ml.inf2.24xlarge | ml.inf2.48xlarge | ml.p5.48xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge, ManagedInstanceScaling: { MaxInstanceCount: 'NUMBER_VALUE', MinInstanceCount: 'NUMBER_VALUE', Status: ENABLED | DISABLED }, ModelDataDownloadTimeoutInSeconds: 'NUMBER_VALUE', ModelName: 'STRING_VALUE', RoutingConfig: { RoutingStrategy: LEAST_OUTSTANDING_REQUESTS | RANDOM /* required */ }, ServerlessConfig: { MaxConcurrency: 'NUMBER_VALUE', /* required */ MemorySizeInMB: 'NUMBER_VALUE', /* required */ ProvisionedConcurrency: 'NUMBER_VALUE' }, VolumeSizeInGB: 'NUMBER_VALUE' }, /* more items */ ], Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }; sagemaker.createEndpointConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointConfigName
— (String
)The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
ProductionVariants
— (Array<map>
)An array of
ProductionVariant
objects, one for each model that you want to host at this endpoint.VariantName
— required — (String
)The name of the production variant.
ModelName
— (String
)The name of the model that you want to host. This is the name that you specified when creating the model.
InitialInstanceCount
— (Integer
)Number of instances to launch initially.
InstanceType
— (String
)The ML compute instance type.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
InitialVariantWeight
— (Float
)Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the
VariantWeight
to the sum of allVariantWeight
values across all ProductionVariants. If unspecified, it defaults to 1.0.AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
CoreDumpConfig
— (map
)Specifies configuration for a core dump from the model container when the process crashes.
DestinationS3Uri
— required — (String
)The Amazon S3 bucket to send the core dump to.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys forOutputDataConfig
. If you use a bucket policy with ans3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint
andUpdateEndpoint
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
ServerlessConfig
— (map
)The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.
ModelDataDownloadTimeoutInSeconds
— (Integer
)The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.
ContainerStartupHealthCheckTimeoutInSeconds
— (Integer
)The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
EnableSSMAccess
— (Boolean
)You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling
UpdateEndpoint
.ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
InferenceAmiVersion
— (String
)Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions. Amazon Web Services optimizes these configurations for different machine learning workloads.
By selecting an AMI version, you can ensure that your inference environment is compatible with specific software requirements, such as CUDA driver versions, Linux kernel versions, or Amazon Web Services Neuron driver versions.
The AMI version names, and their configurations, are the following:
- al2-ami-sagemaker-inference-gpu-2
-
-
Accelerator: GPU
-
NVIDIA driver version: 535.54.03
-
CUDA driver version: 12.2
-
Supported instance types: ml.g4dn., ml.g5., ml.g6., ml.p3., ml.p4d., ml.p4de., ml.p5.*
-
"al2-ami-sagemaker-inference-gpu-2"
DataCaptureConfig
— (map
)Configuration to control how SageMaker captures inference data.
EnableCapture
— (Boolean
)Whether data capture should be enabled or disabled (defaults to enabled).
InitialSamplingPercentage
— required — (Integer
)The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.
DestinationS3Uri
— required — (String
)The Amazon S3 location used to capture the data.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker uses to encrypt the captured data at rest using Amazon S3 server-side encryption.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
CaptureOptions
— required — (Array<map>
)Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both
CaptureMode
— required — (String
)Specify the boundary of data to capture.
Possible values include:"Input"
"Output"
"InputAndOutput"
CaptureContentTypeHeader
— (map
)Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.
CsvContentTypes
— (Array<String>
)The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.
JsonContentTypes
— (Array<String>
)The list of all content type headers that SageMaker will treat as JSON and capture accordingly.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint
,UpdateEndpoint
requests. For more information, refer to the Amazon Web Services Key Management Service section Using Key Policies in Amazon Web Services KMSNote: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aKmsKeyId
when using an instance type with local storage. If any of the models that you specify in theProductionVariants
parameter use nitro-based instances with local storage, do not specify a value for theKmsKeyId
parameter. If you specify a value forKmsKeyId
when using any nitro-based instances with local storage, the call toCreateEndpointConfig
fails. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.-
AsyncInferenceConfig
— (map
)Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync.
ClientConfig
— (map
)Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
MaxConcurrentInvocationsPerInstance
— (Integer
)The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
OutputConfig
— required — (map
)Specifies the configuration for asynchronous inference invocation outputs.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
S3OutputPath
— (String
)The Amazon S3 location to upload inference responses to.
NotificationConfig
— (map
)Specifies the configuration for notifications of inference results for asynchronous inference.
SuccessTopic
— (String
)Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
ErrorTopic
— (String
)Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
IncludeInferenceResponseIn
— (Array<String>
)The Amazon SNS topics where you want the inference response to be included.
Note: The inference response is included only if the response size is less than or equal to 128 KB.
S3FailurePath
— (String
)The Amazon S3 location to upload failure inference responses to.
ExplainerConfig
— (map
)A member of
CreateEndpointConfig
that enables explainers.ClarifyExplainerConfig
— (map
)A member of
ExplainerConfig
that contains configuration parameters for the SageMaker Clarify explainer.EnableExplanations
— (String
)A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See
EnableExplanations
for additional information.InferenceConfig
— (map
)The inference configuration parameter for the model container.
FeaturesAttribute
— (String
)Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if
FeaturesAttribute
is the JMESPath expression'myfeatures'
, it extracts a list of features[1,2,3]
from request data'{"myfeatures":[1,2,3]}'
.ContentTemplate
— (String
)A template string used to format a JSON record into an acceptable model container input. For example, a
ContentTemplate
string'{"myfeatures":$features}'
will format a list of features[1,2,3]
into the record string'{"myfeatures":[1,2,3]}'
. Required only when the model container input is in JSON Lines format.MaxRecordCount
— (Integer
)The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If
MaxRecordCount
is1
, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.MaxPayloadInMB
— (Integer
)The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
6
MB.ProbabilityIndex
— (Integer
)A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.
Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability:
'1,0.6'
, setProbabilityIndex
to1
to select the probability value0.6
.Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, setProbabilityIndex
to1
to select the probability values[0.1,0.6,0.3]
.LabelIndex
— (Integer
)A zero-based index used to extract a label header or list of label headers from model container output in CSV format.
Example for a multiclass model: If the model container output consists of label headers followed by probabilities:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, setLabelIndex
to0
to select the label headers['cat','dog','fish']
.ProbabilityAttribute
— (String
)A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.
Example: If the model container output of a single request is
'{"predicted_label":1,"probability":0.6}'
, then setProbabilityAttribute
to'probability'
.LabelAttribute
— (String
)A JMESPath expression used to locate the list of label headers in the model container output.
Example: If the model container output of a batch request is
'{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}'
, then setLabelAttribute
to'labels'
to extract the list of label headers["cat","dog","fish"]
LabelHeaders
— (Array<String>
)For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the
InvokeEndpoint
API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.FeatureHeaders
— (Array<String>
)The names of the features. If provided, these are included in the endpoint response payload to help readability of the
InvokeEndpoint
output. See the Response section under Invoke the endpoint in the Developer Guide for more information.FeatureTypes
— (Array<String>
)A list of data types of the features (optional). Applicable only to NLP explainability. If provided,
FeatureTypes
must have at least one'text'
string (for example,['text']
). IfFeatureTypes
is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.
ShapConfig
— required — (map
)The configuration for SHAP analysis.
ShapBaselineConfig
— required — (map
)The configuration for the SHAP baseline of the Kernal SHAP algorithm.
MimeType
— (String
)The MIME type of the baseline data. Choose from
'text/csv'
or'application/jsonlines'
. Defaults to'text/csv'
.ShapBaseline
— (String
)The inline SHAP baseline data in string format.
ShapBaseline
can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by theGranularity
of theTextConfig
parameter. The size limit forShapBasline
is 4 KB. Use theShapBaselineUri
parameter if you want to provide more than 4 KB of baseline data.ShapBaselineUri
— (String
)The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the
ShapBaselineUri
should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.
NumberOfSamples
— (Integer
)The number of samples to be used for analysis by the Kernal SHAP algorithm.
Note: The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.UseLogit
— (Boolean
)A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.
Seed
— (Integer
)The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.
TextConfig
— (map
)A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.
Language
— required — (String
)Specifies the language of the text features in ISO 639-1 or ISO 639-3 code of a supported language.
Note: For a mix of multiple languages, use codePossible values include:'xx'
."af"
"sq"
"ar"
"hy"
"eu"
"bn"
"bg"
"ca"
"zh"
"hr"
"cs"
"da"
"nl"
"en"
"et"
"fi"
"fr"
"de"
"el"
"gu"
"he"
"hi"
"hu"
"is"
"id"
"ga"
"it"
"kn"
"ky"
"lv"
"lt"
"lb"
"mk"
"ml"
"mr"
"ne"
"nb"
"fa"
"pl"
"pt"
"ro"
"ru"
"sa"
"sr"
"tn"
"si"
"sk"
"sl"
"es"
"sv"
"tl"
"ta"
"tt"
"te"
"tr"
"uk"
"ur"
"yo"
"lij"
"xx"
Granularity
— required — (String
)The unit of granularity for the analysis of text features. For example, if the unit is
Possible values include:'token'
, then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature."token"
"sentence"
"paragraph"
ShadowProductionVariants
— (Array<map>
)An array of
ProductionVariant
objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified onProductionVariants
. If you use this field, you can only specify one variant forProductionVariants
and one variant forShadowProductionVariants
.VariantName
— required — (String
)The name of the production variant.
ModelName
— (String
)The name of the model that you want to host. This is the name that you specified when creating the model.
InitialInstanceCount
— (Integer
)Number of instances to launch initially.
InstanceType
— (String
)The ML compute instance type.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
InitialVariantWeight
— (Float
)Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the
VariantWeight
to the sum of allVariantWeight
values across all ProductionVariants. If unspecified, it defaults to 1.0.AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
CoreDumpConfig
— (map
)Specifies configuration for a core dump from the model container when the process crashes.
DestinationS3Uri
— required — (String
)The Amazon S3 bucket to send the core dump to.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys forOutputDataConfig
. If you use a bucket policy with ans3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint
andUpdateEndpoint
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
ServerlessConfig
— (map
)The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.
ModelDataDownloadTimeoutInSeconds
— (Integer
)The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.
ContainerStartupHealthCheckTimeoutInSeconds
— (Integer
)The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
EnableSSMAccess
— (Boolean
)You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling
UpdateEndpoint
.ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
InferenceAmiVersion
— (String
)Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions. Amazon Web Services optimizes these configurations for different machine learning workloads.
By selecting an AMI version, you can ensure that your inference environment is compatible with specific software requirements, such as CUDA driver versions, Linux kernel versions, or Amazon Web Services Neuron driver versions.
The AMI version names, and their configurations, are the following:
- al2-ami-sagemaker-inference-gpu-2
-
-
Accelerator: GPU
-
NVIDIA driver version: 535.54.03
-
CUDA driver version: 12.2
-
Supported instance types: ml.g4dn., ml.g5., ml.g6., ml.p3., ml.p4d., ml.p4de., ml.p5.*
-
"al2-ami-sagemaker-inference-gpu-2"
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform actions on your behalf. For more information, see SageMaker Roles.
Note: To be able to pass this role to Amazon SageMaker, the caller of this action must have theiam:PassRole
permission.VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
EnableNetworkIsolation
— (Boolean
)Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointConfigArn
— (String
)The Amazon Resource Name (ARN) of the endpoint configuration.
-
(AWS.Response)
—
Returns:
createExperiment(params = {}, callback) ⇒ AWS.Request
Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
Note: In the Studio UI, trials are referred to as run groups and trial components are referred to as runs.The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional
Description
parameter. To add a description later, or to change the description, call the UpdateExperiment API.To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
Service Reference:
Examples:
Calling the createExperiment operation
var params = { ExperimentName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', DisplayName: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ExperimentName
— (String
)The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.
DisplayName
— (String
)The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify
DisplayName
, the value inExperimentName
is displayed.Description
— (String
)The description of the experiment.
Tags
— (Array<map>
)A list of tags to associate with the experiment. You can use Search API to search on the tags.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ExperimentArn
— (String
)The Amazon Resource Name (ARN) of the experiment.
-
(AWS.Response)
—
Returns:
createFeatureGroup(params = {}, callback) ⇒ AWS.Request
Create a new
FeatureGroup
. AFeatureGroup
is a group ofFeatures
defined in theFeatureStore
to describe aRecord
.The
FeatureGroup
defines the schema and features contained in theFeatureGroup
. AFeatureGroup
definition is composed of a list ofFeatures
, aRecordIdentifierFeatureName
, anEventTimeFeatureName
and configurations for itsOnlineStore
andOfflineStore
. Check Amazon Web Services service quotas to see theFeatureGroup
s quota for your Amazon Web Services account.Note that it can take approximately 10-15 minutes to provision an
OnlineStore
FeatureGroup
with theInMemory
StorageType
.You must include at least one of
OnlineStoreConfig
andOfflineStoreConfig
to create aFeatureGroup
.Service Reference:
Examples:
Calling the createFeatureGroup operation
var params = { EventTimeFeatureName: 'STRING_VALUE', /* required */ FeatureDefinitions: [ /* required */ { FeatureName: 'STRING_VALUE', /* required */ FeatureType: Integral | Fractional | String, /* required */ CollectionConfig: { VectorConfig: { Dimension: 'NUMBER_VALUE' /* required */ } }, CollectionType: List | Set | Vector }, /* more items */ ], FeatureGroupName: 'STRING_VALUE', /* required */ RecordIdentifierFeatureName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', OfflineStoreConfig: { S3StorageConfig: { /* required */ S3Uri: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', ResolvedOutputS3Uri: 'STRING_VALUE' }, DataCatalogConfig: { Catalog: 'STRING_VALUE', /* required */ Database: 'STRING_VALUE', /* required */ TableName: 'STRING_VALUE' /* required */ }, DisableGlueTableCreation: true || false, TableFormat: Default | Glue | Iceberg }, OnlineStoreConfig: { EnableOnlineStore: true || false, SecurityConfig: { KmsKeyId: 'STRING_VALUE' }, StorageType: Standard | InMemory, TtlDuration: { Unit: Seconds | Minutes | Hours | Days | Weeks, Value: 'NUMBER_VALUE' } }, RoleArn: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], ThroughputConfig: { ThroughputMode: OnDemand | Provisioned, /* required */ ProvisionedReadCapacityUnits: 'NUMBER_VALUE', ProvisionedWriteCapacityUnits: 'NUMBER_VALUE' } }; sagemaker.createFeatureGroup(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FeatureGroupName
— (String
)The name of the
FeatureGroup
. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.The name:
-
Must start with an alphanumeric character.
-
Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.
-
RecordIdentifierFeatureName
— (String
)The name of the
Feature
whose value uniquely identifies aRecord
defined in theFeatureStore
. Only the latest record per identifier value will be stored in theOnlineStore
.RecordIdentifierFeatureName
must be one of feature definitions' names.You use the
RecordIdentifierFeatureName
to access data in aFeatureStore
.This name:
-
Must start with an alphanumeric character.
-
Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.
-
EventTimeFeatureName
— (String
)The name of the feature that stores the
EventTime
of aRecord
in aFeatureGroup
.An
EventTime
is a point in time when a new event occurs that corresponds to the creation or update of aRecord
in aFeatureGroup
. AllRecords
in theFeatureGroup
must have a correspondingEventTime
.An
EventTime
can be aString
orFractional
.-
Fractional
:EventTime
feature values must be a Unix timestamp in seconds. -
String
:EventTime
feature values must be an ISO-8601 string in the format. The following formats are supportedyyyy-MM-dd'T'HH:mm:ssZ
andyyyy-MM-dd'T'HH:mm:ss.SSSZ
whereyyyy
,MM
, anddd
represent the year, month, and day respectively andHH
,mm
,ss
, and if applicable,SSS
represent the hour, month, second and milliseconds respsectively.'T'
andZ
are constants.
-
FeatureDefinitions
— (Array<map>
)A list of
Feature
names and types.Name
andType
is compulsory perFeature
.Valid feature
FeatureType
s areIntegral
,Fractional
andString
.FeatureName
s cannot be any of the following:is_deleted
,write_time
,api_invocation_time
You can create up to 2,500
FeatureDefinition
s perFeatureGroup
.FeatureName
— required — (String
)The name of a feature. The type must be a string.
FeatureName
cannot be any of the following:is_deleted
,write_time
,api_invocation_time
.The name:
-
Must start with an alphanumeric character.
-
Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.
-
FeatureType
— required — (String
)The value type of a feature. Valid values are Integral, Fractional, or String.
Possible values include:"Integral"
"Fractional"
"String"
CollectionType
— (String
)A grouping of elements where each element within the collection must have the same feature type (
String
,Integral
, orFractional
).-
List
: An ordered collection of elements. -
Set
: An unordered collection of unique elements. -
Vector
: A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.
"List"
"Set"
"Vector"
-
CollectionConfig
— (map
)Configuration for your collection.
VectorConfig
— (map
)Configuration for your vector collection type.
-
Dimension
: The number of elements in your vector.
Dimension
— required — (Integer
)The number of elements in your vector.
-
OnlineStoreConfig
— (map
)You can turn the
OnlineStore
on or off by specifyingTrue
for theEnableOnlineStore
flag inOnlineStoreConfig
.You can also include an Amazon Web Services KMS key ID (
KMSKeyId
) for at-rest encryption of theOnlineStore
.The default value is
False
.SecurityConfig
— (map
)Use to specify KMS Key ID (
KMSKeyId
) for at-rest encryption of yourOnlineStore
.KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either user or IAM role) of
CreateFeatureGroup
must have below permissions to theOnlineStore
KmsKeyId
:-
"kms:Encrypt"
-
"kms:Decrypt"
-
"kms:DescribeKey"
-
"kms:CreateGrant"
-
"kms:RetireGrant"
-
"kms:ReEncryptFrom"
-
"kms:ReEncryptTo"
-
"kms:GenerateDataKey"
-
"kms:ListAliases"
-
"kms:ListGrants"
-
"kms:RevokeGrant"
The caller (either user or IAM role) to all DataPlane operations (
PutRecord
,GetRecord
,DeleteRecord
) must have the following permissions to theKmsKeyId
:-
"kms:Decrypt"
-
EnableOnlineStore
— (Boolean
)Turn
OnlineStore
off by specifyingFalse
for theEnableOnlineStore
flag. TurnOnlineStore
on by specifyingTrue
for theEnableOnlineStore
flag.The default value is
False
.TtlDuration
— (map
)Time to live duration, where the record is hard deleted after the expiration time is reached;
ExpiresAt
=EventTime
+TtlDuration
. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.Unit
— (String
)TtlDuration
time unit."Seconds"
"Minutes"
"Hours"
"Days"
"Weeks"
Value
— (Integer
)TtlDuration
time value.
StorageType
— (String
)Option for different tiers of low latency storage for real-time data retrieval.
-
Standard
: A managed low latency data store for feature groups. -
InMemory
: A managed data store for feature groups that supports very low latency retrieval.
"Standard"
"InMemory"
-
OfflineStoreConfig
— (map
)Use this to configure an
OfflineFeatureStore
. This parameter allows you to specify:-
The Amazon Simple Storage Service (Amazon S3) location of an
OfflineStore
. -
A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.
-
An KMS encryption key to encrypt the Amazon S3 location used for
OfflineStore
. If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent. -
Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg.
To learn more about this parameter, see OfflineStoreConfig.
S3StorageConfig
— required — (map
)The Amazon Simple Storage (Amazon S3) location of
OfflineStore
.S3Uri
— required — (String
)The S3 URI, or location in Amazon S3, of
OfflineStore
.S3 URIs have a format similar to the following:
s3://example-bucket/prefix/
.KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the
OfflineStore
S3 location.The IAM
roleARN
that is passed as a parameter toCreateFeatureGroup
must have below permissions to theKmsKeyId
:-
"kms:GenerateDataKey"
-
ResolvedOutputS3Uri
— (String
)The S3 path where offline records are written.
DisableGlueTableCreation
— (Boolean
)Set to
True
to disable the automatic creation of an Amazon Web Services Glue table when configuring anOfflineStore
. If set toFalse
, Feature Store will name theOfflineStore
Glue table following Athena's naming recommendations.The default value is
False
.DataCatalogConfig
— (map
)The meta data of the Glue table that is autogenerated when an
OfflineStore
is created.TableName
— required — (String
)The name of the Glue table.
Catalog
— required — (String
)The name of the Glue table catalog.
Database
— required — (String
)The name of the Glue table database.
TableFormat
— (String
)Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg.
Possible values include:"Default"
"Glue"
"Iceberg"
-
ThroughputConfig
— (map
)Used to set feature group throughput configuration. There are two modes:
ON_DEMAND
andPROVISIONED
. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled.Note:
PROVISIONED
throughput mode is supported only for feature groups that are offline-only, or use theStandard
tier online store.ThroughputMode
— required — (String
)The mode used for your feature group throughput:
Possible values include:ON_DEMAND
orPROVISIONED
."OnDemand"
"Provisioned"
ProvisionedReadCapacityUnits
— (Integer
)For provisioned feature groups with online store enabled, this indicates the read throughput you are billed for and can consume without throttling.
This field is not applicable for on-demand feature groups.
ProvisionedWriteCapacityUnits
— (Integer
)For provisioned feature groups, this indicates the write throughput you are billed for and can consume without throttling.
This field is not applicable for on-demand feature groups.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the
OfflineStore
if anOfflineStoreConfig
is provided.Description
— (String
)A free-form description of a
FeatureGroup
.Tags
— (Array<map>
)Tags used to identify
Features
in eachFeatureGroup
.Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:FeatureGroupArn
— (String
)The Amazon Resource Name (ARN) of the
FeatureGroup
. This is a unique identifier for the feature group.
-
(AWS.Response)
—
Returns:
createFlowDefinition(params = {}, callback) ⇒ AWS.Request
Creates a flow definition.
Service Reference:
Examples:
Calling the createFlowDefinition operation
var params = { FlowDefinitionName: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ HumanLoopActivationConfig: { HumanLoopActivationConditionsConfig: { /* required */ HumanLoopActivationConditions: any /* This value will be JSON encoded on your behalf with JSON.stringify() */ /* required */ } }, HumanLoopConfig: { HumanTaskUiArn: 'STRING_VALUE', /* required */ TaskCount: 'NUMBER_VALUE', /* required */ TaskDescription: 'STRING_VALUE', /* required */ TaskTitle: 'STRING_VALUE', /* required */ WorkteamArn: 'STRING_VALUE', /* required */ PublicWorkforceTaskPrice: { AmountInUsd: { Cents: 'NUMBER_VALUE', Dollars: 'NUMBER_VALUE', TenthFractionsOfACent: 'NUMBER_VALUE' } }, TaskAvailabilityLifetimeInSeconds: 'NUMBER_VALUE', TaskKeywords: [ 'STRING_VALUE', /* more items */ ], TaskTimeLimitInSeconds: 'NUMBER_VALUE' }, HumanLoopRequestSource: { AwsManagedHumanLoopRequestSource: AWS/Rekognition/DetectModerationLabels/Image/V3 | AWS/Textract/AnalyzeDocument/Forms/V1 /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createFlowDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FlowDefinitionName
— (String
)The name of your flow definition.
HumanLoopRequestSource
— (map
)Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
AwsManagedHumanLoopRequestSource
— required — (String
)Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:
Possible values include:"AWS/Rekognition/DetectModerationLabels/Image/V3"
"AWS/Textract/AnalyzeDocument/Forms/V1"
HumanLoopActivationConfig
— (map
)An object containing information about the events that trigger a human workflow.
HumanLoopActivationConditionsConfig
— required — (map
)Container structure for defining under what conditions SageMaker creates a human loop.
HumanLoopActivationConditions
— required — (String
)JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide.
HumanLoopConfig
— (map
)An object containing information about the tasks the human reviewers will perform.
WorkteamArn
— required — (String
)Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces.
HumanTaskUiArn
— required — (String
)The Amazon Resource Name (ARN) of the human task user interface.
You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI.
To learn how to create a custom HTML template, see Create Custom Worker Task Template.
To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates.
TaskTitle
— required — (String
)A title for the human worker task.
TaskDescription
— required — (String
)A description for the human worker task.
TaskCount
— required — (Integer
)The number of distinct workers who will perform the same task on each object. For example, if
TaskCount
is set to3
for an image classification labeling job, three workers will classify each input image. IncreasingTaskCount
can improve label accuracy.TaskAvailabilityLifetimeInSeconds
— (Integer
)The length of time that a task remains available for review by human workers.
TaskTimeLimitInSeconds
— (Integer
)The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).
TaskKeywords
— (Array<String>
)Keywords used to describe the task so that workers can discover the task.
PublicWorkforceTaskPrice
— (map
)Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.
-
0.036
-
0.048
-
0.060
-
0.072
-
0.120
-
0.240
-
0.360
-
0.480
-
0.600
-
0.720
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.
-
0.012
-
0.024
-
0.036
-
0.048
-
0.060
-
0.072
-
0.120
-
0.240
-
0.360
-
0.480
-
0.600
-
0.720
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.
-
2.400
-
2.280
-
2.160
-
2.040
-
1.920
-
1.800
-
1.680
-
1.560
-
1.440
-
1.320
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
AmountInUsd
— (map
)Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
Dollars
— (Integer
)The whole number of dollars in the amount.
Cents
— (Integer
)The fractional portion, in cents, of the amount.
TenthFractionsOfACent
— (Integer
)Fractions of a cent, in tenths.
-
OutputConfig
— (map
)An object containing information about where the human review results will be uploaded.
S3OutputPath
— required — (String
)The Amazon S3 path where the object containing human output will be made available.
To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data.
KmsKeyId
— (String
)The Amazon Key Management Service (KMS) key ID for server-side encryption.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example,
arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298
.Tags
— (Array<map>
)An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:FlowDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the flow definition you create.
-
(AWS.Response)
—
Returns:
createHub(params = {}, callback) ⇒ AWS.Request
Create a hub.
Service Reference:
Examples:
Calling the createHub operation
var params = { HubDescription: 'STRING_VALUE', /* required */ HubName: 'STRING_VALUE', /* required */ HubDisplayName: 'STRING_VALUE', HubSearchKeywords: [ 'STRING_VALUE', /* more items */ ], S3StorageConfig: { S3OutputPath: 'STRING_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createHub(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubName
— (String
)The name of the hub to create.
HubDescription
— (String
)A description of the hub.
HubDisplayName
— (String
)The display name of the hub.
HubSearchKeywords
— (Array<String>
)The searchable keywords for the hub.
S3StorageConfig
— (map
)The Amazon S3 storage configuration for the hub.
S3OutputPath
— (String
)The Amazon S3 bucket prefix for hosting hub content.
Tags
— (Array<map>
)Any tags to associate with the hub.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HubArn
— (String
)The Amazon Resource Name (ARN) of the hub.
-
(AWS.Response)
—
Returns:
createHubContentReference(params = {}, callback) ⇒ AWS.Request
Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
Service Reference:
Examples:
Calling the createHubContentReference operation
var params = { HubName: 'STRING_VALUE', /* required */ SageMakerPublicHubContentArn: 'STRING_VALUE', /* required */ HubContentName: 'STRING_VALUE', MinVersion: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createHubContentReference(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubName
— (String
)The name of the hub to add the hub content reference to.
SageMakerPublicHubContentArn
— (String
)The ARN of the public hub content to reference.
HubContentName
— (String
)The name of the hub content to reference.
MinVersion
— (String
)The minimum version of the hub content to reference.
Tags
— (Array<map>
)Any tags associated with the hub content to reference.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HubArn
— (String
)The ARN of the hub that the hub content reference was added to.
HubContentArn
— (String
)The ARN of the hub content.
-
(AWS.Response)
—
Returns:
createHumanTaskUi(params = {}, callback) ⇒ AWS.Request
Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
Service Reference:
Examples:
Calling the createHumanTaskUi operation
var params = { HumanTaskUiName: 'STRING_VALUE', /* required */ UiTemplate: { /* required */ Content: 'STRING_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createHumanTaskUi(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HumanTaskUiName
— (String
)The name of the user interface you are creating.
UiTemplate
— (map
)The Liquid template for the worker user interface.
Content
— required — (String
)The content of the Liquid template for the worker user interface.
Tags
— (Array<map>
)An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HumanTaskUiArn
— (String
)The Amazon Resource Name (ARN) of the human review workflow user interface you create.
-
(AWS.Response)
—
Returns:
createHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
Service Reference:
Examples:
Calling the createHyperParameterTuningJob operation
var params = { HyperParameterTuningJobConfig: { /* required */ ResourceLimits: { /* required */ MaxParallelTrainingJobs: 'NUMBER_VALUE', /* required */ MaxNumberOfTrainingJobs: 'NUMBER_VALUE', MaxRuntimeInSeconds: 'NUMBER_VALUE' }, Strategy: Bayesian | Random | Hyperband | Grid, /* required */ HyperParameterTuningJobObjective: { MetricName: 'STRING_VALUE', /* required */ Type: Maximize | Minimize /* required */ }, ParameterRanges: { AutoParameters: [ { Name: 'STRING_VALUE', /* required */ ValueHint: 'STRING_VALUE' /* required */ }, /* more items */ ], CategoricalParameterRanges: [ { Name: 'STRING_VALUE', /* required */ Values: [ /* required */ 'STRING_VALUE', /* more items */ ] }, /* more items */ ], ContinuousParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ], IntegerParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ] }, RandomSeed: 'NUMBER_VALUE', StrategyConfig: { HyperbandStrategyConfig: { MaxResource: 'NUMBER_VALUE', MinResource: 'NUMBER_VALUE' } }, TrainingJobEarlyStoppingType: Off | Auto, TuningJobCompletionCriteria: { BestObjectiveNotImproving: { MaxNumberOfTrainingJobsNotImproving: 'NUMBER_VALUE' }, ConvergenceDetected: { CompleteOnConvergence: Disabled | Enabled }, TargetObjectiveMetricValue: 'NUMBER_VALUE' } }, HyperParameterTuningJobName: 'STRING_VALUE', /* required */ Autotune: { Mode: Enabled /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], TrainingJobDefinition: { AlgorithmSpecification: { /* required */ TrainingInputMode: Pipe | File | FastFile, /* required */ AlgorithmName: 'STRING_VALUE', MetricDefinitions: [ { Name: 'STRING_VALUE', /* required */ Regex: 'STRING_VALUE' /* required */ }, /* more items */ ], TrainingImage: 'STRING_VALUE' }, OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ CompressionType: GZIP | NONE, KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ StoppingCondition: { /* required */ MaxPendingTimeInSeconds: 'NUMBER_VALUE', MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, CheckpointConfig: { S3Uri: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE' }, DefinitionName: 'STRING_VALUE', EnableInterContainerTrafficEncryption: true || false, EnableManagedSpotTraining: true || false, EnableNetworkIsolation: true || false, Environment: { '<HyperParameterTrainingJobEnvironmentKey>': 'STRING_VALUE', /* '<HyperParameterTrainingJobEnvironmentKey>': ... */ }, HyperParameterRanges: { AutoParameters: [ { Name: 'STRING_VALUE', /* required */ ValueHint: 'STRING_VALUE' /* required */ }, /* more items */ ], CategoricalParameterRanges: [ { Name: 'STRING_VALUE', /* required */ Values: [ /* required */ 'STRING_VALUE', /* more items */ ] }, /* more items */ ], ContinuousParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ], IntegerParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ] }, HyperParameterTuningResourceConfig: { AllocationStrategy: Prioritized, InstanceConfigs: [ { InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge, /* required */ VolumeSizeInGB: 'NUMBER_VALUE' /* required */ }, /* more items */ ], InstanceCount: 'NUMBER_VALUE', InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge, VolumeKmsKeyId: 'STRING_VALUE', VolumeSizeInGB: 'NUMBER_VALUE' }, InputDataConfig: [ { ChannelName: 'STRING_VALUE', /* required */ DataSource: { /* required */ FileSystemDataSource: { DirectoryPath: 'STRING_VALUE', /* required */ FileSystemAccessMode: rw | ro, /* required */ FileSystemId: 'STRING_VALUE', /* required */ FileSystemType: EFS | FSxLustre /* required */ }, S3DataSource: { S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE', /* required */ AttributeNames: [ 'STRING_VALUE', /* more items */ ], InstanceGroupNames: [ 'STRING_VALUE', /* more items */ ], S3DataDistributionType: FullyReplicated | ShardedByS3Key } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', InputMode: Pipe | File | FastFile, RecordWrapperType: None | RecordIO, ShuffleConfig: { Seed: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], ResourceConfig: { VolumeSizeInGB: 'NUMBER_VALUE', /* required */ InstanceCount: 'NUMBER_VALUE', InstanceGroups: [ { InstanceCount: 'NUMBER_VALUE', /* required */ InstanceGroupName: 'STRING_VALUE', /* required */ InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge /* required */ }, /* more items */ ], InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge, KeepAlivePeriodInSeconds: 'NUMBER_VALUE', VolumeKmsKeyId: 'STRING_VALUE' }, RetryStrategy: { MaximumRetryAttempts: 'NUMBER_VALUE' /* required */ }, StaticHyperParameters: { '<HyperParameterKey>': 'STRING_VALUE', /* '<HyperParameterKey>': ... */ }, TuningObjective: { MetricName: 'STRING_VALUE', /* required */ Type: Maximize | Minimize /* required */ }, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, TrainingJobDefinitions: [ { AlgorithmSpecification: { /* required */ TrainingInputMode: Pipe | File | FastFile, /* required */ AlgorithmName: 'STRING_VALUE', MetricDefinitions: [ { Name: 'STRING_VALUE', /* required */ Regex: 'STRING_VALUE' /* required */ }, /* more items */ ], TrainingImage: 'STRING_VALUE' }, OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ CompressionType: GZIP | NONE, KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ StoppingCondition: { /* required */ MaxPendingTimeInSeconds: 'NUMBER_VALUE', MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, CheckpointConfig: { S3Uri: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE' }, DefinitionName: 'STRING_VALUE', EnableInterContainerTrafficEncryption: true || false, EnableManagedSpotTraining: true || false, EnableNetworkIsolation: true || false, Environment: { '<HyperParameterTrainingJobEnvironmentKey>': 'STRING_VALUE', /* '<HyperParameterTrainingJobEnvironmentKey>': ... */ }, HyperParameterRanges: { AutoParameters: [ { Name: 'STRING_VALUE', /* required */ ValueHint: 'STRING_VALUE' /* required */ }, /* more items */ ], CategoricalParameterRanges: [ { Name: 'STRING_VALUE', /* required */ Values: [ /* required */ 'STRING_VALUE', /* more items */ ] }, /* more items */ ], ContinuousParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ], IntegerParameterRanges: [ { MaxValue: 'STRING_VALUE', /* required */ MinValue: 'STRING_VALUE', /* required */ Name: 'STRING_VALUE', /* required */ ScalingType: Auto | Linear | Logarithmic | ReverseLogarithmic }, /* more items */ ] }, HyperParameterTuningResourceConfig: { AllocationStrategy: Prioritized, InstanceConfigs: [ { InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge, /* required */ VolumeSizeInGB: 'NUMBER_VALUE' /* required */ }, /* more items */ ], InstanceCount: 'NUMBER_VALUE', InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge, VolumeKmsKeyId: 'STRING_VALUE', VolumeSizeInGB: 'NUMBER_VALUE' }, InputDataConfig: [ { ChannelName: 'STRING_VALUE', /* required */ DataSource: { /* required */ FileSystemDataSource: { DirectoryPath: 'STRING_VALUE', /* required */ FileSystemAccessMode: rw | ro, /* required */ FileSystemId: 'STRING_VALUE', /* required */ FileSystemType: EFS | FSxLustre /* required */ }, S3DataSource: { S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE', /* required */ AttributeNames: [ 'STRING_VALUE', /* more items */ ], InstanceGroupNames: [ 'STRING_VALUE', /* more items */ ], S3DataDistributionType: FullyReplicated | ShardedByS3Key } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', InputMode: Pipe | File | FastFile, RecordWrapperType: None | RecordIO, ShuffleConfig: { Seed: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], ResourceConfig: { VolumeSizeInGB: 'NUMBER_VALUE', /* required */ InstanceCount: 'NUMBER_VALUE', InstanceGroups: [ { InstanceCount: 'NUMBER_VALUE', /* required */ InstanceGroupName: 'STRING_VALUE', /* required */ InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge /* required */ }, /* more items */ ], InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge, KeepAlivePeriodInSeconds: 'NUMBER_VALUE', VolumeKmsKeyId: 'STRING_VALUE' }, RetryStrategy: { MaximumRetryAttempts: 'NUMBER_VALUE' /* required */ }, StaticHyperParameters: { '<HyperParameterKey>': 'STRING_VALUE', /* '<HyperParameterKey>': ... */ }, TuningObjective: { MetricName: 'STRING_VALUE', /* required */ Type: Maximize | Minimize /* required */ }, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, /* more items */ ], WarmStartConfig: { ParentHyperParameterTuningJobs: [ /* required */ { HyperParameterTuningJobName: 'STRING_VALUE' }, /* more items */ ], WarmStartType: IdenticalDataAndAlgorithm | TransferLearning /* required */ } }; sagemaker.createHyperParameterTuningJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HyperParameterTuningJobName
— (String
)The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
HyperParameterTuningJobConfig
— (map
)The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.
Strategy
— required — (String
)Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.
Possible values include:"Bayesian"
"Random"
"Hyperband"
"Grid"
StrategyConfig
— (map
)The configuration for the
Hyperband
optimization strategy. This parameter should be provided only ifHyperband
is selected as the strategy forHyperParameterTuningJobConfig
.HyperbandStrategyConfig
— (map
)The configuration for the object that specifies the
Hyperband
strategy. This parameter is only supported for theHyperband
selection forStrategy
within theHyperParameterTuningJobConfig
API.MinResource
— (Integer
)The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for
MinResource
has not been reached, the training job is not stopped byHyperband
.MaxResource
— (Integer
)The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the
MaxResource
value, it is stopped. If a value forMaxResource
is not provided, andHyperband
is selected as the hyperparameter tuning strategy,HyperbandTraining
attempts to inferMaxResource
from the following keys (if present) in StaticsHyperParameters:-
epochs
-
numepochs
-
n-epochs
-
n_epochs
-
num_epochs
If
HyperbandStrategyConfig
is unable to infer a value forMaxResource
, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions. For distributed training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.-
HyperParameterTuningJobObjective
— (map
)The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
ResourceLimits
— required — (map
)The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
MaxNumberOfTrainingJobs
— (Integer
)The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs
— required — (Integer
)The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds
— (Integer
)The maximum time in seconds that a hyperparameter tuning job can run.
ParameterRanges
— (map
)The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
AutoParameters
— (Array<map>
)A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
Name
— required — (String
)The name of the hyperparameter to optimize using Autotune.
ValueHint
— required — (String
)An example value of the hyperparameter to optimize using Autotune.
TrainingJobEarlyStoppingType
— (String
)Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the
Hyperband
strategy has its own advanced internal early stopping mechanism,TrainingJobEarlyStoppingType
must beOFF
to useHyperband
. This parameter can take on one of the following values (the default value isOFF
):- OFF
-
Training jobs launched by the hyperparameter tuning job do not use early stopping.
- AUTO
-
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.
"Off"
"Auto"
TuningJobCompletionCriteria
— (map
)The tuning job's completion criteria.
TargetObjectiveMetricValue
— (Float
)The value of the objective metric.
BestObjectiveNotImproving
— (map
)A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
MaxNumberOfTrainingJobsNotImproving
— (Integer
)The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
ConvergenceDetected
— (map
)A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
CompleteOnConvergence
— (String
)A flag to stop a tuning job once AMT has detected that the job has converged.
Possible values include:"Disabled"
"Enabled"
RandomSeed
— (Integer
)A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
TrainingJobDefinition
— (map
)The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
DefinitionName
— (String
)The job definition name.
TuningObjective
— (map
)Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the
Type
parameter. If you want to define a custom objective metric, see Define metrics and environment variables.Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
HyperParameterRanges
— (map
)Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note: The maximum number of items specified forArray Members
refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
AutoParameters
— (Array<map>
)A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
Name
— required — (String
)The name of the hyperparameter to optimize using Autotune.
ValueHint
— required — (String
)An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters
— (map<String>
)Specifies the values of hyperparameters that do not change for the tuning job.
AlgorithmSpecification
— required — (map
)The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
AlgorithmName
— (String
)The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for
TrainingImage
.MetricDefinitions
— (Array<map>
)An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig
— (Array<map>
)An array of Channel objects that specify the input for the training jobs that the tuning job launches.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
VpcConfig
— (map
)The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
OutputDataConfig
— required — (map
)Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.Note: If you want to use hyperparameter optimization with instance type flexibility, useHyperParameterTuningResourceConfig
instead.InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
HyperParameterTuningResourceConfig
— (map
)The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose
File
forTrainingInputMode
in theAlgorithmSpecification
parameter to additionally store training data in the storage volume (optional).InstanceType
— (String
)The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of compute instances of type
InstanceType
to use. For distributed training, select a value greater than 1.VolumeSizeInGB
— (Integer
)The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for
InstanceConfigs
is also specified.Some instance types have a fixed total local storage size. If you select one of these instances for training,
VolumeSizeInGB
cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.Note: SageMaker supports only the General Purpose SSD (gp2) storage volume type.VolumeKmsKeyId
— (String
)A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a
VolumeKmsKeyId
. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.AllocationStrategy
— (String
)The strategy that determines the order of preference for resources specified in
Possible values include:InstanceConfigs
used in hyperparameter optimization."Prioritized"
InstanceConfigs
— (Array<map>
)A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The
AllocationStrategy
controls the order in which multiple configurations provided inInstanceConfigs
are used.Note: If you only want to use a single instance configuration inside theHyperParameterTuningResourceConfig
API, do not provide a value forInstanceConfigs
. Instead, useInstanceType
,VolumeSizeInGB
andInstanceCount
. If you useInstanceConfigs
, do not provide values forInstanceType
,VolumeSizeInGB
orInstanceCount
.InstanceType
— required — (String
)The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)The number of instances of the type specified by
InstanceType
. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.VolumeSizeInGB
— required — (Integer
)The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition
— required — (map
)Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation
— (Boolean
)Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.EnableManagedSpotTraining
— (Boolean
)A Boolean indicating whether managed spot training is enabled (
True
) or not (False
).CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
Environment
— (map<String>
)An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note: The maximum number of items specified forMap Entries
refers to the maximum number of environment variables for eachTrainingJobDefinition
and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
TrainingJobDefinitions
— (Array<map>
)A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
DefinitionName
— (String
)The job definition name.
TuningObjective
— (map
)Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the
Type
parameter. If you want to define a custom objective metric, see Define metrics and environment variables.Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
HyperParameterRanges
— (map
)Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note: The maximum number of items specified forArray Members
refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
AutoParameters
— (Array<map>
)A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
Name
— required — (String
)The name of the hyperparameter to optimize using Autotune.
ValueHint
— required — (String
)An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters
— (map<String>
)Specifies the values of hyperparameters that do not change for the tuning job.
AlgorithmSpecification
— required — (map
)The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
AlgorithmName
— (String
)The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for
TrainingImage
.MetricDefinitions
— (Array<map>
)An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig
— (Array<map>
)An array of Channel objects that specify the input for the training jobs that the tuning job launches.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
VpcConfig
— (map
)The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
OutputDataConfig
— required — (map
)Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.Note: If you want to use hyperparameter optimization with instance type flexibility, useHyperParameterTuningResourceConfig
instead.InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
HyperParameterTuningResourceConfig
— (map
)The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose
File
forTrainingInputMode
in theAlgorithmSpecification
parameter to additionally store training data in the storage volume (optional).InstanceType
— (String
)The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of compute instances of type
InstanceType
to use. For distributed training, select a value greater than 1.VolumeSizeInGB
— (Integer
)The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for
InstanceConfigs
is also specified.Some instance types have a fixed total local storage size. If you select one of these instances for training,
VolumeSizeInGB
cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.Note: SageMaker supports only the General Purpose SSD (gp2) storage volume type.VolumeKmsKeyId
— (String
)A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a
VolumeKmsKeyId
. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.AllocationStrategy
— (String
)The strategy that determines the order of preference for resources specified in
Possible values include:InstanceConfigs
used in hyperparameter optimization."Prioritized"
InstanceConfigs
— (Array<map>
)A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The
AllocationStrategy
controls the order in which multiple configurations provided inInstanceConfigs
are used.Note: If you only want to use a single instance configuration inside theHyperParameterTuningResourceConfig
API, do not provide a value forInstanceConfigs
. Instead, useInstanceType
,VolumeSizeInGB
andInstanceCount
. If you useInstanceConfigs
, do not provide values forInstanceType
,VolumeSizeInGB
orInstanceCount
.InstanceType
— required — (String
)The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)The number of instances of the type specified by
InstanceType
. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.VolumeSizeInGB
— required — (Integer
)The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition
— required — (map
)Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation
— (Boolean
)Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.EnableManagedSpotTraining
— (Boolean
)A Boolean indicating whether managed spot training is enabled (
True
) or not (False
).CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
Environment
— (map<String>
)An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note: The maximum number of items specified forMap Entries
refers to the maximum number of environment variables for eachTrainingJobDefinition
and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
WarmStartConfig
— (map
)Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify
IDENTICAL_DATA_AND_ALGORITHM
as theWarmStartType
value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.Note: All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.ParentHyperParameterTuningJobs
— required — (Array<map>
)An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
HyperParameterTuningJobName
— (String
)The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
WarmStartType
— required — (String
)Specifies one of the following:
- IDENTICAL_DATA_AND_ALGORITHM
-
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
- TRANSFER_LEARNING
-
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
"IdenticalDataAndAlgorithm"
"TransferLearning"
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Autotune
— (map
)Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
-
ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.
-
ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.
-
TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.
-
RetryStrategy: The number of times to retry a training job.
-
Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.
-
ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.
Mode
— required — (String
)Set
Possible values include:Mode
toEnabled
if you want to use Autotune."Enabled"
-
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HyperParameterTuningJobArn
— (String
)The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a hyperparameter tuning job when you create it.
-
(AWS.Response)
—
Returns:
createImage(params = {}, callback) ⇒ AWS.Request
Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see Bring your own SageMaker image.
Service Reference:
Examples:
Calling the createImage operation
var params = { ImageName: 'STRING_VALUE', /* required */ RoleArn: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', DisplayName: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createImage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
Description
— (String
)The description of the image.
DisplayName
— (String
)The display name of the image. If not provided,
ImageName
is displayed.ImageName
— (String
)The name of the image. Must be unique to your account.
RoleArn
— (String
)The ARN of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
Tags
— (Array<map>
)A list of tags to apply to the image.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ImageArn
— (String
)The ARN of the image.
-
(AWS.Response)
—
Returns:
createImageVersion(params = {}, callback) ⇒ AWS.Request
Creates a version of the SageMaker image specified by
ImageName
. The version represents the Amazon ECR container image specified byBaseImage
.Service Reference:
Examples:
Calling the createImageVersion operation
var params = { BaseImage: 'STRING_VALUE', /* required */ ClientToken: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ Aliases: [ 'STRING_VALUE', /* more items */ ], Horovod: true || false, JobType: TRAINING | INFERENCE | NOTEBOOK_KERNEL, MLFramework: 'STRING_VALUE', Processor: CPU | GPU, ProgrammingLang: 'STRING_VALUE', ReleaseNotes: 'STRING_VALUE', VendorGuidance: NOT_PROVIDED | STABLE | TO_BE_ARCHIVED | ARCHIVED }; sagemaker.createImageVersion(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
BaseImage
— (String
)The registry path of the container image to use as the starting point for this version. The path is an Amazon ECR URI in the following format:
<acct-id>.dkr.ecr.<region>.amazonaws.com/<repo-name[:tag] or [@digest]>
ClientToken
— (String
)A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.
If a token is not provided, the SDK will use a version 4 UUID.ImageName
— (String
)The
ImageName
of theImage
to create a version of.Aliases
— (Array<String>
)A list of aliases created with the image version.
VendorGuidance
— (String
)The stability of the image version, specified by the maintainer.
-
NOT_PROVIDED
: The maintainers did not provide a status for image version stability. -
STABLE
: The image version is stable. -
TO_BE_ARCHIVED
: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months. -
ARCHIVED
: The image version is archived. Archived image versions are not searchable and are no longer actively supported.
"NOT_PROVIDED"
"STABLE"
"TO_BE_ARCHIVED"
"ARCHIVED"
-
JobType
— (String
)Indicates SageMaker job type compatibility.
-
TRAINING
: The image version is compatible with SageMaker training jobs. -
INFERENCE
: The image version is compatible with SageMaker inference jobs. -
NOTEBOOK_KERNEL
: The image version is compatible with SageMaker notebook kernels.
"TRAINING"
"INFERENCE"
"NOTEBOOK_KERNEL"
-
MLFramework
— (String
)The machine learning framework vended in the image version.
ProgrammingLang
— (String
)The supported programming language and its version.
Processor
— (String
)Indicates CPU or GPU compatibility.
-
CPU
: The image version is compatible with CPU. -
GPU
: The image version is compatible with GPU.
"CPU"
"GPU"
-
Horovod
— (Boolean
)Indicates Horovod compatibility.
ReleaseNotes
— (String
)The maintainer description of the image version.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ImageVersionArn
— (String
)The ARN of the image version.
-
(AWS.Response)
—
Returns:
createInferenceComponent(params = {}, callback) ⇒ AWS.Request
Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.
Service Reference:
Examples:
Calling the createInferenceComponent operation
var params = { EndpointName: 'STRING_VALUE', /* required */ InferenceComponentName: 'STRING_VALUE', /* required */ RuntimeConfig: { /* required */ CopyCount: 'NUMBER_VALUE' /* required */ }, Specification: { /* required */ ComputeResourceRequirements: { /* required */ MinMemoryRequiredInMb: 'NUMBER_VALUE', /* required */ MaxMemoryRequiredInMb: 'NUMBER_VALUE', NumberOfAcceleratorDevicesRequired: 'NUMBER_VALUE', NumberOfCpuCoresRequired: 'NUMBER_VALUE' }, Container: { ArtifactUrl: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Image: 'STRING_VALUE' }, ModelName: 'STRING_VALUE', StartupParameters: { ContainerStartupHealthCheckTimeoutInSeconds: 'NUMBER_VALUE', ModelDataDownloadTimeoutInSeconds: 'NUMBER_VALUE' } }, VariantName: 'STRING_VALUE', /* required */ Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createInferenceComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
InferenceComponentName
— (String
)A unique name to assign to the inference component.
EndpointName
— (String
)The name of an existing endpoint where you host the inference component.
VariantName
— (String
)The name of an existing production variant where you host the inference component.
Specification
— (map
)Details about the resources to deploy with this inference component, including the model, container, and compute resources.
ModelName
— (String
)The name of an existing SageMaker model object in your account that you want to deploy with the inference component.
Container
— (map
)Defines a container that provides the runtime environment for a model that you deploy with an inference component.
Image
— (String
)The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.
ArtifactUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the Environment string-to-string map can have length of up to 1024. We support up to 16 entries in the map.
StartupParameters
— (map
)Settings that take effect while the model container starts up.
ModelDataDownloadTimeoutInSeconds
— (Integer
)The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.
ContainerStartupHealthCheckTimeoutInSeconds
— (Integer
)The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
ComputeResourceRequirements
— required — (map
)The compute resources allocated to run the model assigned to the inference component.
NumberOfCpuCoresRequired
— (Float
)The number of CPU cores to allocate to run a model that you assign to an inference component.
NumberOfAcceleratorDevicesRequired
— (Float
)The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia.
MinMemoryRequiredInMb
— required — (Integer
)The minimum MB of memory to allocate to run a model that you assign to an inference component.
MaxMemoryRequiredInMb
— (Integer
)The maximum MB of memory to allocate to run a model that you assign to an inference component.
RuntimeConfig
— (map
)Runtime settings for a model that is deployed with an inference component.
CopyCount
— required — (Integer
)The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
Tags
— (Array<map>
)A list of key-value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceComponentArn
— (String
)The Amazon Resource Name (ARN) of the inference component.
-
(AWS.Response)
—
Returns:
createInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Creates an inference experiment using the configurations specified in the request.
Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.
Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.
While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.
Service Reference:
Examples:
Calling the createInferenceExperiment operation
var params = { EndpointName: 'STRING_VALUE', /* required */ ModelVariants: [ /* required */ { InfrastructureConfig: { /* required */ InfrastructureType: RealTimeInference, /* required */ RealTimeInferenceConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge /* required */ } }, ModelName: 'STRING_VALUE', /* required */ VariantName: 'STRING_VALUE' /* required */ }, /* more items */ ], Name: 'STRING_VALUE', /* required */ RoleArn: 'STRING_VALUE', /* required */ ShadowModeConfig: { /* required */ ShadowModelVariants: [ /* required */ { SamplingPercentage: 'NUMBER_VALUE', /* required */ ShadowModelVariantName: 'STRING_VALUE' /* required */ }, /* more items */ ], SourceModelVariantName: 'STRING_VALUE' /* required */ }, Type: ShadowMode, /* required */ DataStorageConfig: { Destination: 'STRING_VALUE', /* required */ ContentType: { CsvContentTypes: [ 'STRING_VALUE', /* more items */ ], JsonContentTypes: [ 'STRING_VALUE', /* more items */ ] }, KmsKey: 'STRING_VALUE' }, Description: 'STRING_VALUE', KmsKey: 'STRING_VALUE', Schedule: { EndTime: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, StartTime: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789 }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createInferenceExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
Name
— (String
)The name for the inference experiment.
Type
— (String
)The type of the inference experiment that you want to run. The following types of experiments are possible:
-
ShadowMode
: You can use this type to validate a shadow variant. For more information, see Shadow tests.
"ShadowMode"
-
Schedule
— (map
)The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.
StartTime
— (Date
)The timestamp at which the inference experiment started or will start.
EndTime
— (Date
)The timestamp at which the inference experiment ended or will end.
Description
— (String
)A description for the inference experiment.
RoleArn
— (String
)The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
EndpointName
— (String
)The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
ModelVariants
— (Array<map>
)An array of
ModelVariantConfig
objects. There is one for each variant in the inference experiment. EachModelVariantConfig
object in the array describes the infrastructure configuration for the corresponding variant.ModelName
— required — (String
)The name of the Amazon SageMaker Model entity.
VariantName
— required — (String
)The name of the variant.
InfrastructureConfig
— required — (map
)The configuration for the infrastructure that the model will be deployed to.
InfrastructureType
— required — (String
)The inference option to which to deploy your model. Possible values are the following:
-
RealTime
: Deploy to real-time inference.
"RealTimeInference"
-
RealTimeInferenceConfig
— required — (map
)The infrastructure configuration for deploying the model to real-time inference.
InstanceType
— required — (String
)The instance type the model is deployed to.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
InstanceCount
— required — (Integer
)The number of instances of the type specified by
InstanceType
.
DataStorageConfig
— (map
)The Amazon S3 location and configuration for storing inference request and response data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
Destination
— required — (String
)The Amazon S3 bucket where the inference request and response data is stored.
KmsKey
— (String
)The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
ContentType
— (map
)Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker will by default base64 encode when capturing the data.
CsvContentTypes
— (Array<String>
)The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.
JsonContentTypes
— (Array<String>
)The list of all content type headers that SageMaker will treat as JSON and capture accordingly.
ShadowModeConfig
— (map
)The configuration of
ShadowMode
inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.SourceModelVariantName
— required — (String
)The name of the production variant, which takes all the inference requests.
ShadowModelVariants
— required — (Array<map>
)List of shadow variant configurations.
ShadowModelVariantName
— required — (String
)The name of the shadow variant.
SamplingPercentage
— required — (Integer
)The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant.
KmsKey
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The
KmsKey
can be any of the following formats:-
KMS key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
Amazon Resource Name (ARN) of a KMS key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KMS key Alias
"alias/ExampleAlias"
-
Amazon Resource Name (ARN) of a KMS key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys forOutputDataConfig
. If you use a bucket policy with ans3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint
andUpdateEndpoint
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
Tags
— (Array<map>
)Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceExperimentArn
— (String
)The ARN for your inference experiment.
-
(AWS.Response)
—
Returns:
createInferenceRecommendationsJob(params = {}, callback) ⇒ AWS.Request
Starts a recommendation job. You can create either an instance recommendation or load test job.
Service Reference:
Examples:
Calling the createInferenceRecommendationsJob operation
var params = { InputConfig: { /* required */ ContainerConfig: { DataInputConfig: 'STRING_VALUE', Domain: 'STRING_VALUE', Framework: 'STRING_VALUE', FrameworkVersion: 'STRING_VALUE', NearestModelName: 'STRING_VALUE', PayloadConfig: { SamplePayloadUrl: 'STRING_VALUE', SupportedContentTypes: [ 'STRING_VALUE', /* more items */ ] }, SupportedEndpointType: RealTime | Serverless, SupportedInstanceTypes: [ 'STRING_VALUE', /* more items */ ], SupportedResponseMIMETypes: [ 'STRING_VALUE', /* more items */ ], Task: 'STRING_VALUE' }, EndpointConfigurations: [ { EnvironmentParameterRanges: { CategoricalParameterRanges: [ { Name: 'STRING_VALUE', /* required */ Value: [ /* required */ 'STRING_VALUE', /* more items */ ] }, /* more items */ ] }, InferenceSpecificationName: 'STRING_VALUE', InstanceType: ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.12xlarge | ml.m5d.24xlarge | ml.c4.large | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.large | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.12xlarge | ml.r5.24xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.12xlarge | ml.r5d.24xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.dl1.24xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.p4d.24xlarge | ml.c7g.large | ml.c7g.xlarge | ml.c7g.2xlarge | ml.c7g.4xlarge | ml.c7g.8xlarge | ml.c7g.12xlarge | ml.c7g.16xlarge | ml.m6g.large | ml.m6g.xlarge | ml.m6g.2xlarge | ml.m6g.4xlarge | ml.m6g.8xlarge | ml.m6g.12xlarge | ml.m6g.16xlarge | ml.m6gd.large | ml.m6gd.xlarge | ml.m6gd.2xlarge | ml.m6gd.4xlarge | ml.m6gd.8xlarge | ml.m6gd.12xlarge | ml.m6gd.16xlarge | ml.c6g.large | ml.c6g.xlarge | ml.c6g.2xlarge | ml.c6g.4xlarge | ml.c6g.8xlarge | ml.c6g.12xlarge | ml.c6g.16xlarge | ml.c6gd.large | ml.c6gd.xlarge | ml.c6gd.2xlarge | ml.c6gd.4xlarge | ml.c6gd.8xlarge | ml.c6gd.12xlarge | ml.c6gd.16xlarge | ml.c6gn.large | ml.c6gn.xlarge | ml.c6gn.2xlarge | ml.c6gn.4xlarge | ml.c6gn.8xlarge | ml.c6gn.12xlarge | ml.c6gn.16xlarge | ml.r6g.large | ml.r6g.xlarge | ml.r6g.2xlarge | ml.r6g.4xlarge | ml.r6g.8xlarge | ml.r6g.12xlarge | ml.r6g.16xlarge | ml.r6gd.large | ml.r6gd.xlarge | ml.r6gd.2xlarge | ml.r6gd.4xlarge | ml.r6gd.8xlarge | ml.r6gd.12xlarge | ml.r6gd.16xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.inf2.xlarge | ml.inf2.8xlarge | ml.inf2.24xlarge | ml.inf2.48xlarge | ml.p5.48xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge, ServerlessConfig: { MaxConcurrency: 'NUMBER_VALUE', /* required */ MemorySizeInMB: 'NUMBER_VALUE', /* required */ ProvisionedConcurrency: 'NUMBER_VALUE' } }, /* more items */ ], Endpoints: [ { EndpointName: 'STRING_VALUE' }, /* more items */ ], JobDurationInSeconds: 'NUMBER_VALUE', ModelName: 'STRING_VALUE', ModelPackageVersionArn: 'STRING_VALUE', ResourceLimit: { MaxNumberOfTests: 'NUMBER_VALUE', MaxParallelOfTests: 'NUMBER_VALUE' }, TrafficPattern: { Phases: [ { DurationInSeconds: 'NUMBER_VALUE', InitialNumberOfUsers: 'NUMBER_VALUE', SpawnRate: 'NUMBER_VALUE' }, /* more items */ ], Stairs: { DurationInSeconds: 'NUMBER_VALUE', NumberOfSteps: 'NUMBER_VALUE', UsersPerStep: 'NUMBER_VALUE' }, TrafficType: PHASES | STAIRS }, VolumeKmsKeyId: 'STRING_VALUE', VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, JobName: 'STRING_VALUE', /* required */ JobType: Default | Advanced, /* required */ RoleArn: 'STRING_VALUE', /* required */ JobDescription: 'STRING_VALUE', OutputConfig: { CompiledOutputConfig: { S3OutputUri: 'STRING_VALUE' }, KmsKeyId: 'STRING_VALUE' }, StoppingConditions: { FlatInvocations: Continue | Stop, MaxInvocations: 'NUMBER_VALUE', ModelLatencyThresholds: [ { Percentile: 'STRING_VALUE', ValueInMilliseconds: 'NUMBER_VALUE' }, /* more items */ ] }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createInferenceRecommendationsJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobName
— (String
)A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account. The job name is passed down to the resources created by the recommendation job. The names of resources (such as the model, endpoint configuration, endpoint, and compilation) that are prefixed with the job name are truncated at 40 characters.
JobType
— (String
)Defines the type of recommendation job. Specify
Possible values include:Default
to initiate an instance recommendation andAdvanced
to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (DEFAULT
) job."Default"
"Advanced"
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
InputConfig
— (map
)Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.
ModelPackageVersionArn
— (String
)The Amazon Resource Name (ARN) of a versioned model package.
ModelName
— (String
)The name of the created model.
JobDurationInSeconds
— (Integer
)Specifies the maximum duration of the job, in seconds. The maximum value is 18,000 seconds.
TrafficPattern
— (map
)Specifies the traffic pattern of the job.
TrafficType
— (String
)Defines the traffic patterns. Choose either
Possible values include:PHASES
orSTAIRS
."PHASES"
"STAIRS"
Phases
— (Array<map>
)Defines the phases traffic specification.
InitialNumberOfUsers
— (Integer
)Specifies how many concurrent users to start with. The value should be between 1 and 3.
SpawnRate
— (Integer
)Specified how many new users to spawn in a minute.
DurationInSeconds
— (Integer
)Specifies how long a traffic phase should be. For custom load tests, the value should be between 120 and 3600. This value should not exceed
JobDurationInSeconds
.
Stairs
— (map
)Defines the stairs traffic pattern.
DurationInSeconds
— (Integer
)Defines how long each traffic step should be.
NumberOfSteps
— (Integer
)Specifies how many steps to perform during traffic.
UsersPerStep
— (Integer
)Specifies how many new users to spawn in each step.
ResourceLimit
— (map
)Defines the resource limit of the job.
MaxNumberOfTests
— (Integer
)Defines the maximum number of load tests.
MaxParallelOfTests
— (Integer
)Defines the maximum number of parallel load tests.
EndpointConfigurations
— (Array<map>
)Specifies the endpoint configuration to use for a job.
InstanceType
— (String
)The instance types to use for the load test.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
ServerlessConfig
— (map
)Specifies the serverless configuration for an endpoint variant.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
EnvironmentParameterRanges
— (map
)The parameter you want to benchmark against.
CategoricalParameterRanges
— (Array<map>
)Specified a list of parameters for each category.
Name
— required — (String
)The Name of the environment variable.
Value
— required — (Array<String>
)The list of values you can pass.
VolumeKmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. This key will be passed to SageMaker Hosting for endpoint creation.
The SageMaker execution role must have
kms:CreateGrant
permission in order to encrypt data on the storage volume of the endpoints created for inference recommendation. The inference recommendation job will fail asynchronously during endpoint configuration creation if the role passed does not havekms:CreateGrant
permission.The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"
For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
-
ContainerConfig
— (map
)Specifies mandatory fields for running an Inference Recommender job. The fields specified in
ContainerConfig
override the corresponding fields in the model package.Domain
— (String
)The machine learning domain of the model and its components.
Valid Values:
COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING
Task
— (String
)The machine learning task that the model accomplishes.
Valid Values:
IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER
Framework
— (String
)The machine learning framework of the container image.
Valid Values:
TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN
FrameworkVersion
— (String
)The framework version of the container image.
PayloadConfig
— (map
)Specifies the
SamplePayloadUrl
and all other sample payload-related fields.SamplePayloadUrl
— (String
)The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
NearestModelName
— (String
)The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.
Valid Values:
efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet
SupportedInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
SupportedEndpointType
— (String
)The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.
Possible values include:"RealTime"
"Serverless"
DataInputConfig
— (String
)Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
Endpoints
— (Array<map>
)Existing customer endpoints on which to run an Inference Recommender job.
EndpointName
— (String
)The name of a customer's endpoint.
VpcConfig
— (map
)Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs. IDs have the form of
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your model.
JobDescription
— (String
)Description of the recommendation job.
StoppingConditions
— (map
)A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.
MaxInvocations
— (Integer
)The maximum number of requests per minute expected for the endpoint.
ModelLatencyThresholds
— (Array<map>
)The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
Percentile
— (String
)The model latency percentile threshold. Acceptable values are
P95
andP99
. For custom load tests, specify the value asP95
.ValueInMilliseconds
— (Integer
)The model latency percentile value in milliseconds.
FlatInvocations
— (String
)Stops a load test when the number of invocations (TPS) peaks and flattens, which means that the instance has reached capacity. The default value is
Possible values include:Stop
. If you want the load test to continue after invocations have flattened, set the value toContinue
."Continue"
"Stop"
OutputConfig
— (map
)Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt your output artifacts with Amazon S3 server-side encryption. The SageMaker execution role must have
kms:GenerateDataKey
permission.The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"
For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
-
CompiledOutputConfig
— (map
)Provides information about the output configuration for the compiled model.
S3OutputUri
— (String
)Identifies the Amazon S3 bucket where you want SageMaker to store the compiled model artifacts.
Tags
— (Array<map>
)The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobArn
— (String
)The Amazon Resource Name (ARN) of the recommendation job.
-
(AWS.Response)
—
Returns:
createLabelingJob(params = {}, callback) ⇒ AWS.Request
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
You can select your workforce from one of three providers:
-
A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
-
One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.
-
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in
ManifestS3Uri
have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress
) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.Service Reference:
Examples:
Calling the createLabelingJob operation
var params = { HumanTaskConfig: { /* required */ AnnotationConsolidationConfig: { /* required */ AnnotationConsolidationLambdaArn: 'STRING_VALUE' /* required */ }, NumberOfHumanWorkersPerDataObject: 'NUMBER_VALUE', /* required */ PreHumanTaskLambdaArn: 'STRING_VALUE', /* required */ TaskDescription: 'STRING_VALUE', /* required */ TaskTimeLimitInSeconds: 'NUMBER_VALUE', /* required */ TaskTitle: 'STRING_VALUE', /* required */ UiConfig: { /* required */ HumanTaskUiArn: 'STRING_VALUE', UiTemplateS3Uri: 'STRING_VALUE' }, WorkteamArn: 'STRING_VALUE', /* required */ MaxConcurrentTaskCount: 'NUMBER_VALUE', PublicWorkforceTaskPrice: { AmountInUsd: { Cents: 'NUMBER_VALUE', Dollars: 'NUMBER_VALUE', TenthFractionsOfACent: 'NUMBER_VALUE' } }, TaskAvailabilityLifetimeInSeconds: 'NUMBER_VALUE', TaskKeywords: [ 'STRING_VALUE', /* more items */ ] }, InputConfig: { /* required */ DataSource: { /* required */ S3DataSource: { ManifestS3Uri: 'STRING_VALUE' /* required */ }, SnsDataSource: { SnsTopicArn: 'STRING_VALUE' /* required */ } }, DataAttributes: { ContentClassifiers: [ FreeOfPersonallyIdentifiableInformation | FreeOfAdultContent, /* more items */ ] } }, LabelAttributeName: 'STRING_VALUE', /* required */ LabelingJobName: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', SnsTopicArn: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ LabelCategoryConfigS3Uri: 'STRING_VALUE', LabelingJobAlgorithmsConfig: { LabelingJobAlgorithmSpecificationArn: 'STRING_VALUE', /* required */ InitialActiveLearningModelArn: 'STRING_VALUE', LabelingJobResourceConfig: { VolumeKmsKeyId: 'STRING_VALUE', VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } } }, StoppingConditions: { MaxHumanLabeledObjectCount: 'NUMBER_VALUE', MaxPercentageOfInputDatasetLabeled: 'NUMBER_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createLabelingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
LabelingJobName
— (String
)The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region.
LabelingJobName
is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.LabelAttributeName
— (String
)The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The
LabelAttributeName
must meet the following requirements.-
The name can't end with "-metadata".
-
If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
-
Image semantic segmentation (
SemanticSegmentation)
, and adjustment (AdjustmentSemanticSegmentation
) and verification (VerificationSemanticSegmentation
) labeling jobs for this task type. -
Video frame object detection (
VideoObjectDetection
), and adjustment and verification (AdjustmentVideoObjectDetection
) labeling jobs for this task type. -
Video frame object tracking (
VideoObjectTracking
), and adjustment and verification (AdjustmentVideoObjectTracking
) labeling jobs for this task type. -
3D point cloud semantic segmentation (
3DPointCloudSemanticSegmentation
), and adjustment and verification (Adjustment3DPointCloudSemanticSegmentation
) labeling jobs for this task type. -
3D point cloud object tracking (
3DPointCloudObjectTracking
), and adjustment and verification (Adjustment3DPointCloudObjectTracking
) labeling jobs for this task type.
-
If you are creating an adjustment or verification labeling job, you must use a different
LabelAttributeName
than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see Verify and Adjust Labels.-
InputConfig
— (map
)Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
You must specify at least one of the following:
S3DataSource
orSnsDataSource
.-
Use
SnsDataSource
to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in the input manifest file have been labeled. -
Use
S3DataSource
to specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSource
is optional if you useSnsDataSource
to create a streaming labeling job.
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use
ContentClassifiers
to specify that your data is free of personally identifiable information and adult content.DataSource
— required — (map
)The location of the input data.
S3DataSource
— (map
)The Amazon S3 location of the input data objects.
ManifestS3Uri
— required — (String
)The Amazon S3 location of the manifest file that describes the input data objects.
The input manifest file referenced in
ManifestS3Uri
must contain one of the following keys:source-ref
orsource
. The value of the keys are interpreted as follows:-
source-ref
: The source of the object is the Amazon S3 object specified in the value. Use this value when the object is a binary object, such as an image. -
source
: The source of the object is the value. Use this value when the object is a text value.
If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
-
SnsDataSource
— (map
)An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job.
SnsTopicArn
— required — (String
)The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.
DataAttributes
— (map
)Attributes of the data specified by the customer.
ContentClassifiers
— (Array<String>
)Declares that your content is free of personally identifiable information or adult content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
-
OutputConfig
— (map
)The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
S3OutputPath
— required — (String
)The Amazon S3 location to write output data.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any.
If you provide your own KMS key ID, you must add the required permissions to your KMS key described in Encrypt Output Data and Storage Volume with Amazon Web Services KMS.
If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your role's account to encrypt your output data.
If you use a bucket policy with an
s3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.SnsTopicArn
— (String
)An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a
SnsTopicArn
if you want to do real time chaining to another streaming job and receive an Amazon SNS notifications each time a data object is submitted by a worker.If you provide an
SnsTopicArn
inOutputConfig
, when workers complete labeling tasks, Ground Truth will send labeling task output data to the SNS output topic you specify here.To learn more, see Receive Output Data from a Streaming Labeling Job.
RoleArn
— (String
)The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
LabelCategoryConfigS3Uri
— (String
)The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For named entity recognition jobs, in addition to
"labels"
, you must provide worker instructions in the label category configuration file using the"instructions"
parameter:"instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}
. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .For all other built-in task types and custom tasks, your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing
label_1
,label_2
,...
,label_n
with your label categories.{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
-
For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
-
Each label category must be unique, you cannot specify duplicate label categories.
-
If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include
auditLabelAttributeName
in the label category configuration. Use this parameter to enter theLabelAttributeName
of the labeling job you want to adjust or verify annotations of.
-
StoppingConditions
— (map
)A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
MaxHumanLabeledObjectCount
— (Integer
)The maximum number of objects that can be labeled by human workers.
MaxPercentageOfInputDatasetLabeled
— (Integer
)The maximum number of input data objects that should be labeled.
LabelingJobAlgorithmsConfig
— (map
)Configures the information required to perform automated data labeling.
LabelingJobAlgorithmSpecificationArn
— required — (String
)Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:
-
Image classification
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification
-
Text classification
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification
-
Object detection
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection
-
Semantic Segmentation
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation
-
InitialActiveLearningModelArn
— (String
)At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.
LabelingJobResourceConfig
— (map
)Provides configuration information for a labeling job.
VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training and inference jobs used for automated data labeling.
You can only specify a
VolumeKmsKeyId
when you create a labeling job with automated data labeling enabled using the API operationCreateLabelingJob
. You cannot specify an Amazon Web Services KMS key to encrypt the storage volume used for automated data labeling model training and inference when you create a labeling job using the console. To learn more, see Output Data and Storage Volume Encryption.The
VolumeKmsKeyId
can be any of the following formats:-
KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
HumanTaskConfig
— (map
)Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
WorkteamArn
— required — (String
)The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
UiConfig
— required — (map
)Information about the user interface that workers use to complete the labeling task.
UiTemplateS3Uri
— (String
)The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template.
HumanTaskUiArn
— (String
)The ARN of the worker task template used to render the worker UI and tools for labeling job tasks.
Use this parameter when you are creating a labeling job for named entity recognition, 3D point cloud and video frame labeling jobs. Use your labeling job task type to select one of the following ARNs and use it with this parameter when you create a labeling job. Replace
aws-region
with the Amazon Web Services Region you are creating your labeling job in. For example, replaceaws-region
withus-west-1
if you create a labeling job in US West (N. California).Named Entity Recognition
Use the following
HumanTaskUiArn
for named entity recognition labeling jobs:arn:aws:sagemaker:aws-region:394669845002:human-task-ui/NamedEntityRecognition
3D Point Cloud HumanTaskUiArns
Use this
HumanTaskUiArn
for 3D point cloud object detection and 3D point cloud object detection adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection
Use this
HumanTaskUiArn
for 3D point cloud object tracking and 3D point cloud object tracking adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking
Use this
HumanTaskUiArn
for 3D point cloud semantic segmentation and 3D point cloud semantic segmentation adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation
Video Frame HumanTaskUiArns
Use this
HumanTaskUiArn
for video frame object detection and video frame object detection adjustment labeling jobs.-
arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetection
Use this
HumanTaskUiArn
for video frame object tracking and video frame object tracking adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTracking
-
PreHumanTaskLambdaArn
— required — (String
)The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.
For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for
PreHumanTaskLambdaArn
. For custom labeling workflows, see Pre-annotation Lambda.Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking
3D Point Cloud Modalities
Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types to learn more.
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .
Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBox
Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox
Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation
Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking
3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection
3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking
3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
TaskKeywords
— (Array<String>
)Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
TaskTitle
— required — (String
)A title for the task for your human workers.
TaskDescription
— required — (String
)A description of the task for your human workers.
NumberOfHumanWorkersPerDataObject
— required — (Integer
)The number of human workers that will label an object.
TaskTimeLimitInSeconds
— required — (Integer
)The amount of time that a worker has to complete a task.
If you create a custom labeling job, the maximum value for this parameter is 8 hours (28,800 seconds).
If you create a labeling job using a built-in task type the maximum for this parameter depends on the task type you use:
-
For image and text labeling jobs, the maximum is 8 hours (28,800 seconds).
-
For 3D point cloud and video frame labeling jobs, the maximum is 30 days (2952,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
-
TaskAvailabilityLifetimeInSeconds
— (Integer
)The length of time that a task remains available for labeling by human workers. The default and maximum values for this parameter depend on the type of workforce you use.
-
If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43,200 seconds). The default is 6 hours (21,600 seconds).
-
If you choose a private or vendor workforce, the default value is 30 days (2592,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
-
MaxConcurrentTaskCount
— (Integer
)Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects. To increase the maximum value to 5000 objects, contact Amazon Web Services Support.
AnnotationConsolidationConfig
— required — (map
)Configures how labels are consolidated across human workers.
AnnotationConsolidationLambdaArn
— required — (String
)The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.
This parameter is required for all labeling jobs. For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for
AnnotationConsolidationLambdaArn
. For custom labeling workflows, see Post-annotation Lambda.Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClass
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetection
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .
Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation
Semantic Segmentation Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation
Bounding Box Adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox
Bounding Box Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectDetection
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectTracking
3D Point Cloud Object Detection Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection
3D Point Cloud Object Tracking Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking
3D Point Cloud Semantic Segmentation Adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
PublicWorkforceTaskPrice
— (map
)The price that you pay for each task performed by an Amazon Mechanical Turk worker.
AmountInUsd
— (map
)Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
Dollars
— (Integer
)The whole number of dollars in the amount.
Cents
— (Integer
)The fractional portion, in cents, of the amount.
TenthFractionsOfACent
— (Integer
)Fractions of a cent, in tenths.
Tags
— (Array<map>
)An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.
-
(AWS.Response)
—
Returns:
createMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.
Service Reference:
Examples:
Calling the createMlflowTrackingServer operation
var params = { ArtifactStoreUri: 'STRING_VALUE', /* required */ RoleArn: 'STRING_VALUE', /* required */ TrackingServerName: 'STRING_VALUE', /* required */ AutomaticModelRegistration: true || false, MlflowVersion: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], TrackingServerSize: Small | Medium | Large, WeeklyMaintenanceWindowStart: 'STRING_VALUE' }; sagemaker.createMlflowTrackingServer(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrackingServerName
— (String
)A unique string identifying the tracking server name. This string is part of the tracking server ARN.
ArtifactStoreUri
— (String
)The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.
TrackingServerSize
— (String
)The size of the tracking server you want to create. You can choose between
"Small"
,"Medium"
, and"Large"
. The default MLflow Tracking Server configuration size is"Small"
. You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.
Possible values include:"Small"
"Medium"
"Large"
MlflowVersion
— (String
)The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.
RoleArn
— (String
)The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have
AmazonS3FullAccess
permissions. For more information on IAM permissions for tracking server creation, see Set up IAM permissions for MLflow.AutomaticModelRegistration
— (Boolean
)Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to
True
. To disable automatic model registration, set this value toFalse
. If not specified,AutomaticModelRegistration
defaults toFalse
.WeeklyMaintenanceWindowStart
— (String
)The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.
Tags
— (Array<map>
)Tags consisting of key-value pairs used to manage metadata for the tracking server.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrackingServerArn
— (String
)The ARN of the tracking server.
-
(AWS.Response)
—
Returns:
createModel(params = {}, callback) ⇒ AWS.Request
Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the
CreateEndpointConfig
API, and then create an endpoint with theCreateEndpoint
API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.To run a batch transform using your model, you start a job with the
CreateTransformJob
API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
Service Reference:
Examples:
Calling the createModel operation
var params = { ModelName: 'STRING_VALUE', /* required */ Containers: [ { AdditionalModelDataSources: [ { ChannelName: 'STRING_VALUE', /* required */ S3DataSource: { /* required */ CompressionType: None | Gzip, /* required */ S3DataType: S3Prefix | S3Object, /* required */ S3Uri: 'STRING_VALUE', /* required */ HubAccessConfig: { HubContentArn: 'STRING_VALUE' /* required */ }, ModelAccessConfig: { AcceptEula: true || false /* required */ } } }, /* more items */ ], ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Image: 'STRING_VALUE', ImageConfig: { RepositoryAccessMode: Platform | Vpc, /* required */ RepositoryAuthConfig: { RepositoryCredentialsProviderArn: 'STRING_VALUE' /* required */ } }, InferenceSpecificationName: 'STRING_VALUE', Mode: SingleModel | MultiModel, ModelDataSource: { S3DataSource: { CompressionType: None | Gzip, /* required */ S3DataType: S3Prefix | S3Object, /* required */ S3Uri: 'STRING_VALUE', /* required */ HubAccessConfig: { HubContentArn: 'STRING_VALUE' /* required */ }, ModelAccessConfig: { AcceptEula: true || false /* required */ } } }, ModelDataUrl: 'STRING_VALUE', ModelPackageName: 'STRING_VALUE', MultiModelConfig: { ModelCacheSetting: Enabled | Disabled } }, /* more items */ ], EnableNetworkIsolation: true || false, ExecutionRoleArn: 'STRING_VALUE', InferenceExecutionConfig: { Mode: Serial | Direct /* required */ }, PrimaryContainer: { AdditionalModelDataSources: [ { ChannelName: 'STRING_VALUE', /* required */ S3DataSource: { /* required */ CompressionType: None | Gzip, /* required */ S3DataType: S3Prefix | S3Object, /* required */ S3Uri: 'STRING_VALUE', /* required */ HubAccessConfig: { HubContentArn: 'STRING_VALUE' /* required */ }, ModelAccessConfig: { AcceptEula: true || false /* required */ } } }, /* more items */ ], ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Image: 'STRING_VALUE', ImageConfig: { RepositoryAccessMode: Platform | Vpc, /* required */ RepositoryAuthConfig: { RepositoryCredentialsProviderArn: 'STRING_VALUE' /* required */ } }, InferenceSpecificationName: 'STRING_VALUE', Mode: SingleModel | MultiModel, ModelDataSource: { S3DataSource: { CompressionType: None | Gzip, /* required */ S3DataType: S3Prefix | S3Object, /* required */ S3Uri: 'STRING_VALUE', /* required */ HubAccessConfig: { HubContentArn: 'STRING_VALUE' /* required */ }, ModelAccessConfig: { AcceptEula: true || false /* required */ } } }, ModelDataUrl: 'STRING_VALUE', ModelPackageName: 'STRING_VALUE', MultiModelConfig: { ModelCacheSetting: Enabled | Disabled } }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }; sagemaker.createModel(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelName
— (String
)The name of the new model.
PrimaryContainer
— (map
)The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
ContainerHostname
— (String
)This parameter is ignored for models that contain only a
PrimaryContainer
.When a
ContainerDefinition
is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for aContainerDefinition
that is part of an inference pipeline, a unique name is automatically assigned based on the position of theContainerDefinition
in the pipeline. If you specify a value for theContainerHostName
for anyContainerDefinition
that is part of an inference pipeline, you must specify a value for theContainerHostName
parameter of everyContainerDefinition
in that pipeline.Image
— (String
)The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.ImageConfig
— (map
)Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.RepositoryAccessMode
— required — (String
)Set this to one of the following values:
-
Platform
- The model image is hosted in Amazon ECR. -
Vpc
- The model image is hosted in a private Docker registry in your VPC.
"Platform"
"Vpc"
-
RepositoryAuthConfig
— (map
)(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified
Vpc
as the value for theRepositoryAccessMode
field, and the private Docker registry where the model image is hosted requires authentication.RepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide.
Mode
— (String
)Whether the container hosts a single model or multiple models.
Possible values include:"SingleModel"
"MultiModel"
ModelDataUrl
— (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in
ModelDataUrl
.ModelDataSource
— (map
)Specifies the location of ML model data to deploy.
Note: Currently you cannot useModelDataSource
in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
AdditionalModelDataSources
— (Array<map>
)Data sources that are available to your model in addition to the one that you specify for
ModelDataSource
when you use theCreateModel
action.ChannelName
— required — (String
)A custom name for this
AdditionalModelDataSource
object.S3DataSource
— required — (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
Environment
— (map<String>
)The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.
The maximum length of each key and value in the
Environment
map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to aCreateModel
request, then the maximum length of all of their maps, combined, is also 32 KB.ModelPackageName
— (String
)The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
MultiModelConfig
— (map
)Specifies additional configuration for multi-model endpoints.
ModelCacheSetting
— (String
)Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to
Possible values include:Disabled
."Enabled"
"Disabled"
Containers
— (Array<map>
)Specifies the containers in the inference pipeline.
ContainerHostname
— (String
)This parameter is ignored for models that contain only a
PrimaryContainer
.When a
ContainerDefinition
is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for aContainerDefinition
that is part of an inference pipeline, a unique name is automatically assigned based on the position of theContainerDefinition
in the pipeline. If you specify a value for theContainerHostName
for anyContainerDefinition
that is part of an inference pipeline, you must specify a value for theContainerHostName
parameter of everyContainerDefinition
in that pipeline.Image
— (String
)The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.ImageConfig
— (map
)Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.RepositoryAccessMode
— required — (String
)Set this to one of the following values:
-
Platform
- The model image is hosted in Amazon ECR. -
Vpc
- The model image is hosted in a private Docker registry in your VPC.
"Platform"
"Vpc"
-
RepositoryAuthConfig
— (map
)(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified
Vpc
as the value for theRepositoryAccessMode
field, and the private Docker registry where the model image is hosted requires authentication.RepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide.
Mode
— (String
)Whether the container hosts a single model or multiple models.
Possible values include:"SingleModel"
"MultiModel"
ModelDataUrl
— (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in
ModelDataUrl
.ModelDataSource
— (map
)Specifies the location of ML model data to deploy.
Note: Currently you cannot useModelDataSource
in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
AdditionalModelDataSources
— (Array<map>
)Data sources that are available to your model in addition to the one that you specify for
ModelDataSource
when you use theCreateModel
action.ChannelName
— required — (String
)A custom name for this
AdditionalModelDataSource
object.S3DataSource
— required — (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
Environment
— (map<String>
)The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.
The maximum length of each key and value in the
Environment
map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to aCreateModel
request, then the maximum length of all of their maps, combined, is also 32 KB.ModelPackageName
— (String
)The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
MultiModelConfig
— (map
)Specifies additional configuration for multi-model endpoints.
ModelCacheSetting
— (String
)Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to
Possible values include:Disabled
."Enabled"
"Disabled"
InferenceExecutionConfig
— (map
)Specifies details of how containers in a multi-container endpoint are called.
Mode
— required — (String
)How containers in a multi-container are run. The following values are valid.
-
SERIAL
- Containers run as a serial pipeline. -
DIRECT
- Only the individual container that you specify is run.
"Serial"
"Direct"
-
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles.
Note: To be able to pass this role to SageMaker, the caller of this API must have theiam:PassRole
permission.Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
VpcConfig
— (map
)A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC.
VpcConfig
is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
EnableNetworkIsolation
— (Boolean
)Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelArn
— (String
)The ARN of the model created in SageMaker.
-
(AWS.Response)
—
Returns:
createModelBiasJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates the definition for a model bias job.
Service Reference:
Examples:
Calling the createModelBiasJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE', /* required */ JobResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, ModelBiasAppSpecification: { /* required */ ConfigUri: 'STRING_VALUE', /* required */ ImageUri: 'STRING_VALUE', /* required */ Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ } }, ModelBiasJobInput: { /* required */ GroundTruthS3Input: { /* required */ S3Uri: 'STRING_VALUE' }, BatchTransformInput: { DataCapturedDestinationS3Uri: 'STRING_VALUE', /* required */ DatasetFormat: { /* required */ Csv: { Header: true || false }, Json: { Line: true || false }, Parquet: { } }, LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' }, EndpointInput: { EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' } }, ModelBiasJobOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ ModelBiasBaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' } }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createModelBiasJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ModelBiasBaselineConfig
— (map
)The baseline configuration for a model bias job.
BaseliningJobName
— (String
)The name of the baseline model bias job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
ModelBiasAppSpecification
— (map
)Configures the model bias job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the model bias job.
ConfigUri
— required — (String
)JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
ModelBiasJobInput
— (map
)Inputs for the model bias job.
EndpointInput
— (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
GroundTruthS3Input
— required — (map
)Location of ground truth labels to use in model bias job.
S3Uri
— (String
)The address of the Amazon S3 location of the ground truth labels.
ModelBiasJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
NetworkConfig
— (map
)Networking options for a model bias job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Tags
— (Array<map>
)(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the model bias job.
-
(AWS.Response)
—
Returns:
createModelCard(params = {}, callback) ⇒ AWS.Request
Creates an Amazon SageMaker Model Card.
For information about how to use model cards, see Amazon SageMaker Model Card.
Service Reference:
Examples:
Calling the createModelCard operation
var params = { Content: 'STRING_VALUE', /* required */ ModelCardName: 'STRING_VALUE', /* required */ ModelCardStatus: Draft | PendingReview | Approved | Archived, /* required */ SecurityConfig: { KmsKeyId: 'STRING_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createModelCard(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelCardName
— (String
)The unique name of the model card.
SecurityConfig
— (map
)An optional Key Management Service key to encrypt, decrypt, and re-encrypt model card content for regulated workloads with highly sensitive data.
KmsKeyId
— (String
)A Key Management Service key ID to use for encrypting a model card.
Content
— (String
)The content of the model card. Content must be in model card JSON schema and provided as a string.
ModelCardStatus
— (String
)The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft
: The model card is a work in progress. -
PendingReview
: The model card is pending review. -
Approved
: The model card is approved. -
Archived
: The model card is archived. No more updates should be made to the model card, but it can still be exported.
"Draft"
"PendingReview"
"Approved"
"Archived"
-
Tags
— (Array<map>
)Key-value pairs used to manage metadata for model cards.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelCardArn
— (String
)The Amazon Resource Name (ARN) of the successfully created model card.
-
(AWS.Response)
—
Returns:
createModelCardExportJob(params = {}, callback) ⇒ AWS.Request
Creates an Amazon SageMaker Model Card export job.
Service Reference:
Examples:
Calling the createModelCardExportJob operation
var params = { ModelCardExportJobName: 'STRING_VALUE', /* required */ ModelCardName: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputPath: 'STRING_VALUE' /* required */ }, ModelCardVersion: 'NUMBER_VALUE' }; sagemaker.createModelCardExportJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelCardName
— (String
)The name or Amazon Resource Name (ARN) of the model card to export.
ModelCardVersion
— (Integer
)The version of the model card to export. If a version is not provided, then the latest version of the model card is exported.
ModelCardExportJobName
— (String
)The name of the model card export job.
OutputConfig
— (map
)The model card output configuration that specifies the Amazon S3 path for exporting.
S3OutputPath
— required — (String
)The Amazon S3 output path to export your model card PDF.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelCardExportJobArn
— (String
)The Amazon Resource Name (ARN) of the model card export job.
-
(AWS.Response)
—
Returns:
createModelExplainabilityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates the definition for a model explainability job.
Service Reference:
Examples:
Calling the createModelExplainabilityJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE', /* required */ JobResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, ModelExplainabilityAppSpecification: { /* required */ ConfigUri: 'STRING_VALUE', /* required */ ImageUri: 'STRING_VALUE', /* required */ Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ } }, ModelExplainabilityJobInput: { /* required */ BatchTransformInput: { DataCapturedDestinationS3Uri: 'STRING_VALUE', /* required */ DatasetFormat: { /* required */ Csv: { Header: true || false }, Json: { Line: true || false }, Parquet: { } }, LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' }, EndpointInput: { EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' } }, ModelExplainabilityJobOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ ModelExplainabilityBaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' } }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createModelExplainabilityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ModelExplainabilityBaselineConfig
— (map
)The baseline configuration for a model explainability job.
BaseliningJobName
— (String
)The name of the baseline model explainability job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
ModelExplainabilityAppSpecification
— (map
)Configures the model explainability job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the model explainability job.
ConfigUri
— required — (String
)JSON formatted Amazon S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
ModelExplainabilityJobInput
— (map
)Inputs for the model explainability job.
EndpointInput
— (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
ModelExplainabilityJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
NetworkConfig
— (map
)Networking options for a model explainability job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Tags
— (Array<map>
)(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the model explainability job.
-
(AWS.Response)
—
Returns:
createModelPackage(params = {}, callback) ⇒ AWS.Request
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for
InferenceSpecification
. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value forSourceAlgorithmSpecification
.Note: There are two types of model packages:- Versioned - a model that is part of a model group in the model registry.
- Unversioned - a model package that is not part of a model group.
Service Reference:
Examples:
Calling the createModelPackage operation
var params = { AdditionalInferenceSpecifications: [ { Containers: [ /* required */ { Image: 'STRING_VALUE', /* required */ AdditionalS3DataSource: { S3DataType: S3Object | S3Prefix, /* required */ S3Uri: 'STRING_VALUE', /* required */ CompressionType: None | Gzip }, ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Framework: 'STRING_VALUE', FrameworkVersion: 'STRING_VALUE', ImageDigest: 'STRING_VALUE', ModelDataSource: { S3DataSource: { CompressionType: None | Gzip, /* required */ S3DataType: S3Prefix | S3Object, /* required */ S3Uri: 'STRING_VALUE', /* required */ HubAccessConfig: { HubContentArn: 'STRING_VALUE' /* required */ }, ModelAccessConfig: { AcceptEula: true || false /* required */ } } }, ModelDataUrl: 'STRING_VALUE', ModelInput: { DataInputConfig: 'STRING_VALUE' /* required */ }, NearestModelName: 'STRING_VALUE', ProductId: 'STRING_VALUE' }, /* more items */ ], Name: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', SupportedContentTypes: [ 'STRING_VALUE', /* more items */ ], SupportedRealtimeInferenceInstanceTypes: [ ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.12xlarge | ml.m5d.24xlarge | ml.c4.large | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.large | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.12xlarge | ml.r5.24xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.12xlarge | ml.r5d.24xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.dl1.24xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.p4d.24xlarge | ml.c7g.large | ml.c7g.xlarge | ml.c7g.2xlarge | ml.c7g.4xlarge | ml.c7g.8xlarge | ml.c7g.12xlarge | ml.c7g.16xlarge | ml.m6g.large | ml.m6g.xlarge | ml.m6g.2xlarge | ml.m6g.4xlarge | ml.m6g.8xlarge | ml.m6g.12xlarge | ml.m6g.16xlarge | ml.m6gd.large | ml.m6gd.xlarge | ml.m6gd.2xlarge | ml.m6gd.4xlarge | ml.m6gd.8xlarge | ml.m6gd.12xlarge | ml.m6gd.16xlarge | ml.c6g.large | ml.c6g.xlarge | ml.c6g.2xlarge | ml.c6g.4xlarge | ml.c6g.8xlarge | ml.c6g.12xlarge | ml.c6g.16xlarge | ml.c6gd.large | ml.c6gd.xlarge | ml.c6gd.2xlarge | ml.c6gd.4xlarge | ml.c6gd.8xlarge | ml.c6gd.12xlarge | ml.c6gd.16xlarge | ml.c6gn.large | ml.c6gn.xlarge | ml.c6gn.2xlarge | ml.c6gn.4xlarge | ml.c6gn.8xlarge | ml.c6gn.12xlarge | ml.c6gn.16xlarge | ml.r6g.large | ml.r6g.xlarge | ml.r6g.2xlarge | ml.r6g.4xlarge | ml.r6g.8xlarge | ml.r6g.12xlarge | ml.r6g.16xlarge | ml.r6gd.large | ml.r6gd.xlarge | ml.r6gd.2xlarge | ml.r6gd.4xlarge | ml.r6gd.8xlarge | ml.r6gd.12xlarge | ml.r6gd.16xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.inf2.xlarge | ml.inf2.8xlarge | ml.inf2.24xlarge | ml.inf2.48xlarge | ml.p5.48xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge, /* more items */ ], SupportedResponseMIMETypes: [ 'STRING_VALUE', /* more items */ ], SupportedTransformInstanceTypes: [ ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge, /* more items */ ] }, /* more items */ ], CertifyForMarketplace: true || false, ClientToken: 'STRING_VALUE', CustomerMetadataProperties: { '<CustomerMetadataKey>': 'STRING_VALUE', /* '<CustomerMetadataKey>': ... */ }, Domain: 'STRING_VALUE', DriftCheckBaselines: { Bias: { ConfigFile: { S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE', ContentType: 'STRING_VALUE' }, PostTrainingConstraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, PreTrainingConstraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, Explainability: { ConfigFile: { S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE', ContentType: 'STRING_VALUE' }, Constraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, ModelDataQuality: { Constraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, Statistics: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, ModelQuality: { Constraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, Statistics: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } } }, InferenceSpecification: { Containers: [ /* required */ { Image: 'STRING_VALUE', /* required */ AdditionalS3DataSource: { S3DataType: S3Object | S3Prefix, /* required */ S3Uri: 'STRING_VALUE', /* required */ CompressionType: None | Gzip }, ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Framework: 'STRING_VALUE', FrameworkVersion: 'STRING_VALUE', ImageDigest: 'STRING_VALUE', ModelDataSource: { S3DataSource: { CompressionType: None | Gzip, /* required */ S3DataType: S3Prefix | S3Object, /* required */ S3Uri: 'STRING_VALUE', /* required */ HubAccessConfig: { HubContentArn: 'STRING_VALUE' /* required */ }, ModelAccessConfig: { AcceptEula: true || false /* required */ } } }, ModelDataUrl: 'STRING_VALUE', ModelInput: { DataInputConfig: 'STRING_VALUE' /* required */ }, NearestModelName: 'STRING_VALUE', ProductId: 'STRING_VALUE' }, /* more items */ ], SupportedContentTypes: [ 'STRING_VALUE', /* more items */ ], SupportedRealtimeInferenceInstanceTypes: [ ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.12xlarge | ml.m5d.24xlarge | ml.c4.large | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.large | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.12xlarge | ml.r5.24xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.12xlarge | ml.r5d.24xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.dl1.24xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.p4d.24xlarge | ml.c7g.large | ml.c7g.xlarge | ml.c7g.2xlarge | ml.c7g.4xlarge | ml.c7g.8xlarge | ml.c7g.12xlarge | ml.c7g.16xlarge | ml.m6g.large | ml.m6g.xlarge | ml.m6g.2xlarge | ml.m6g.4xlarge | ml.m6g.8xlarge | ml.m6g.12xlarge | ml.m6g.16xlarge | ml.m6gd.large | ml.m6gd.xlarge | ml.m6gd.2xlarge | ml.m6gd.4xlarge | ml.m6gd.8xlarge | ml.m6gd.12xlarge | ml.m6gd.16xlarge | ml.c6g.large | ml.c6g.xlarge | ml.c6g.2xlarge | ml.c6g.4xlarge | ml.c6g.8xlarge | ml.c6g.12xlarge | ml.c6g.16xlarge | ml.c6gd.large | ml.c6gd.xlarge | ml.c6gd.2xlarge | ml.c6gd.4xlarge | ml.c6gd.8xlarge | ml.c6gd.12xlarge | ml.c6gd.16xlarge | ml.c6gn.large | ml.c6gn.xlarge | ml.c6gn.2xlarge | ml.c6gn.4xlarge | ml.c6gn.8xlarge | ml.c6gn.12xlarge | ml.c6gn.16xlarge | ml.r6g.large | ml.r6g.xlarge | ml.r6g.2xlarge | ml.r6g.4xlarge | ml.r6g.8xlarge | ml.r6g.12xlarge | ml.r6g.16xlarge | ml.r6gd.large | ml.r6gd.xlarge | ml.r6gd.2xlarge | ml.r6gd.4xlarge | ml.r6gd.8xlarge | ml.r6gd.12xlarge | ml.r6gd.16xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.inf2.xlarge | ml.inf2.8xlarge | ml.inf2.24xlarge | ml.inf2.48xlarge | ml.p5.48xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge, /* more items */ ], SupportedResponseMIMETypes: [ 'STRING_VALUE', /* more items */ ], SupportedTransformInstanceTypes: [ ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge, /* more items */ ] }, MetadataProperties: { CommitId: 'STRING_VALUE', GeneratedBy: 'STRING_VALUE', ProjectId: 'STRING_VALUE', Repository: 'STRING_VALUE' }, ModelApprovalStatus: Approved | Rejected | PendingManualApproval, ModelCard: { ModelCardContent: 'STRING_VALUE', ModelCardStatus: Draft | PendingReview | Approved | Archived }, ModelMetrics: { Bias: { PostTrainingReport: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, PreTrainingReport: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, Report: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, Explainability: { Report: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, ModelDataQuality: { Constraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, Statistics: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } }, ModelQuality: { Constraints: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' }, Statistics: { ContentType: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ ContentDigest: 'STRING_VALUE' } } }, ModelPackageDescription: 'STRING_VALUE', ModelPackageGroupName: 'STRING_VALUE', ModelPackageName: 'STRING_VALUE', SamplePayloadUrl: 'STRING_VALUE', SecurityConfig: { KmsKeyId: 'STRING_VALUE' /* required */ }, SkipModelValidation: All | None, SourceAlgorithmSpecification: { SourceAlgorithms: [ /* required */ { AlgorithmName: 'STRING_VALUE', /* required */ ModelDataSource: { S3DataSource: { CompressionType: None | Gzip, /* required */ S3DataType: S3Prefix | S3Object, /* required */ S3Uri: 'STRING_VALUE', /* required */ HubAccessConfig: { HubContentArn: 'STRING_VALUE' /* required */ }, ModelAccessConfig: { AcceptEula: true || false /* required */ } } }, ModelDataUrl: 'STRING_VALUE' }, /* more items */ ] }, SourceUri: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], Task: 'STRING_VALUE', ValidationSpecification: { ValidationProfiles: [ /* required */ { ProfileName: 'STRING_VALUE', /* required */ TransformJobDefinition: { /* required */ TransformInput: { /* required */ DataSource: { /* required */ S3DataSource: { /* required */ S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE' /* required */ } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', SplitType: None | Line | RecordIO | TFRecord }, TransformOutput: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ Accept: 'STRING_VALUE', AssembleWith: None | Line, KmsKeyId: 'STRING_VALUE' }, TransformResources: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge, /* required */ VolumeKmsKeyId: 'STRING_VALUE' }, BatchStrategy: MultiRecord | SingleRecord, Environment: { '<TransformEnvironmentKey>': 'STRING_VALUE', /* '<TransformEnvironmentKey>': ... */ }, MaxConcurrentTransforms: 'NUMBER_VALUE', MaxPayloadInMB: 'NUMBER_VALUE' } }, /* more items */ ], ValidationRole: 'STRING_VALUE' /* required */ } }; sagemaker.createModelPackage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageName
— (String
)The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
This parameter is required for unversioned models. It is not applicable to versioned models.
ModelPackageGroupName
— (String
)The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.
This parameter is required for versioned models, and does not apply to unversioned models.
ModelPackageDescription
— (String
)A description of the model package.
InferenceSpecification
— (map
)Specifies details about inference jobs that you can run with models based on this model package, including the following information:
-
The Amazon ECR paths of containers that contain the inference code and model artifacts.
-
The instance types that the model package supports for transform jobs and real-time endpoints used for inference.
-
The input and output content formats that the model package supports for inference.
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
-
ValidationSpecification
— (map
)Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.
ValidationRole
— required — (String
)The IAM roles to be used for the validation of the model package.
ValidationProfiles
— required — (Array<map>
)An array of
ModelPackageValidationProfile
objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.ProfileName
— required — (String
)The name of the profile for the model package.
TransformJobDefinition
— required — (map
)The
TransformJobDefinition
object that describes the transform job used for the validation of the model package.MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB
— (Integer
)The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy
— (String
)A string that determines the number of records included in a single mini-batch.
SingleRecord
means only one record is used per mini-batch.MultiRecord
means a mini-batch is set to contain as many records that can fit within theMaxPayloadInMB
limit."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— required — (map
)A description of the input source and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— required — (map
)Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
TransformResources
— required — (map
)Identifies the ML compute instances for the transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. The default value is
1
, and the maximum is100
. For distributed transform jobs, specify a value greater than1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
SourceAlgorithmSpecification
— (map
)Details about the algorithm that was used to create the model package.
SourceAlgorithms
— required — (Array<map>
)A list of the algorithms that were used to create a model package.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the algorithm.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
AlgorithmName
— required — (String
)The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
CertifyForMarketplace
— (Boolean
)Whether to certify the model package for listing on Amazon Web Services Marketplace.
This parameter is optional for unversioned models, and does not apply to versioned models.
Tags
— (Array<map>
)A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
If you supply
ModelPackageGroupName
, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply atag
argument.Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ModelApprovalStatus
— (String
)Whether the model is approved for deployment.
This parameter is optional for versioned models, and does not apply to unversioned models.
For versioned models, the value of this parameter must be set to
Possible values include:Approved
to deploy the model."Approved"
"Rejected"
"PendingManualApproval"
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
ModelMetrics
— (map
)A structure that contains model metrics reports.
ModelQuality
— (map
)Metrics that measure the quality of a model.
Statistics
— (map
)Model quality statistics.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)Model quality constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
ModelDataQuality
— (map
)Metrics that measure the quality of the input data for a model.
Statistics
— (map
)Data quality statistics for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)Data quality constraints for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Bias
— (map
)Metrics that measure bias in a model.
Report
— (map
)The bias report for a model
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
PreTrainingReport
— (map
)The pre-training bias report for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
PostTrainingReport
— (map
)The post-training bias report for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Explainability
— (map
)Metrics that help explain a model.
Report
— (map
)The explainability report for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
ClientToken
— (String
)A unique token that guarantees that the call to this API is idempotent.
If a token is not provided, the SDK will use a version 4 UUID.Domain
— (String
)The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
Task
— (String
)The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender:
"IMAGE_CLASSIFICATION"
|"OBJECT_DETECTION"
|"TEXT_GENERATION"
|"IMAGE_SEGMENTATION"
|"FILL_MASK"
|"CLASSIFICATION"
|"REGRESSION"
|"OTHER"
.Specify "OTHER" if none of the tasks listed fit your use case.
SamplePayloadUrl
— (String
)The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.
CustomerMetadataProperties
— (map<String>
)The metadata properties associated with the model package versions.
DriftCheckBaselines
— (map
)Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
Bias
— (map
)Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
ConfigFile
— (map
)The bias config file for a model.
ContentType
— (String
)The type of content stored in the file source.
ContentDigest
— (String
)The digest of the file source.
S3Uri
— required — (String
)The Amazon S3 URI for the file source.
PreTrainingConstraints
— (map
)The pre-training constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
PostTrainingConstraints
— (map
)The post-training constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Explainability
— (map
)Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Constraints
— (map
)The drift check explainability constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
ConfigFile
— (map
)The explainability config file for the model.
ContentType
— (String
)The type of content stored in the file source.
ContentDigest
— (String
)The digest of the file source.
S3Uri
— required — (String
)The Amazon S3 URI for the file source.
ModelQuality
— (map
)Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Statistics
— (map
)The drift check model quality statistics.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)The drift check model quality constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
ModelDataQuality
— (map
)Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
Statistics
— (map
)The drift check model data quality statistics.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)The drift check model data quality constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
AdditionalInferenceSpecifications
— (Array<map>
)An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
Name
— required — (String
)A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description
— (String
)A description of the additional Inference specification
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
SkipModelValidation
— (String
)Indicates if you want to skip model validation.
Possible values include:"All"
"None"
SourceUri
— (String
)The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.
SecurityConfig
— (map
)The KMS Key ID (
KMSKeyId
) used for encryption of model package information.KmsKeyId
— required — (String
)The KMS Key ID (
KMSKeyId
) used for encryption of model package information.
ModelCard
— (map
)The model card associated with the model package. Since
ModelPackageModelCard
is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema ofModelCard
. TheModelPackageModelCard
schema does not includemodel_package_details
, andmodel_overview
is composed of themodel_creator
andmodel_artifact
properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.ModelCardContent
— (String
)The content of the model card. The content must follow the schema described in Model Package Model Card Schema.
ModelCardStatus
— (String
)The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft
: The model card is a work in progress. -
PendingReview
: The model card is pending review. -
Approved
: The model card is approved. -
Archived
: The model card is archived. No more updates can be made to the model card content. If you try to update the model card content, you will receive the messageModel Card is in Archived state
.
"Draft"
"PendingReview"
"Approved"
"Archived"
-
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageArn
— (String
)The Amazon Resource Name (ARN) of the new model package.
-
(AWS.Response)
—
Returns:
createModelPackageGroup(params = {}, callback) ⇒ AWS.Request
Creates a model group. A model group contains a group of model versions.
Service Reference:
Examples:
Calling the createModelPackageGroup operation
var params = { ModelPackageGroupName: 'STRING_VALUE', /* required */ ModelPackageGroupDescription: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createModelPackageGroup(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageGroupName
— (String
)The name of the model group.
ModelPackageGroupDescription
— (String
)A description for the model group.
Tags
— (Array<map>
)A list of key value pairs associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageGroupArn
— (String
)The Amazon Resource Name (ARN) of the model group.
-
(AWS.Response)
—
Returns:
createModelQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
Service Reference:
Examples:
Calling the createModelQualityJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE', /* required */ JobResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, ModelQualityAppSpecification: { /* required */ ImageUri: 'STRING_VALUE', /* required */ ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ }, PostAnalyticsProcessorSourceUri: 'STRING_VALUE', ProblemType: BinaryClassification | MulticlassClassification | Regression, RecordPreprocessorSourceUri: 'STRING_VALUE' }, ModelQualityJobInput: { /* required */ GroundTruthS3Input: { /* required */ S3Uri: 'STRING_VALUE' }, BatchTransformInput: { DataCapturedDestinationS3Uri: 'STRING_VALUE', /* required */ DatasetFormat: { /* required */ Csv: { Header: true || false }, Json: { Line: true || false }, Parquet: { } }, LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' }, EndpointInput: { EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' } }, ModelQualityJobOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ ModelQualityBaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' } }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createModelQualityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the monitoring job definition.
ModelQualityBaselineConfig
— (map
)Specifies the constraints and baselines for the monitoring job.
BaseliningJobName
— (String
)The name of the job that performs baselining for the monitoring job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
ModelQualityAppSpecification
— (map
)The container that runs the monitoring job.
ImageUri
— required — (String
)The address of the container image that the monitoring job runs.
ContainerEntrypoint
— (Array<String>
)Specifies the entrypoint for a container that the monitoring job runs.
ContainerArguments
— (Array<String>
)An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
ProblemType
— (String
)The machine learning problem type of the model that the monitoring job monitors.
Possible values include:"BinaryClassification"
"MulticlassClassification"
"Regression"
Environment
— (map<String>
)Sets the environment variables in the container that the monitoring job runs.
ModelQualityJobInput
— (map
)A list of the inputs that are monitored. Currently endpoints are supported.
EndpointInput
— (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
GroundTruthS3Input
— required — (map
)The ground truth label provided for the model.
S3Uri
— (String
)The address of the Amazon S3 location of the ground truth labels.
ModelQualityJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
NetworkConfig
— (map
)Specifies the network configuration for the monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Tags
— (Array<map>
)(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the model quality monitoring job.
-
(AWS.Response)
—
Returns:
createMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
Service Reference:
Examples:
Calling the createMonitoringSchedule operation
var params = { MonitoringScheduleConfig: { /* required */ MonitoringJobDefinition: { MonitoringAppSpecification: { /* required */ ImageUri: 'STRING_VALUE', /* required */ ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], PostAnalyticsProcessorSourceUri: 'STRING_VALUE', RecordPreprocessorSourceUri: 'STRING_VALUE' }, MonitoringInputs: [ /* required */ { BatchTransformInput: { DataCapturedDestinationS3Uri: 'STRING_VALUE', /* required */ DatasetFormat: { /* required */ Csv: { Header: true || false }, Json: { Line: true || false }, Parquet: { } }, LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' }, EndpointInput: { EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' } }, /* more items */ ], MonitoringOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, MonitoringResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, RoleArn: 'STRING_VALUE', /* required */ BaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' }, StatisticsResource: { S3Uri: 'STRING_VALUE' } }, Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ } }, MonitoringJobDefinitionName: 'STRING_VALUE', MonitoringType: DataQuality | ModelQuality | ModelBias | ModelExplainability, ScheduleConfig: { ScheduleExpression: 'STRING_VALUE', /* required */ DataAnalysisEndTime: 'STRING_VALUE', DataAnalysisStartTime: 'STRING_VALUE' } }, MonitoringScheduleName: 'STRING_VALUE', /* required */ Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createMonitoringSchedule(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
MonitoringScheduleConfig
— (map
)The configuration object that specifies the monitoring schedule and defines the monitoring job.
ScheduleConfig
— (map
)Configures the monitoring schedule.
ScheduleExpression
— required — (String
)A cron expression that describes details about the monitoring schedule.
The supported cron expressions are:
-
If you want to set the job to start every hour, use the following:
Hourly: cron(0 * ? * * *)
-
If you want to start the job daily:
cron(0 [00-23] ? * * *)
-
If you want to run the job one time, immediately, use the following keyword:
NOW
For example, the following are valid cron expressions:
-
Daily at noon UTC:
cron(0 12 ? * * *)
-
Daily at midnight UTC:
cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
-
Every 12 hours, starting at 5pm UTC:
cron(0 17/12 ? * * *)
-
Every two hours starting at midnight:
cron(0 0/2 ? * * *)
Note:- Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
- We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
You can also specify the keyword
NOW
to run the monitoring job immediately, one time, without recurring.-
DataAnalysisStartTime
— (String
)Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the
ScheduleExpression
parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify:"-PT5H"
.The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the
DataAnalysisEndTime
parameter.If you set
ScheduleExpression
toNOW
, this parameter is required.DataAnalysisEndTime
— (String
)Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the
ScheduleExpression
parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify:"-PT1H"
.The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the
DataAnalysisStartTime
parameter.If you set
ScheduleExpression
toNOW
, this parameter is required.
MonitoringJobDefinition
— (map
)Defines the monitoring job.
BaselineConfig
— (map
)Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName
— (String
)The name of the job that performs baselining for the monitoring job.
ConstraintsResource
— (map
)The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
StatisticsResource
— (map
)The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri
— (String
)The Amazon S3 URI for the statistics resource.
MonitoringInputs
— required — (Array<map>
)The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
EndpointInput
— (map
)The endpoint for a monitoring job.
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
MonitoringOutputConfig
— required — (map
)The array of outputs from the monitoring job to be uploaded to Amazon S3.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources
— required — (map
)Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification
— required — (map
)Configures the monitoring job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the monitoring job.
ContainerEntrypoint
— (Array<String>
)Specifies the entrypoint for a container used to run the monitoring job.
ContainerArguments
— (Array<String>
)An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition
— (map
)Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
NetworkConfig
— (map
)Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job definition to schedule.
MonitoringType
— (String
)The type of the monitoring job definition to schedule.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
Tags
— (Array<map>
)(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:MonitoringScheduleArn
— (String
)The Amazon Resource Name (ARN) of the monitoring schedule.
-
(AWS.Response)
—
Returns:
createNotebookInstance(params = {}, callback) ⇒ AWS.Request
Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
In a
CreateNotebookInstance
request, specify the type of ML compute instance that you want to run. SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, SageMaker does the following:
-
Creates a network interface in the SageMaker VPC.
-
(Option) If you specified
SubnetId
, SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC. -
Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified
SubnetId
of your VPC, SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.
After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
Service Reference:
Examples:
Calling the createNotebookInstance operation
var params = { InstanceType: ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge, /* required */ NotebookInstanceName: 'STRING_VALUE', /* required */ RoleArn: 'STRING_VALUE', /* required */ AcceleratorTypes: [ ml.eia1.medium | ml.eia1.large | ml.eia1.xlarge | ml.eia2.medium | ml.eia2.large | ml.eia2.xlarge, /* more items */ ], AdditionalCodeRepositories: [ 'STRING_VALUE', /* more items */ ], DefaultCodeRepository: 'STRING_VALUE', DirectInternetAccess: Enabled | Disabled, InstanceMetadataServiceConfiguration: { MinimumInstanceMetadataServiceVersion: 'STRING_VALUE' /* required */ }, KmsKeyId: 'STRING_VALUE', LifecycleConfigName: 'STRING_VALUE', PlatformIdentifier: 'STRING_VALUE', RootAccess: Enabled | Disabled, SecurityGroupIds: [ 'STRING_VALUE', /* more items */ ], SubnetId: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], VolumeSizeInGB: 'NUMBER_VALUE' }; sagemaker.createNotebookInstance(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceName
— (String
)The name of the new notebook instance.
InstanceType
— (String
)The type of ML compute instance to launch for the notebook instance.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
SubnetId
— (String
)The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.
SecurityGroupIds
— (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
RoleArn
— (String
)When you send any requests to Amazon Web Services resources from the notebook instance, SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so SageMaker can perform these tasks. The policy must allow the SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see SageMaker Roles.
Note: To be able to pass this role to SageMaker, the caller of this API must have theiam:PassRole
permission.KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
LifecycleConfigName
— (String
)The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
DirectInternetAccess
— (String
)Sets whether SageMaker provides internet access to the notebook instance. If you set this to
Disabled
this notebook instance is able to access resources only in your VPC, and is not be able to connect to SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC.For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to
Possible values include:Disabled
only if you set a value for theSubnetId
parameter."Enabled"
"Disabled"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.
AcceleratorTypes
— (Array<String>
)A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
DefaultCodeRepository
— (String
)A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
AdditionalCodeRepositories
— (Array<String>
)An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
RootAccess
— (String
)Whether root access is enabled or disabled for users of the notebook instance. The default value is
Enabled
.Note: Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.Possible values include:"Enabled"
"Disabled"
PlatformIdentifier
— (String
)The platform identifier of the notebook instance runtime environment.
InstanceMetadataServiceConfiguration
— (map
)Information on the IMDS configuration of the notebook instance
MinimumInstanceMetadataServiceVersion
— required — (String
)Indicates the minimum IMDS version that the notebook instance supports. When passed as part of
CreateNotebookInstance
, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part ofUpdateNotebookInstance
, there is no default.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NotebookInstanceArn
— (String
)The Amazon Resource Name (ARN) of the notebook instance.
-
(AWS.Response)
—
Returns:
createNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the
$PATH
environment variable that is available to both scripts is/sbin:bin:/usr/sbin:/usr/bin
.View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group
/aws/sagemaker/NotebookInstances
in log stream[notebook-instance-name]/[LifecycleConfigHook]
.Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
Service Reference:
Examples:
Calling the createNotebookInstanceLifecycleConfig operation
var params = { NotebookInstanceLifecycleConfigName: 'STRING_VALUE', /* required */ OnCreate: [ { Content: 'STRING_VALUE' }, /* more items */ ], OnStart: [ { Content: 'STRING_VALUE' }, /* more items */ ] }; sagemaker.createNotebookInstanceLifecycleConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceLifecycleConfigName
— (String
)The name of the lifecycle configuration.
OnCreate
— (Array<map>
)A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
Content
— (String
)A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
OnStart
— (Array<map>
)A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
Content
— (String
)A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NotebookInstanceLifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the lifecycle configuration.
-
(AWS.Response)
—
Returns:
createOptimizationJob(params = {}, callback) ⇒ AWS.Request
Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify.
For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
Service Reference:
Examples:
Calling the createOptimizationJob operation
var params = { DeploymentInstanceType: ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.inf2.xlarge | ml.inf2.8xlarge | ml.inf2.24xlarge | ml.inf2.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge, /* required */ ModelSource: { /* required */ S3: { ModelAccessConfig: { AcceptEula: true || false /* required */ }, S3Uri: 'STRING_VALUE' } }, OptimizationConfigs: [ /* required */ { ModelCompilationConfig: { Image: 'STRING_VALUE', OverrideEnvironment: { '<NonEmptyString256>': 'STRING_VALUE', /* '<NonEmptyString256>': ... */ } }, ModelQuantizationConfig: { Image: 'STRING_VALUE', OverrideEnvironment: { '<NonEmptyString256>': 'STRING_VALUE', /* '<NonEmptyString256>': ... */ } } }, /* more items */ ], OptimizationJobName: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputLocation: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ StoppingCondition: { /* required */ MaxPendingTimeInSeconds: 'NUMBER_VALUE', MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, OptimizationEnvironment: { '<NonEmptyString256>': 'STRING_VALUE', /* '<NonEmptyString256>': ... */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }; sagemaker.createOptimizationJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
OptimizationJobName
— (String
)A custom name for the new optimization job.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
During model optimization, Amazon SageMaker needs your permission to:
-
Read input data from an S3 bucket
-
Write model artifacts to an S3 bucket
-
Write logs to Amazon CloudWatch Logs
-
Publish metrics to Amazon CloudWatch
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the
iam:PassRole
permission. For more information, see Amazon SageMaker Roles.-
ModelSource
— (map
)The location of the source model to optimize with an optimization job.
S3
— (map
)The Amazon S3 location of a source model to optimize with an optimization job.
S3Uri
— (String
)An Amazon S3 URI that locates a source model to optimize with an optimization job.
ModelAccessConfig
— (map
)The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).
AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
DeploymentInstanceType
— (String
)The type of instance that hosts the optimized model that you create with the optimization job.
Possible values include:"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
OptimizationEnvironment
— (map<String>
)The environment variables to set in the model container.
OptimizationConfigs
— (Array<map>
)Settings for each of the optimization techniques that the job applies.
ModelQuantizationConfig
— (map
)Settings for the model quantization technique that's applied by a model optimization job.
Image
— (String
)The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
OverrideEnvironment
— (map<String>
)Environment variables that override the default ones in the model container.
ModelCompilationConfig
— (map
)Settings for the model compilation technique that's applied by a model optimization job.
Image
— (String
)The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
OverrideEnvironment
— (map<String>
)Environment variables that override the default ones in the model container.
OutputConfig
— (map
)Details for where to store the optimized model that you create with the optimization job.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a key in Amazon Web Services KMS. SageMaker uses they key to encrypt the artifacts of the optimized model when SageMaker uploads the model to Amazon S3.
S3OutputLocation
— required — (String
)The Amazon S3 URI for where to store the optimized model that you create with an optimization job.
StoppingCondition
— (map
)Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.
To stop a training job, SageMaker sends the algorithm the
SIGTERM
signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with
CreateModel
.Note: The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
Tags
— (Array<map>
)A list of key-value pairs associated with the optimization job. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
VpcConfig
— (map
)A VPC in Amazon VPC that your optimized model has access to.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your optimized model.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:OptimizationJobArn
— (String
)The Amazon Resource Name (ARN) of the optimization job.
-
(AWS.Response)
—
Returns:
createPipeline(params = {}, callback) ⇒ AWS.Request
Creates a pipeline using a JSON pipeline definition.
Service Reference:
Examples:
Calling the createPipeline operation
var params = { ClientRequestToken: 'STRING_VALUE', /* required */ PipelineName: 'STRING_VALUE', /* required */ RoleArn: 'STRING_VALUE', /* required */ ParallelismConfiguration: { MaxParallelExecutionSteps: 'NUMBER_VALUE' /* required */ }, PipelineDefinition: 'STRING_VALUE', PipelineDefinitionS3Location: { Bucket: 'STRING_VALUE', /* required */ ObjectKey: 'STRING_VALUE', /* required */ VersionId: 'STRING_VALUE' }, PipelineDescription: 'STRING_VALUE', PipelineDisplayName: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createPipeline(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineName
— (String
)The name of the pipeline.
PipelineDisplayName
— (String
)The display name of the pipeline.
PipelineDefinition
— (String
)The JSON pipeline definition of the pipeline.
PipelineDefinitionS3Location
— (map
)The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.
Bucket
— required — (String
)Name of the S3 bucket.
ObjectKey
— required — (String
)The object key (or key name) uniquely identifies the object in an S3 bucket.
VersionId
— (String
)Version Id of the pipeline definition file. If not specified, Amazon SageMaker will retrieve the latest version.
PipelineDescription
— (String
)A description of the pipeline.
ClientRequestToken
— (String
)A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
If a token is not provided, the SDK will use a version 4 UUID.RoleArn
— (String
)The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.
Tags
— (Array<map>
)A list of tags to apply to the created pipeline.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ParallelismConfiguration
— (map
)This is the configuration that controls the parallelism of the pipeline. If specified, it applies to all runs of this pipeline by default.
MaxParallelExecutionSteps
— required — (Integer
)The max number of steps that can be executed in parallel.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineArn
— (String
)The Amazon Resource Name (ARN) of the created pipeline.
-
(AWS.Response)
—
Returns:
createPresignedDomainUrl(params = {}, callback) ⇒ AWS.Request
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM.
The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.
You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker Studio Through an Interface VPC Endpoint .
Note: The URL that you get from a call toCreatePresignedDomainUrl
has a default timeout of 5 minutes. You can configure this value usingExpiresInSeconds
. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.Service Reference:
Examples:
Calling the createPresignedDomainUrl operation
var params = { DomainId: 'STRING_VALUE', /* required */ UserProfileName: 'STRING_VALUE', /* required */ ExpiresInSeconds: 'NUMBER_VALUE', LandingUri: 'STRING_VALUE', SessionExpirationDurationInSeconds: 'NUMBER_VALUE', SpaceName: 'STRING_VALUE' }; sagemaker.createPresignedDomainUrl(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The name of the UserProfile to sign-in as.
SessionExpirationDurationInSeconds
— (Integer
)The session expiration duration in seconds. This value defaults to 43200.
ExpiresInSeconds
— (Integer
)The number of seconds until the pre-signed URL expires. This value defaults to 300.
SpaceName
— (String
)The name of the space.
LandingUri
— (String
)The landing page that the user is directed to when accessing the presigned URL. Using this value, users can access Studio or Studio Classic, even if it is not the default experience for the domain. The supported values are:
-
studio::relative/path
: Directs users to the relative path in Studio. -
app:JupyterServer:relative/path
: Directs users to the relative path in the Studio Classic application. -
app:JupyterLab:relative/path
: Directs users to the relative path in the JupyterLab application. -
app:RStudioServerPro:relative/path
: Directs users to the relative path in the RStudio application. -
app:CodeEditor:relative/path
: Directs users to the relative path in the Code Editor, based on Code-OSS, Visual Studio Code - Open Source application. -
app:Canvas:relative/path
: Directs users to the relative path in the Canvas application.
-
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AuthorizedUrl
— (String
)The presigned URL.
-
(AWS.Response)
—
Returns:
createPresignedMlflowTrackingServerUrl(params = {}, callback) ⇒ AWS.Request
Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.
Service Reference:
Examples:
Calling the createPresignedMlflowTrackingServerUrl operation
var params = { TrackingServerName: 'STRING_VALUE', /* required */ ExpiresInSeconds: 'NUMBER_VALUE', SessionExpirationDurationInSeconds: 'NUMBER_VALUE' }; sagemaker.createPresignedMlflowTrackingServerUrl(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrackingServerName
— (String
)The name of the tracking server to connect to your MLflow UI.
ExpiresInSeconds
— (Integer
)The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.
SessionExpirationDurationInSeconds
— (Integer
)The duration in seconds that your MLflow UI session is valid.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AuthorizedUrl
— (String
)A presigned URL with an authorization token.
-
(AWS.Response)
—
Returns:
createPresignedNotebookInstanceUrl(params = {}, callback) ⇒ AWS.Request
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose
Open
next to a notebook instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the
NotIpAddress
condition operator and theaws:SourceIP
condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.Note: The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.Service Reference:
Examples:
Calling the createPresignedNotebookInstanceUrl operation
var params = { NotebookInstanceName: 'STRING_VALUE', /* required */ SessionExpirationDurationInSeconds: 'NUMBER_VALUE' }; sagemaker.createPresignedNotebookInstanceUrl(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceName
— (String
)The name of the notebook instance.
SessionExpirationDurationInSeconds
— (Integer
)The duration of the session, in seconds. The default is 12 hours.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AuthorizedUrl
— (String
)A JSON object that contains the URL string.
-
(AWS.Response)
—
Returns:
createProcessingJob(params = {}, callback) ⇒ AWS.Request
Creates a processing job.
Service Reference:
Examples:
Calling the createProcessingJob operation
var params = { AppSpecification: { /* required */ ImageUri: 'STRING_VALUE', /* required */ ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ] }, ProcessingJobName: 'STRING_VALUE', /* required */ ProcessingResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, RoleArn: 'STRING_VALUE', /* required */ Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ }, ExperimentConfig: { ExperimentName: 'STRING_VALUE', RunName: 'STRING_VALUE', TrialComponentDisplayName: 'STRING_VALUE', TrialName: 'STRING_VALUE' }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, ProcessingInputs: [ { InputName: 'STRING_VALUE', /* required */ AppManaged: true || false, DatasetDefinition: { AthenaDatasetDefinition: { Catalog: 'STRING_VALUE', /* required */ Database: 'STRING_VALUE', /* required */ OutputFormat: PARQUET | ORC | AVRO | JSON | TEXTFILE, /* required */ OutputS3Uri: 'STRING_VALUE', /* required */ QueryString: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', OutputCompression: GZIP | SNAPPY | ZLIB, WorkGroup: 'STRING_VALUE' }, DataDistributionType: FullyReplicated | ShardedByS3Key, InputMode: Pipe | File, LocalPath: 'STRING_VALUE', RedshiftDatasetDefinition: { ClusterId: 'STRING_VALUE', /* required */ ClusterRoleArn: 'STRING_VALUE', /* required */ Database: 'STRING_VALUE', /* required */ DbUser: 'STRING_VALUE', /* required */ OutputFormat: PARQUET | CSV, /* required */ OutputS3Uri: 'STRING_VALUE', /* required */ QueryString: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', OutputCompression: None | GZIP | BZIP2 | ZSTD | SNAPPY } }, S3Input: { S3DataType: ManifestFile | S3Prefix, /* required */ S3Uri: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', S3CompressionType: None | Gzip, S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File } }, /* more items */ ], ProcessingOutputConfig: { Outputs: [ /* required */ { OutputName: 'STRING_VALUE', /* required */ AppManaged: true || false, FeatureStoreOutput: { FeatureGroupName: 'STRING_VALUE' /* required */ }, S3Output: { S3UploadMode: Continuous | EndOfJob, /* required */ S3Uri: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE' } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createProcessingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ProcessingInputs
— (Array<map>
)An array of inputs configuring the data to download into the processing container.
InputName
— required — (String
)The name for the processing job input.
AppManaged
— (Boolean
)When
True
, input operations such as data download are managed natively by the processing job application. WhenFalse
(default), input operations are managed by Amazon SageMaker.S3Input
— (map
)Configuration for downloading input data from Amazon S3 into the processing container.
S3Uri
— required — (String
)The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
LocalPath
— (String
)The local path in your container where you want Amazon SageMaker to write input data to.
LocalPath
is an absolute path to the input data and must begin with/opt/ml/processing/
.LocalPath
is a required parameter whenAppManaged
isFalse
(default).S3DataType
— required — (String
)Whether you use an
Possible values include:S3Prefix
or aManifestFile
for the data type. If you chooseS3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you chooseManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job."ManifestFile"
"S3Prefix"
S3InputMode
— (String
)Whether to use
Possible values include:File
orPipe
input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. InPipe
mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume."Pipe"
"File"
S3DataDistributionType
— (String
)Whether to distribute the data from Amazon S3 to all processing instances with
Possible values include:FullyReplicated
, or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance."FullyReplicated"
"ShardedByS3Key"
S3CompressionType
— (String
)Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container.
Possible values include:Gzip
can only be used whenPipe
mode is specified as theS3InputMode
. InPipe
mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume."None"
"Gzip"
DatasetDefinition
— (map
)Configuration for a Dataset Definition input.
AthenaDatasetDefinition
— (map
)Configuration for Athena Dataset Definition input.
Catalog
— required — (String
)The name of the data catalog used in Athena query execution.
Database
— required — (String
)The name of the database used in the Athena query execution.
QueryString
— required — (String
)The SQL query statements, to be executed.
WorkGroup
— (String
)The name of the workgroup in which the Athena query is being started.
OutputS3Uri
— required — (String
)The location in Amazon S3 where Athena query results are stored.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat
— required — (String
)The data storage format for Athena query results.
Possible values include:"PARQUET"
"ORC"
"AVRO"
"JSON"
"TEXTFILE"
OutputCompression
— (String
)The compression used for Athena query results.
Possible values include:"GZIP"
"SNAPPY"
"ZLIB"
RedshiftDatasetDefinition
— (map
)Configuration for Redshift Dataset Definition input.
ClusterId
— required — (String
)The Redshift cluster Identifier.
Database
— required — (String
)The name of the Redshift database used in Redshift query execution.
DbUser
— required — (String
)The database user name used in Redshift query execution.
QueryString
— required — (String
)The SQL query statements to be executed.
ClusterRoleArn
— required — (String
)The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri
— required — (String
)The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat
— required — (String
)The data storage format for Redshift query results.
Possible values include:"PARQUET"
"CSV"
OutputCompression
— (String
)The compression used for Redshift query results.
Possible values include:"None"
"GZIP"
"BZIP2"
"ZSTD"
"SNAPPY"
LocalPath
— (String
)The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job.
LocalPath
is an absolute path to the input data. This is a required parameter whenAppManaged
isFalse
(default).DataDistributionType
— (String
)Whether the generated dataset is
Possible values include:FullyReplicated
orShardedByS3Key
(default)."FullyReplicated"
"ShardedByS3Key"
InputMode
— (String
)Whether to use
Possible values include:File
orPipe
input mode. InFile
(default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. InPipe
mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume."Pipe"
"File"
ProcessingOutputConfig
— (map
)Output configuration for the processing job.
Outputs
— required — (Array<map>
)An array of outputs configuring the data to upload from the processing container.
OutputName
— required — (String
)The name for the processing job output.
S3Output
— (map
)Configuration for processing job outputs in Amazon S3.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath
— (String
)The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3.
LocalPath
is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.S3UploadMode
— required — (String
)Whether to upload the results of the processing job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
FeatureStoreOutput
— (map
)Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when
AppManaged
is specified.FeatureGroupName
— required — (String
)The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
AppManaged
— (Boolean
)When
True
, output operations such as data upload are managed natively by the processing job application. WhenFalse
(default), output operations are managed by Amazon SageMaker.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output.
KmsKeyId
can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. TheKmsKeyId
is applied to all outputs.
ProcessingJobName
— (String
)The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ProcessingResources
— (map
)Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig
— required — (map
)The configuration for the resources in a cluster used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request aVolumeSizeInGB
greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.
StoppingCondition
— (map
)The time limit for how long the processing job is allowed to run.
MaxRuntimeInSeconds
— required — (Integer
)Specifies the maximum runtime in seconds.
AppSpecification
— (map
)Configures the processing job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the processing job.
ContainerEntrypoint
— (Array<String>
)The entrypoint for a container used to run a processing job.
ContainerArguments
— (Array<String>
)The arguments for a container used to run a processing job.
Environment
— (map<String>
)The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.
NetworkConfig
— (map
)Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
Tags
— (Array<map>
)(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ProcessingJobArn
— (String
)The Amazon Resource Name (ARN) of the processing job.
-
(AWS.Response)
—
Returns:
createProject(params = {}, callback) ⇒ AWS.Request
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
Service Reference:
Examples:
Calling the createProject operation
var params = { ProjectName: 'STRING_VALUE', /* required */ ServiceCatalogProvisioningDetails: { /* required */ ProductId: 'STRING_VALUE', /* required */ PathId: 'STRING_VALUE', ProvisioningArtifactId: 'STRING_VALUE', ProvisioningParameters: [ { Key: 'STRING_VALUE', Value: 'STRING_VALUE' }, /* more items */ ] }, ProjectDescription: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createProject(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ProjectName
— (String
)The name of the project.
ProjectDescription
— (String
)A description for the project.
ServiceCatalogProvisioningDetails
— (map
)The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.
ProductId
— required — (String
)The ID of the product to provision.
ProvisioningArtifactId
— (String
)The ID of the provisioning artifact.
PathId
— (String
)The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
ProvisioningParameters
— (Array<map>
)A list of key value pairs that you specify when you provision a product.
Key
— (String
)The key that identifies a provisioning parameter.
Value
— (String
)The value of the provisioning parameter.
Tags
— (Array<map>
)An array of key-value pairs that you want to use to organize and track your Amazon Web Services resource costs. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ProjectArn
— (String
)The Amazon Resource Name (ARN) of the project.
ProjectId
— (String
)The ID of the new project.
-
(AWS.Response)
—
Returns:
createSpace(params = {}, callback) ⇒ AWS.Request
Creates a private space or a space used for real time collaboration in a domain.
Service Reference:
Examples:
Calling the createSpace operation
var params = { DomainId: 'STRING_VALUE', /* required */ SpaceName: 'STRING_VALUE', /* required */ OwnershipSettings: { OwnerUserProfileName: 'STRING_VALUE' /* required */ }, SpaceDisplayName: 'STRING_VALUE', SpaceSettings: { AppType: JupyterServer | KernelGateway | DetailedProfiler | TensorBoard | CodeEditor | JupyterLab | RStudioServerPro | RSessionGateway | Canvas, CodeEditorAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE' } }, DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } }, CustomFileSystems: [ { EFSFileSystem: { FileSystemId: 'STRING_VALUE' /* required */ } }, /* more items */ ], JupyterLabAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } }, JupyterServerAppSettings: { CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, KernelGatewayAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, SpaceStorageSettings: { EbsStorageSettings: { EbsVolumeSizeInGb: 'NUMBER_VALUE' /* required */ } } }, SpaceSharingSettings: { SharingType: Private | Shared /* required */ }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createSpace(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The ID of the associated domain.
SpaceName
— (String
)The name of the space.
Tags
— (Array<map>
)Tags to associated with the space. Each tag consists of a key and an optional value. Tag keys must be unique for each resource. Tags are searchable using the
Search
API.Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
SpaceSettings
— (map
)A collection of space settings.
JupyterServerAppSettings
— (map
)The JupyterServer app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The KernelGateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
CodeEditorAppSettings
— (map
)The Code Editor application settings.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of CodeEditor applications in a space.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of JupyterLab applications in a space.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
AppType
— (String
)The type of app created within the space.
Possible values include:"JupyterServer"
"KernelGateway"
"DetailedProfiler"
"TensorBoard"
"CodeEditor"
"JupyterLab"
"RStudioServerPro"
"RSessionGateway"
"Canvas"
SpaceStorageSettings
— (map
)The storage settings for a space.
EbsStorageSettings
— (map
)A collection of EBS storage settings for a space.
EbsVolumeSizeInGb
— required — (Integer
)The size of an EBS storage volume for a space.
CustomFileSystems
— (Array<map>
)A file system, created by you, that you assign to a space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystem
— (map
)A custom file system in Amazon EFS.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
OwnershipSettings
— (map
)A collection of ownership settings.
OwnerUserProfileName
— required — (String
)The user profile who is the owner of the space.
SpaceSharingSettings
— (map
)A collection of space sharing settings.
SharingType
— required — (String
)Specifies the sharing type of the space.
Possible values include:"Private"
"Shared"
SpaceDisplayName
— (String
)The name of the space that appears in the SageMaker Studio UI.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:SpaceArn
— (String
)The space's Amazon Resource Name (ARN).
-
(AWS.Response)
—
Returns:
createStudioLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Creates a new Amazon SageMaker Studio Lifecycle Configuration.
Service Reference:
Examples:
Calling the createStudioLifecycleConfig operation
var params = { StudioLifecycleConfigAppType: JupyterServer | KernelGateway | CodeEditor | JupyterLab, /* required */ StudioLifecycleConfigContent: 'STRING_VALUE', /* required */ StudioLifecycleConfigName: 'STRING_VALUE', /* required */ Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createStudioLifecycleConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
StudioLifecycleConfigName
— (String
)The name of the Amazon SageMaker Studio Lifecycle Configuration to create.
StudioLifecycleConfigContent
— (String
)The content of your Amazon SageMaker Studio Lifecycle Configuration script. This content must be base64 encoded.
StudioLifecycleConfigAppType
— (String
)The App type that the Lifecycle Configuration is attached to.
Possible values include:"JupyterServer"
"KernelGateway"
"CodeEditor"
"JupyterLab"
Tags
— (Array<map>
)Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:StudioLifecycleConfigArn
— (String
)The ARN of your created Lifecycle Configuration.
-
(AWS.Response)
—
Returns:
createTrainingJob(params = {}, callback) ⇒ AWS.Request
Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.
In the request body, you provide the following:
-
AlgorithmSpecification
- Identifies the training algorithm to use. -
HyperParameters
- Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
-
InputDataConfig
- Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored. -
OutputDataConfig
- Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training. -
ResourceConfig
- Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. -
EnableManagedSpotTraining
- Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training. -
RoleArn
- The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training. -
StoppingCondition
- To help cap training costs, useMaxRuntimeInSeconds
to set a time limit for training. UseMaxWaitTimeInSeconds
to specify how long a managed spot training job has to complete. -
Environment
- The environment variables to set in the Docker container. -
RetryStrategy
- The number of times to retry the job when the job fails due to anInternalServerError
.
For more information about SageMaker, see How It Works.
Service Reference:
Examples:
Calling the createTrainingJob operation
var params = { AlgorithmSpecification: { /* required */ TrainingInputMode: Pipe | File | FastFile, /* required */ AlgorithmName: 'STRING_VALUE', ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], EnableSageMakerMetricsTimeSeries: true || false, MetricDefinitions: [ { Name: 'STRING_VALUE', /* required */ Regex: 'STRING_VALUE' /* required */ }, /* more items */ ], TrainingImage: 'STRING_VALUE', TrainingImageConfig: { TrainingRepositoryAccessMode: Platform | Vpc, /* required */ TrainingRepositoryAuthConfig: { TrainingRepositoryCredentialsProviderArn: 'STRING_VALUE' /* required */ } } }, OutputDataConfig: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ CompressionType: GZIP | NONE, KmsKeyId: 'STRING_VALUE' }, ResourceConfig: { /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ InstanceCount: 'NUMBER_VALUE', InstanceGroups: [ { InstanceCount: 'NUMBER_VALUE', /* required */ InstanceGroupName: 'STRING_VALUE', /* required */ InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge /* required */ }, /* more items */ ], InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5n.xlarge | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.8xlarge | ml.c6i.4xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge, KeepAlivePeriodInSeconds: 'NUMBER_VALUE', VolumeKmsKeyId: 'STRING_VALUE' }, RoleArn: 'STRING_VALUE', /* required */ StoppingCondition: { /* required */ MaxPendingTimeInSeconds: 'NUMBER_VALUE', MaxRuntimeInSeconds: 'NUMBER_VALUE', MaxWaitTimeInSeconds: 'NUMBER_VALUE' }, TrainingJobName: 'STRING_VALUE', /* required */ CheckpointConfig: { S3Uri: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE' }, DebugHookConfig: { S3OutputPath: 'STRING_VALUE', /* required */ CollectionConfigurations: [ { CollectionName: 'STRING_VALUE', CollectionParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ } }, /* more items */ ], HookParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ }, LocalPath: 'STRING_VALUE' }, DebugRuleConfigurations: [ { RuleConfigurationName: 'STRING_VALUE', /* required */ RuleEvaluatorImage: 'STRING_VALUE', /* required */ InstanceType: ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge, LocalPath: 'STRING_VALUE', RuleParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ }, S3OutputPath: 'STRING_VALUE', VolumeSizeInGB: 'NUMBER_VALUE' }, /* more items */ ], EnableInterContainerTrafficEncryption: true || false, EnableManagedSpotTraining: true || false, EnableNetworkIsolation: true || false, Environment: { '<TrainingEnvironmentKey>': 'STRING_VALUE', /* '<TrainingEnvironmentKey>': ... */ }, ExperimentConfig: { ExperimentName: 'STRING_VALUE', RunName: 'STRING_VALUE', TrialComponentDisplayName: 'STRING_VALUE', TrialName: 'STRING_VALUE' }, HyperParameters: { '<HyperParameterKey>': 'STRING_VALUE', /* '<HyperParameterKey>': ... */ }, InfraCheckConfig: { EnableInfraCheck: true || false }, InputDataConfig: [ { ChannelName: 'STRING_VALUE', /* required */ DataSource: { /* required */ FileSystemDataSource: { DirectoryPath: 'STRING_VALUE', /* required */ FileSystemAccessMode: rw | ro, /* required */ FileSystemId: 'STRING_VALUE', /* required */ FileSystemType: EFS | FSxLustre /* required */ }, S3DataSource: { S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE', /* required */ AttributeNames: [ 'STRING_VALUE', /* more items */ ], InstanceGroupNames: [ 'STRING_VALUE', /* more items */ ], S3DataDistributionType: FullyReplicated | ShardedByS3Key } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', InputMode: Pipe | File | FastFile, RecordWrapperType: None | RecordIO, ShuffleConfig: { Seed: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], ProfilerConfig: { DisableProfiler: true || false, ProfilingIntervalInMilliseconds: 'NUMBER_VALUE', ProfilingParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ }, S3OutputPath: 'STRING_VALUE' }, ProfilerRuleConfigurations: [ { RuleConfigurationName: 'STRING_VALUE', /* required */ RuleEvaluatorImage: 'STRING_VALUE', /* required */ InstanceType: ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge, LocalPath: 'STRING_VALUE', RuleParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ }, S3OutputPath: 'STRING_VALUE', VolumeSizeInGB: 'NUMBER_VALUE' }, /* more items */ ], RemoteDebugConfig: { EnableRemoteDebug: true || false }, RetryStrategy: { MaximumRetryAttempts: 'NUMBER_VALUE' /* required */ }, SessionChainingConfig: { EnableSessionTagChaining: true || false }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], TensorBoardOutputConfig: { S3OutputPath: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE' }, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }; sagemaker.createTrainingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrainingJobName
— (String
)The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
HyperParameters
— (map<String>
)Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.
You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the
Length Constraint
.Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
AlgorithmSpecification
— (map
)The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. For more information, see the note in theAlgorithmName
parameter description.AlgorithmName
— (String
)The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. Note that theAlgorithmName
parameter is mutually exclusive with theTrainingImage
parameter. If you specify a value for theAlgorithmName
parameter, you can't specify a value forTrainingImage
, and vice versa. If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise anull
error.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
MetricDefinitions
— (Array<map>
)A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
EnableSageMakerMetricsTimeSeries
— (Boolean
)To generate and save time-series metrics during training, set to
true
. The default isfalse
and time-series metrics aren't generated except in the following cases:-
You use one of the SageMaker built-in algorithms
-
You use one of the following Prebuilt SageMaker Docker Images:
-
Tensorflow (version >= 1.15)
-
MXNet (version >= 1.6)
-
PyTorch (version >= 1.3)
-
-
You specify at least one MetricDefinition
-
ContainerEntrypoint
— (Array<String>
)The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
ContainerArguments
— (Array<String>
)The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
TrainingImageConfig
— (map
)The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode
— required — (String
)The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to
Possible values include:Vpc
."Platform"
"Vpc"
TrainingRepositoryAuthConfig
— (map
)An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.
During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see SageMaker Roles.
Note: To be able to pass this role to SageMaker, the caller of this API must have theiam:PassRole
permission.InputDataConfig
— (Array<map>
)An array of
Channel
objects. Each channel is a named input source.InputDataConfig
describes the input data and its location.Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data,
training_data
andvalidation_data
. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.Depending on the input mode that the algorithm supports, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.
Your input must be in the same Amazon Web Services region as your training job.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
OutputDataConfig
— (map
)Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
VpcConfig
— (map
)A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
StoppingCondition
— (map
)Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the
SIGTERM
signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
EnableNetworkIsolation
— (Boolean
)Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.EnableManagedSpotTraining
— (Boolean
)To train models using managed spot training, choose
True
. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
DebugHookConfig
— (map
)Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the
DebugHookConfig
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.LocalPath
— (String
)Path to local storage location for metrics and tensors. Defaults to
/opt/ml/output/tensors/
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for metrics and tensors.
HookParameters
— (map<String>
)Configuration information for the Amazon SageMaker Debugger hook parameters.
CollectionConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the
CollectionConfiguration
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.CollectionName
— (String
)The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters
— (map<String>
)Parameter values for the tensor collection. The allowed parameters are
"name"
,"include_regex"
,"reduction_config"
,"save_config"
,"tensor_names"
, and"save_histogram"
.
DebugRuleConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a custom rule for debugging a training job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
TensorBoardOutputConfig
— (map
)Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath
— (String
)Path to local storage location for tensorBoard output. Defaults to
/opt/ml/output/tensorboard
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for TensorBoard output.
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
ProfilerConfig
— (map
)Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath
— (String
)Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds
— (Integer
)A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters
— (map<String>
)Configuration information for capturing framework metrics. Available key strings for different profiling options are
DetailedProfilingConfig
,PythonProfilingConfig
, andDataLoaderProfilingConfig
. The following codes are configuration structures for theProfilingParameters
parameter. To learn more about how to configure theProfilingParameters
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.DisableProfiler
— (Boolean
)Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to
True
.
ProfilerRuleConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container Registry Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a custom rule for profiling a training job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
Environment
— (map<String>
)The environment variables to set in the Docker container.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
RemoteDebugConfig
— (map
)Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
EnableRemoteDebug
— (Boolean
)If set to True, enables remote debugging.
InfraCheckConfig
— (map
)Contains information about the infrastructure health check configuration for the training job.
EnableInfraCheck
— (Boolean
)Enables an infrastructure health check.
SessionChainingConfig
— (map
)Contains information about attribute-based access control (ABAC) for the training job.
EnableSessionTagChaining
— (Boolean
)Set to
True
to allow SageMaker to extract session tags from a training job creation role and reuse these tags when assuming the training job execution role.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrainingJobArn
— (String
)The Amazon Resource Name (ARN) of the training job.
-
(AWS.Response)
—
Returns:
createTransformJob(params = {}, callback) ⇒ AWS.Request
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
To perform batch transformations, you create a transform job and use the data that you have readily available.
In the request body, you provide the following:
-
TransformJobName
- Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. -
ModelName
- Identifies the model to use.ModelName
must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel. -
TransformInput
- Describes the dataset to be transformed and the Amazon S3 location where it is stored. -
TransformOutput
- Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. -
TransformResources
- Identifies the ML compute instances for the transform job.
For more information about how batch transformation works, see Batch Transform.
Service Reference:
Examples:
Calling the createTransformJob operation
var params = { ModelName: 'STRING_VALUE', /* required */ TransformInput: { /* required */ DataSource: { /* required */ S3DataSource: { /* required */ S3DataType: ManifestFile | S3Prefix | AugmentedManifestFile, /* required */ S3Uri: 'STRING_VALUE' /* required */ } }, CompressionType: None | Gzip, ContentType: 'STRING_VALUE', SplitType: None | Line | RecordIO | TFRecord }, TransformJobName: 'STRING_VALUE', /* required */ TransformOutput: { /* required */ S3OutputPath: 'STRING_VALUE', /* required */ Accept: 'STRING_VALUE', AssembleWith: None | Line, KmsKeyId: 'STRING_VALUE' }, TransformResources: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge, /* required */ VolumeKmsKeyId: 'STRING_VALUE' }, BatchStrategy: MultiRecord | SingleRecord, DataCaptureConfig: { DestinationS3Uri: 'STRING_VALUE', /* required */ GenerateInferenceId: true || false, KmsKeyId: 'STRING_VALUE' }, DataProcessing: { InputFilter: 'STRING_VALUE', JoinSource: Input | None, OutputFilter: 'STRING_VALUE' }, Environment: { '<TransformEnvironmentKey>': 'STRING_VALUE', /* '<TransformEnvironmentKey>': ... */ }, ExperimentConfig: { ExperimentName: 'STRING_VALUE', RunName: 'STRING_VALUE', TrialComponentDisplayName: 'STRING_VALUE', TrialName: 'STRING_VALUE' }, MaxConcurrentTransforms: 'NUMBER_VALUE', MaxPayloadInMB: 'NUMBER_VALUE', ModelClientConfig: { InvocationsMaxRetries: 'NUMBER_VALUE', InvocationsTimeoutInSeconds: 'NUMBER_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createTransformJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TransformJobName
— (String
)The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
ModelName
— (String
)The name of the model that you want to use for the transform job.
ModelName
must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. If
MaxConcurrentTransforms
is set to0
or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is1
. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value forMaxConcurrentTransforms
.ModelClientConfig
— (map
)Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds
— (Integer
)The timeout value in seconds for an invocation request. The default value is 600.
InvocationsMaxRetries
— (Integer
)The maximum number of retries when invocation requests are failing. The default value is 3.
MaxPayloadInMB
— (Integer
)The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in
MaxPayloadInMB
must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is6
MB.The value of
MaxPayloadInMB
cannot be greater than 100 MB. If you specify theMaxConcurrentTransforms
parameter, the value of(MaxConcurrentTransforms * MaxPayloadInMB)
also cannot exceed 100 MB.For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to
0
. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.BatchStrategy
— (String
)Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
To enable the batch strategy, you must set the
SplitType
property toLine
,RecordIO
, orTFRecord
.To use only one record when making an HTTP invocation request to a container, set
BatchStrategy
toSingleRecord
andSplitType
toLine
.To fit as many records in a mini-batch as can fit within the
Possible values include:MaxPayloadInMB
limit, setBatchStrategy
toMultiRecord
andSplitType
toLine
."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. We support up to 16 key and values entries in the map.
TransformInput
— (map
)Describes the input source and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— (map
)Describes the results of the transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
DataCaptureConfig
— (map
)Configuration to control how SageMaker captures inference data.
DestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
GenerateInferenceId
— (Boolean
)Flag that indicates whether to append inference id to the output.
TransformResources
— (map
)Describes the resources, including ML instance types and ML instance count, to use for the transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. The default value is
1
, and the maximum is100
. For distributed transform jobs, specify a value greater than1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
DataProcessing
— (map
)The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
InputFilter
— (String
)A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
InputFilter
parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value$
.Examples:
"$"
,"$[1:]"
,"$.features"
OutputFilter
— (String
)A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value,
$
. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.Examples:
"$"
,"$[0,5:]"
,"$['id','SageMakerOutput']"
JoinSource
— (String
)Specifies the source of the data to join with the transformed data. The valid values are
None
andInput
. The default value isNone
, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, setJoinSource
toInput
. You can specifyOutputFilter
as an additional filter to select a portion of the joined dataset and store it in the output file.For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called
SageMakerOutput
. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under theSageMakerInput
key and the results are stored inSageMakerOutput
.For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
Possible values include:"Input"
"None"
Tags
— (Array<map>
)(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TransformJobArn
— (String
)The Amazon Resource Name (ARN) of the transform job.
-
(AWS.Response)
—
Returns:
createTrial(params = {}, callback) ⇒ AWS.Request
Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
Service Reference:
Examples:
Calling the createTrial operation
var params = { ExperimentName: 'STRING_VALUE', /* required */ TrialName: 'STRING_VALUE', /* required */ DisplayName: 'STRING_VALUE', MetadataProperties: { CommitId: 'STRING_VALUE', GeneratedBy: 'STRING_VALUE', ProjectId: 'STRING_VALUE', Repository: 'STRING_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createTrial(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialName
— (String
)The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.
DisplayName
— (String
)The name of the trial as displayed. The name doesn't need to be unique. If
DisplayName
isn't specified,TrialName
is displayed.ExperimentName
— (String
)The name of the experiment to associate the trial with.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
Tags
— (Array<map>
)A list of tags to associate with the trial. You can use Search API to search on the tags.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialArn
— (String
)The Amazon Resource Name (ARN) of the trial.
-
(AWS.Response)
—
Returns:
createTrialComponent(params = {}, callback) ⇒ AWS.Request
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
Service Reference:
Examples:
Calling the createTrialComponent operation
var params = { TrialComponentName: 'STRING_VALUE', /* required */ DisplayName: 'STRING_VALUE', EndTime: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, InputArtifacts: { '<TrialComponentKey128>': { Value: 'STRING_VALUE', /* required */ MediaType: 'STRING_VALUE' }, /* '<TrialComponentKey128>': ... */ }, MetadataProperties: { CommitId: 'STRING_VALUE', GeneratedBy: 'STRING_VALUE', ProjectId: 'STRING_VALUE', Repository: 'STRING_VALUE' }, OutputArtifacts: { '<TrialComponentKey128>': { Value: 'STRING_VALUE', /* required */ MediaType: 'STRING_VALUE' }, /* '<TrialComponentKey128>': ... */ }, Parameters: { '<TrialComponentKey320>': { NumberValue: 'NUMBER_VALUE', StringValue: 'STRING_VALUE' }, /* '<TrialComponentKey320>': ... */ }, StartTime: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, Status: { Message: 'STRING_VALUE', PrimaryStatus: InProgress | Completed | Failed | Stopping | Stopped }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.createTrialComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialComponentName
— (String
)The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.
DisplayName
— (String
)The name of the component as displayed. The name doesn't need to be unique. If
DisplayName
isn't specified,TrialComponentName
is displayed.Status
— (map
)The status of the component. States include:
-
InProgress
-
Completed
-
Failed
PrimaryStatus
— (String
)The status of the trial component.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
Message
— (String
)If the component failed, a message describing why.
-
StartTime
— (Date
)When the component started.
EndTime
— (Date
)When the component ended.
Parameters
— (map<map>
)The hyperparameters for the component.
StringValue
— (String
)The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the
NumberValue
parameter.NumberValue
— (Float
)The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the
StringValue
parameter.
InputArtifacts
— (map<map>
)The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.
MediaType
— (String
)The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value
— required — (String
)The location of the artifact.
OutputArtifacts
— (map<map>
)The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType
— (String
)The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value
— required — (String
)The location of the artifact.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
Tags
— (Array<map>
)A list of tags to associate with the component. You can use Search API to search on the tags.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialComponentArn
— (String
)The Amazon Resource Name (ARN) of the trial component.
-
(AWS.Response)
—
Returns:
createUserProfile(params = {}, callback) ⇒ AWS.Request
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory.
Service Reference:
Examples:
Calling the createUserProfile operation
var params = { DomainId: 'STRING_VALUE', /* required */ UserProfileName: 'STRING_VALUE', /* required */ SingleSignOnUserIdentifier: 'STRING_VALUE', SingleSignOnUserValue: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], UserSettings: { AutoMountHomeEFS: Enabled | Disabled | DefaultAsDomain, CanvasAppSettings: { DirectDeploySettings: { Status: ENABLED | DISABLED }, EmrServerlessSettings: { ExecutionRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, GenerativeAiSettings: { AmazonBedrockRoleArn: 'STRING_VALUE' }, IdentityProviderOAuthSettings: [ { DataSourceName: SalesforceGenie | Snowflake, SecretArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, /* more items */ ], KendraSettings: { Status: ENABLED | DISABLED }, ModelRegisterSettings: { CrossAccountModelRegisterRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, TimeSeriesForecastingSettings: { AmazonForecastRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, WorkspaceSettings: { S3ArtifactPath: 'STRING_VALUE', S3KmsKeyId: 'STRING_VALUE' } }, CodeEditorAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE', LifecycleManagement: ENABLED | DISABLED, MaxIdleTimeoutInMinutes: 'NUMBER_VALUE', MinIdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, CustomFileSystemConfigs: [ { EFSFileSystemConfig: { FileSystemId: 'STRING_VALUE', /* required */ FileSystemPath: 'STRING_VALUE' } }, /* more items */ ], CustomPosixUserConfig: { Gid: 'NUMBER_VALUE', /* required */ Uid: 'NUMBER_VALUE' /* required */ }, DefaultLandingUri: 'STRING_VALUE', ExecutionRole: 'STRING_VALUE', JupyterLabAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE', LifecycleManagement: ENABLED | DISABLED, MaxIdleTimeoutInMinutes: 'NUMBER_VALUE', MinIdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, EmrSettings: { AssumableRoleArns: [ 'STRING_VALUE', /* more items */ ], ExecutionRoleArns: [ 'STRING_VALUE', /* more items */ ] }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, JupyterServerAppSettings: { CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, KernelGatewayAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, RSessionAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } }, RStudioServerProAppSettings: { AccessStatus: ENABLED | DISABLED, UserGroup: R_STUDIO_ADMIN | R_STUDIO_USER }, SecurityGroups: [ 'STRING_VALUE', /* more items */ ], SharingSettings: { NotebookOutputOption: Allowed | Disabled, S3KmsKeyId: 'STRING_VALUE', S3OutputPath: 'STRING_VALUE' }, SpaceStorageSettings: { DefaultEbsStorageSettings: { DefaultEbsVolumeSizeInGb: 'NUMBER_VALUE', /* required */ MaximumEbsVolumeSizeInGb: 'NUMBER_VALUE' /* required */ } }, StudioWebPortal: ENABLED | DISABLED, StudioWebPortalSettings: { HiddenAppTypes: [ JupyterServer | KernelGateway | DetailedProfiler | TensorBoard | CodeEditor | JupyterLab | RStudioServerPro | RSessionGateway | Canvas, /* more items */ ], HiddenMlTools: [ DataWrangler | FeatureStore | EmrClusters | AutoMl | Experiments | Training | ModelEvaluation | Pipelines | Models | JumpStart | InferenceRecommender | Endpoints | Projects | InferenceOptimization, /* more items */ ] }, TensorBoardAppSettings: { DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } } } }; sagemaker.createUserProfile(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The ID of the associated Domain.
UserProfileName
— (String
)A name for the UserProfile. This value is not case sensitive.
SingleSignOnUserIdentifier
— (String
)A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is IAM Identity Center, this field is required. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.
SingleSignOnUserValue
— (String
)The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain's AuthMode is IAM Identity Center, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.
Tags
— (Array<map>
)Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
UserSettings
— (map
)A collection of settings.
ExecutionRole
— (String
)The execution role for the user.
SecurityGroups
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the
CreateDomain.AppNetworkAccessType
parameter is set toPublicInternetOnly
.Required when the
CreateDomain.AppNetworkAccessType
parameter is set toVpcOnly
, unless specified as part of theDefaultUserSettings
for the domain.Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SharingSettings
— (map
)Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption
— (String
)Whether to include the notebook cell output when sharing the notebook. The default is
Possible values include:Disabled
."Allowed"
"Disabled"
S3OutputPath
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon S3 bucket used to store the shared notebook snapshots.S3KmsKeyId
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings
— (map
)The Jupyter server's app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The kernel gateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
TensorBoardAppSettings
— (map
)The TensorBoard app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings
— (map
)A collection of settings that configure user interaction with the
RStudioServerPro
app.AccessStatus
— (String
)Indicates whether the current user has access to the
Possible values include:RStudioServerPro
app."ENABLED"
"DISABLED"
UserGroup
— (String
)The level of permissions that the user has within the
Possible values include:RStudioServerPro
app. This value defaults toUser
. TheAdmin
value allows the user access to the RStudio Administrative Dashboard."R_STUDIO_ADMIN"
"R_STUDIO_USER"
RSessionAppSettings
— (map
)A collection of settings that configure the
RSessionGateway
app.DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a RSession app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
CanvasAppSettings
— (map
)The Canvas app settings.
TimeSeriesForecastingSettings
— (map
)Time series forecast settings for the SageMaker Canvas application.
Status
— (String
)Describes whether time series forecasting is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
AmazonForecastRoleArn
— (String
)The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the
UserProfile
that launches the Canvas application. If an execution role is not specified in theUserProfile
, Canvas uses the execution role specified in the Domain that owns theUserProfile
. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached andforecast.amazonaws.com
added in the trust relationship as a service principal.
ModelRegisterSettings
— (map
)The model registry settings for the SageMaker Canvas application.
Status
— (String
)Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CrossAccountModelRegisterRoleArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings
— (map
)The workspace settings for the SageMaker Canvas application.
S3ArtifactPath
— (String
)The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings
— (Array<map>
)The settings for connecting to an external data source with OAuth.
DataSourceName
— (String
)The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Possible values include:"SalesforceGenie"
"Snowflake"
Status
— (String
)Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
SecretArn
— (String
)The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings
— (map
)The model deployment settings for the SageMaker Canvas application.
Status
— (String
)Describes whether model deployment permissions are enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
KendraSettings
— (map
)The settings for document querying.
Status
— (String
)Describes whether the document querying feature is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
GenerativeAiSettings
— (map
)The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn
— (String
)The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes
bedrock.amazonaws.com
as a service principal.
EmrServerlessSettings
— (map
)The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status
— (String
)Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CodeEditorAppSettings
— (map
)The Code Editor application settings.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a Code Editor app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a JupyterLab app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set
LifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings
— (map
)The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
ExecutionRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
SpaceStorageSettings
— (map
)The storage settings for a space.
DefaultEbsStorageSettings
— (map
)The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb
— required — (Integer
)The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb
— required — (Integer
)The maximum size of the EBS storage volume for a space.
DefaultLandingUri
— (String
)The default experience that the user is directed to when accessing the domain. The supported values are:
-
studio::
: Indicates that Studio is the default experience. This value can only be passed ifStudioWebPortal
is set toENABLED
. -
app:JupyterServer:
: Indicates that Studio Classic is the default experience.
-
StudioWebPortal
— (String
)Whether the user can access Studio. If this value is set to
Possible values include:DISABLED
, the user cannot access Studio, even if that is the default experience for the domain."ENABLED"
"DISABLED"
CustomPosixUserConfig
— (map
)Details about the POSIX identity that is used for file system operations.
Uid
— required — (Integer
)The POSIX user ID.
Gid
— required — (Integer
)The POSIX group ID.
CustomFileSystemConfigs
— (Array<map>
)The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystemConfig
— (map
)The settings for a custom Amazon EFS file system.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
FileSystemPath
— (String
)The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings
— (map
)Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools
— (Array<String>
)The machine learning tools that are hidden from the Studio left navigation pane.
HiddenAppTypes
— (Array<String>
)The Applications supported in Studio that are hidden from the Studio left navigation pane.
AutoMountHomeEFS
— (String
)Indicates whether auto-mounting of an EFS volume is supported for the user profile. The
Possible values include:DefaultAsDomain
value is only supported for user profiles. Do not use theDefaultAsDomain
value when setting this parameter for a domain."Enabled"
"Disabled"
"DefaultAsDomain"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:UserProfileArn
— (String
)The user profile Amazon Resource Name (ARN).
-
(AWS.Response)
—
Returns:
createWorkforce(params = {}, callback) ⇒ AWS.Request
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use
CreateWorkforce
to create a new workforce.To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in
CognitoConfig
. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito).To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in
OidcConfig
. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).Service Reference:
Examples:
Calling the createWorkforce operation
var params = { WorkforceName: 'STRING_VALUE', /* required */ CognitoConfig: { ClientId: 'STRING_VALUE', /* required */ UserPool: 'STRING_VALUE' /* required */ }, OidcConfig: { AuthorizationEndpoint: 'STRING_VALUE', /* required */ ClientId: 'STRING_VALUE', /* required */ ClientSecret: 'STRING_VALUE', /* required */ Issuer: 'STRING_VALUE', /* required */ JwksUri: 'STRING_VALUE', /* required */ LogoutEndpoint: 'STRING_VALUE', /* required */ TokenEndpoint: 'STRING_VALUE', /* required */ UserInfoEndpoint: 'STRING_VALUE', /* required */ AuthenticationRequestExtraParams: { '<AuthenticationRequestExtraParamsKey>': 'STRING_VALUE', /* '<AuthenticationRequestExtraParamsKey>': ... */ }, Scope: 'STRING_VALUE' }, SourceIpConfig: { Cidrs: [ /* required */ 'STRING_VALUE', /* more items */ ] }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], WorkforceVpcConfig: { SecurityGroupIds: [ 'STRING_VALUE', /* more items */ ], Subnets: [ 'STRING_VALUE', /* more items */ ], VpcId: 'STRING_VALUE' } }; sagemaker.createWorkforce(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CognitoConfig
— (map
)Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
Do not use
OidcConfig
if you specify values forCognitoConfig
.UserPool
— required — (String
)A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId
— required — (String
)The client ID for your Amazon Cognito user pool.
OidcConfig
— (map
)Use this parameter to configure a private workforce using your own OIDC Identity Provider.
Do not use
CognitoConfig
if you specify values forOidcConfig
.ClientId
— required — (String
)The OIDC IdP client ID used to configure your private workforce.
ClientSecret
— required — (String
)The OIDC IdP client secret used to configure your private workforce.
Issuer
— required — (String
)The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint
— required — (String
)The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint
— required — (String
)The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint
— required — (String
)The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint
— required — (String
)The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri
— required — (String
)The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
Scope
— (String
)An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.
AuthenticationRequestExtraParams
— (map<String>
)A string to string map of identifiers specific to the custom identity provider (IdP) being used.
SourceIpConfig
— (map
)A list of IP address ranges (CIDRs). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to log in to their worker portal from an IP address within this range. By default, a workforce isn't restricted to specific IP addresses.
Cidrs
— required — (Array<String>
)A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note: The following Length Constraints apply to individual CIDR values in the CIDR value list.
WorkforceName
— (String
)The name of the private workforce.
Tags
— (Array<map>
)An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
WorkforceVpcConfig
— (map
)Use this parameter to configure a workforce using VPC.
VpcId
— (String
)The ID of the VPC that the workforce uses for communication.
SecurityGroupIds
— (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. The security groups must be for the same VPC as specified in the subnet.Subnets
— (Array<String>
)The ID of the subnets in the VPC that you want to connect.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:WorkforceArn
— (String
)The Amazon Resource Name (ARN) of the workforce.
-
(AWS.Response)
—
Returns:
createWorkteam(params = {}, callback) ⇒ AWS.Request
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
You cannot create more than 25 work teams in an account and region.
Service Reference:
Examples:
Calling the createWorkteam operation
var params = { Description: 'STRING_VALUE', /* required */ MemberDefinitions: [ /* required */ { CognitoMemberDefinition: { ClientId: 'STRING_VALUE', /* required */ UserGroup: 'STRING_VALUE', /* required */ UserPool: 'STRING_VALUE' /* required */ }, OidcMemberDefinition: { Groups: [ 'STRING_VALUE', /* more items */ ] } }, /* more items */ ], WorkteamName: 'STRING_VALUE', /* required */ NotificationConfiguration: { NotificationTopicArn: 'STRING_VALUE' }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], WorkerAccessConfiguration: { S3Presign: { IamPolicyConstraints: { SourceIp: Enabled | Disabled, VpcSourceIp: Enabled | Disabled } } }, WorkforceName: 'STRING_VALUE' }; sagemaker.createWorkteam(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
WorkteamName
— (String
)The name of the work team. Use this name to identify the work team.
WorkforceName
— (String
)The name of the workforce.
MemberDefinitions
— (Array<map>
)A list of
MemberDefinition
objects that contains objects that identify the workers that make up the work team.Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use
CognitoMemberDefinition
. For workforces created using your own OIDC identity provider (IdP) useOidcMemberDefinition
. Do not provide input for both of these parameters in a single request.For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the
CognitoMemberDefinition
objects that make up the member definition must have the sameClientId
andUserPool
values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools.For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in
OidcMemberDefinition
by listing those groups inGroups
.CognitoMemberDefinition
— (map
)The Amazon Cognito user group that is part of the work team.
UserPool
— required — (String
)An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup
— required — (String
)An identifier for a user group.
ClientId
— required — (String
)An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition
— (map
)A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of
Groups
, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.Groups
— (Array<String>
)A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
Description
— (String
)A description of the work team.
NotificationConfiguration
— (map
)Configures notification of workers regarding available or expiring work items.
NotificationTopicArn
— (String
)The ARN for the Amazon SNS topic to which notifications should be published.
WorkerAccessConfiguration
— (map
)Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.
S3Presign
— (map
)Defines any Amazon S3 resource constraints.
IamPolicyConstraints
— (map
)Use this parameter to specify the allowed request source. Possible sources are either
SourceIp
orVpcSourceIp
.SourceIp
— (String
)When
Possible values include:SourceIp
isEnabled
the worker's IP address when a task is rendered in the worker portal is added to the IAM policy as aCondition
used to generate the Amazon S3 presigned URL. This IP address is checked by Amazon S3 and must match in order for the Amazon S3 resource to be rendered in the worker portal."Enabled"
"Disabled"
VpcSourceIp
— (String
)When
Possible values include:VpcSourceIp
isEnabled
the worker's IP address when a task is rendered in private worker portal inside the VPC is added to the IAM policy as aCondition
used to generate the Amazon S3 presigned URL. To render the task successfully Amazon S3 checks that the presigned URL is being accessed over an Amazon S3 VPC Endpoint, and that the worker's IP address matches the IP address in the IAM policy. To learn more about configuring private worker portal, see Use Amazon VPC mode from a private worker portal."Enabled"
"Disabled"
Tags
— (Array<map>
)An array of key-value pairs.
For more information, see Resource Tag and Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:WorkteamArn
— (String
)The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.
-
(AWS.Response)
—
Returns:
deleteAction(params = {}, callback) ⇒ AWS.Request
Deletes an action.
Service Reference:
Examples:
Calling the deleteAction operation
var params = { ActionName: 'STRING_VALUE' /* required */ }; sagemaker.deleteAction(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ActionName
— (String
)The name of the action to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ActionArn
— (String
)The Amazon Resource Name (ARN) of the action.
-
(AWS.Response)
—
Returns:
deleteAlgorithm(params = {}, callback) ⇒ AWS.Request
Removes the specified algorithm from your account.
Service Reference:
Examples:
Calling the deleteAlgorithm operation
var params = { AlgorithmName: 'STRING_VALUE' /* required */ }; sagemaker.deleteAlgorithm(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AlgorithmName
— (String
)The name of the algorithm to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteApp(params = {}, callback) ⇒ AWS.Request
Used to stop and delete an app.
Service Reference:
Examples:
Calling the deleteApp operation
var params = { AppName: 'STRING_VALUE', /* required */ AppType: JupyterServer | KernelGateway | DetailedProfiler | TensorBoard | CodeEditor | JupyterLab | RStudioServerPro | RSessionGateway | Canvas, /* required */ DomainId: 'STRING_VALUE', /* required */ SpaceName: 'STRING_VALUE', UserProfileName: 'STRING_VALUE' }; sagemaker.deleteApp(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The user profile name. If this value is not set, then
SpaceName
must be set.SpaceName
— (String
)The name of the space. If this value is not set, then
UserProfileName
must be set.AppType
— (String
)The type of app.
Possible values include:"JupyterServer"
"KernelGateway"
"DetailedProfiler"
"TensorBoard"
"CodeEditor"
"JupyterLab"
"RStudioServerPro"
"RSessionGateway"
"Canvas"
AppName
— (String
)The name of the app.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteAppImageConfig(params = {}, callback) ⇒ AWS.Request
Deletes an AppImageConfig.
Service Reference:
Examples:
Calling the deleteAppImageConfig operation
var params = { AppImageConfigName: 'STRING_VALUE' /* required */ }; sagemaker.deleteAppImageConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AppImageConfigName
— (String
)The name of the AppImageConfig to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteArtifact(params = {}, callback) ⇒ AWS.Request
Deletes an artifact. Either
ArtifactArn
orSource
must be specified.Service Reference:
Examples:
Calling the deleteArtifact operation
var params = { ArtifactArn: 'STRING_VALUE', Source: { SourceUri: 'STRING_VALUE', /* required */ SourceTypes: [ { SourceIdType: MD5Hash | S3ETag | S3Version | Custom, /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] } }; sagemaker.deleteArtifact(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ArtifactArn
— (String
)The Amazon Resource Name (ARN) of the artifact to delete.
Source
— (map
)The URI of the source.
SourceUri
— required — (String
)The URI of the source.
SourceTypes
— (Array<map>
)A list of source types.
SourceIdType
— required — (String
)The type of ID.
Possible values include:"MD5Hash"
"S3ETag"
"S3Version"
"Custom"
Value
— required — (String
)The ID.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ArtifactArn
— (String
)The Amazon Resource Name (ARN) of the artifact.
-
(AWS.Response)
—
Returns:
deleteAssociation(params = {}, callback) ⇒ AWS.Request
Deletes an association.
Service Reference:
Examples:
Calling the deleteAssociation operation
var params = { DestinationArn: 'STRING_VALUE', /* required */ SourceArn: 'STRING_VALUE' /* required */ }; sagemaker.deleteAssociation(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SourceArn
— (String
)The ARN of the source.
DestinationArn
— (String
)The Amazon Resource Name (ARN) of the destination.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:SourceArn
— (String
)The ARN of the source.
DestinationArn
— (String
)The Amazon Resource Name (ARN) of the destination.
-
(AWS.Response)
—
Returns:
deleteCluster(params = {}, callback) ⇒ AWS.Request
Delete a SageMaker HyperPod cluster.
Service Reference:
Examples:
Calling the deleteCluster operation
var params = { ClusterName: 'STRING_VALUE' /* required */ }; sagemaker.deleteCluster(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ClusterName
— (String
)The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ClusterArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.
-
(AWS.Response)
—
Returns:
deleteCodeRepository(params = {}, callback) ⇒ AWS.Request
Deletes the specified Git repository from your account.
Service Reference:
Examples:
Calling the deleteCodeRepository operation
var params = { CodeRepositoryName: 'STRING_VALUE' /* required */ }; sagemaker.deleteCodeRepository(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CodeRepositoryName
— (String
)The name of the Git repository to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteCompilationJob(params = {}, callback) ⇒ AWS.Request
Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role.
You can delete a compilation job only if its current status is
COMPLETED
,FAILED
, orSTOPPED
. If the job status isSTARTING
orINPROGRESS
, stop the job, and then delete it after its status becomesSTOPPED
.Service Reference:
Examples:
Calling the deleteCompilationJob operation
var params = { CompilationJobName: 'STRING_VALUE' /* required */ }; sagemaker.deleteCompilationJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CompilationJobName
— (String
)The name of the compilation job to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteContext(params = {}, callback) ⇒ AWS.Request
Deletes an context.
Service Reference:
Examples:
Calling the deleteContext operation
var params = { ContextName: 'STRING_VALUE' /* required */ }; sagemaker.deleteContext(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ContextName
— (String
)The name of the context to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ContextArn
— (String
)The Amazon Resource Name (ARN) of the context.
-
(AWS.Response)
—
Returns:
deleteDataQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes a data quality monitoring job definition.
Service Reference:
Examples:
Calling the deleteDataQualityJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE' /* required */ }; sagemaker.deleteDataQualityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the data quality monitoring job definition to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteDeviceFleet(params = {}, callback) ⇒ AWS.Request
Deletes a fleet.
Service Reference:
Examples:
Calling the deleteDeviceFleet operation
var params = { DeviceFleetName: 'STRING_VALUE' /* required */ }; sagemaker.deleteDeviceFleet(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DeviceFleetName
— (String
)The name of the fleet to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteDomain(params = {}, callback) ⇒ AWS.Request
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
Service Reference:
Examples:
Calling the deleteDomain operation
var params = { DomainId: 'STRING_VALUE', /* required */ RetentionPolicy: { HomeEfsFileSystem: Retain | Delete } }; sagemaker.deleteDomain(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
RetentionPolicy
— (map
)The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted).
HomeEfsFileSystem
— (String
)The default is
Retain
, which specifies to keep the data stored on the Amazon EFS volume.Specify
Possible values include:Delete
to delete the data stored on the Amazon EFS volume."Retain"
"Delete"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteEdgeDeploymentPlan(params = {}, callback) ⇒ AWS.Request
Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
Service Reference:
Examples:
Calling the deleteEdgeDeploymentPlan operation
var params = { EdgeDeploymentPlanName: 'STRING_VALUE' /* required */ }; sagemaker.deleteEdgeDeploymentPlan(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgeDeploymentPlanName
— (String
)The name of the edge deployment plan to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteEdgeDeploymentStage(params = {}, callback) ⇒ AWS.Request
Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
Service Reference:
Examples:
Calling the deleteEdgeDeploymentStage operation
var params = { EdgeDeploymentPlanName: 'STRING_VALUE', /* required */ StageName: 'STRING_VALUE' /* required */ }; sagemaker.deleteEdgeDeploymentStage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgeDeploymentPlanName
— (String
)The name of the edge deployment plan from which the stage will be deleted.
StageName
— (String
)The name of the stage.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteEndpoint(params = {}, callback) ⇒ AWS.Request
Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.
SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your
ExecutionRoleArn
, otherwise SageMaker cannot delete these resources.Service Reference:
Examples:
Calling the deleteEndpoint operation
var params = { EndpointName: 'STRING_VALUE' /* required */ }; sagemaker.deleteEndpoint(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (String
)The name of the endpoint that you want to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteEndpointConfig(params = {}, callback) ⇒ AWS.Request
Deletes an endpoint configuration. The
DeleteEndpointConfig
API deletes only the specified configuration. It does not delete endpoints created using the configuration.You must not delete an
EndpointConfig
in use by an endpoint that is live or while theUpdateEndpoint
orCreateEndpoint
operations are being performed on the endpoint. If you delete theEndpointConfig
of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.Service Reference:
Examples:
Calling the deleteEndpointConfig operation
var params = { EndpointConfigName: 'STRING_VALUE' /* required */ }; sagemaker.deleteEndpointConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointConfigName
— (String
)The name of the endpoint configuration that you want to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteExperiment(params = {}, callback) ⇒ AWS.Request
Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
Service Reference:
Examples:
Calling the deleteExperiment operation
var params = { ExperimentName: 'STRING_VALUE' /* required */ }; sagemaker.deleteExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ExperimentName
— (String
)The name of the experiment to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ExperimentArn
— (String
)The Amazon Resource Name (ARN) of the experiment that is being deleted.
-
(AWS.Response)
—
Returns:
deleteFeatureGroup(params = {}, callback) ⇒ AWS.Request
Delete the
FeatureGroup
and any data that was written to theOnlineStore
of theFeatureGroup
. Data cannot be accessed from theOnlineStore
immediately afterDeleteFeatureGroup
is called.Data written into the
OfflineStore
will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for yourOfflineStore
are not deleted.Note that it can take approximately 10-15 minutes to delete an
OnlineStore
FeatureGroup
with theInMemory
StorageType
.Service Reference:
Examples:
Calling the deleteFeatureGroup operation
var params = { FeatureGroupName: 'STRING_VALUE' /* required */ }; sagemaker.deleteFeatureGroup(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FeatureGroupName
— (String
)The name of the
FeatureGroup
you want to delete. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteFlowDefinition(params = {}, callback) ⇒ AWS.Request
Deletes the specified flow definition.
Service Reference:
Examples:
Calling the deleteFlowDefinition operation
var params = { FlowDefinitionName: 'STRING_VALUE' /* required */ }; sagemaker.deleteFlowDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FlowDefinitionName
— (String
)The name of the flow definition you are deleting.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteHub(params = {}, callback) ⇒ AWS.Request
Delete a hub.
Service Reference:
Examples:
Calling the deleteHub operation
var params = { HubName: 'STRING_VALUE' /* required */ }; sagemaker.deleteHub(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubName
— (String
)The name of the hub to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteHubContent(params = {}, callback) ⇒ AWS.Request
Delete the contents of a hub.
Service Reference:
Examples:
Calling the deleteHubContent operation
var params = { HubContentName: 'STRING_VALUE', /* required */ HubContentType: Model | Notebook | ModelReference, /* required */ HubContentVersion: 'STRING_VALUE', /* required */ HubName: 'STRING_VALUE' /* required */ }; sagemaker.deleteHubContent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubName
— (String
)The name of the hub that you want to delete content in.
HubContentType
— (String
)The type of content that you want to delete from a hub.
Possible values include:"Model"
"Notebook"
"ModelReference"
HubContentName
— (String
)The name of the content that you want to delete from a hub.
HubContentVersion
— (String
)The version of the content that you want to delete from a hub.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteHubContentReference(params = {}, callback) ⇒ AWS.Request
Delete a hub content reference in order to remove a model from a private hub.
Service Reference:
Examples:
Calling the deleteHubContentReference operation
var params = { HubContentName: 'STRING_VALUE', /* required */ HubContentType: Model | Notebook | ModelReference, /* required */ HubName: 'STRING_VALUE' /* required */ }; sagemaker.deleteHubContentReference(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubName
— (String
)The name of the hub to delete the hub content reference from.
HubContentType
— (String
)The type of hub content reference to delete. The only supported type of hub content reference to delete is
Possible values include:ModelReference
."Model"
"Notebook"
"ModelReference"
HubContentName
— (String
)The name of the hub content to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteHumanTaskUi(params = {}, callback) ⇒ AWS.Request
Use this operation to delete a human task user interface (worker task template).
To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis. When you delete a worker task template, it no longer appears when you call
ListHumanTaskUis
.Service Reference:
Examples:
Calling the deleteHumanTaskUi operation
var params = { HumanTaskUiName: 'STRING_VALUE' /* required */ }; sagemaker.deleteHumanTaskUi(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HumanTaskUiName
— (String
)The name of the human task user interface (work task template) you want to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Deletes a hyperparameter tuning job. The
DeleteHyperParameterTuningJob
API deletes only the tuning job entry that was created in SageMaker when you called theCreateHyperParameterTuningJob
API. It does not delete training jobs, artifacts, or the IAM role that you specified when creating the model.Service Reference:
Examples:
Calling the deleteHyperParameterTuningJob operation
var params = { HyperParameterTuningJobName: 'STRING_VALUE' /* required */ }; sagemaker.deleteHyperParameterTuningJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HyperParameterTuningJobName
— (String
)The name of the hyperparameter tuning job that you want to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteImage(params = {}, callback) ⇒ AWS.Request
Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
Service Reference:
Examples:
Calling the deleteImage operation
var params = { ImageName: 'STRING_VALUE' /* required */ }; sagemaker.deleteImage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ImageName
— (String
)The name of the image to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteImageVersion(params = {}, callback) ⇒ AWS.Request
Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
Service Reference:
Examples:
Calling the deleteImageVersion operation
var params = { ImageName: 'STRING_VALUE', /* required */ Alias: 'STRING_VALUE', Version: 'NUMBER_VALUE' }; sagemaker.deleteImageVersion(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ImageName
— (String
)The name of the image to delete.
Version
— (Integer
)The version to delete.
Alias
— (String
)The alias of the image to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteInferenceComponent(params = {}, callback) ⇒ AWS.Request
Deletes an inference component.
Service Reference:
Examples:
Calling the deleteInferenceComponent operation
var params = { InferenceComponentName: 'STRING_VALUE' /* required */ }; sagemaker.deleteInferenceComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
InferenceComponentName
— (String
)The name of the inference component to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Deletes an inference experiment.
Note: This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.Service Reference:
Examples:
Calling the deleteInferenceExperiment operation
var params = { Name: 'STRING_VALUE' /* required */ }; sagemaker.deleteInferenceExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
Name
— (String
)The name of the inference experiment you want to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceExperimentArn
— (String
)The ARN of the deleted inference experiment.
-
(AWS.Response)
—
Returns:
deleteMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
Service Reference:
Examples:
Calling the deleteMlflowTrackingServer operation
var params = { TrackingServerName: 'STRING_VALUE' /* required */ }; sagemaker.deleteMlflowTrackingServer(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrackingServerName
— (String
)The name of the the tracking server to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrackingServerArn
— (String
)A
TrackingServerArn
object, the ARN of the tracking server that is deleted if successfully found.
-
(AWS.Response)
—
Returns:
deleteModel(params = {}, callback) ⇒ AWS.Request
Deletes a model. The
DeleteModel
API deletes only the model entry that was created in SageMaker when you called theCreateModel
API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.Service Reference:
Examples:
Calling the deleteModel operation
var params = { ModelName: 'STRING_VALUE' /* required */ }; sagemaker.deleteModel(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelName
— (String
)The name of the model to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteModelBiasJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes an Amazon SageMaker model bias job definition.
Service Reference:
Examples:
Calling the deleteModelBiasJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE' /* required */ }; sagemaker.deleteModelBiasJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the model bias job definition to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteModelCard(params = {}, callback) ⇒ AWS.Request
Deletes an Amazon SageMaker Model Card.
Service Reference:
Examples:
Calling the deleteModelCard operation
var params = { ModelCardName: 'STRING_VALUE' /* required */ }; sagemaker.deleteModelCard(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelCardName
— (String
)The name of the model card to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteModelExplainabilityJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes an Amazon SageMaker model explainability job definition.
Service Reference:
Examples:
Calling the deleteModelExplainabilityJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE' /* required */ }; sagemaker.deleteModelExplainabilityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the model explainability job definition to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteModelPackage(params = {}, callback) ⇒ AWS.Request
Deletes a model package.
A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
Service Reference:
Examples:
Calling the deleteModelPackage operation
var params = { ModelPackageName: 'STRING_VALUE' /* required */ }; sagemaker.deleteModelPackage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageName
— (String
)The name or Amazon Resource Name (ARN) of the model package to delete.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteModelPackageGroup(params = {}, callback) ⇒ AWS.Request
Deletes the specified model group.
Service Reference:
Examples:
Calling the deleteModelPackageGroup operation
var params = { ModelPackageGroupName: 'STRING_VALUE' /* required */ }; sagemaker.deleteModelPackageGroup(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageGroupName
— (String
)The name of the model group to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteModelPackageGroupPolicy(params = {}, callback) ⇒ AWS.Request
Deletes a model group resource policy.
Service Reference:
Examples:
Calling the deleteModelPackageGroupPolicy operation
var params = { ModelPackageGroupName: 'STRING_VALUE' /* required */ }; sagemaker.deleteModelPackageGroupPolicy(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageGroupName
— (String
)The name of the model group for which to delete the policy.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteModelQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Deletes the secified model quality monitoring job definition.
Service Reference:
Examples:
Calling the deleteModelQualityJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE' /* required */ }; sagemaker.deleteModelQualityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the model quality monitoring job definition to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
Service Reference:
Examples:
Calling the deleteMonitoringSchedule operation
var params = { MonitoringScheduleName: 'STRING_VALUE' /* required */ }; sagemaker.deleteMonitoringSchedule(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)The name of the monitoring schedule to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteNotebookInstance(params = {}, callback) ⇒ AWS.Request
Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you must call the
StopNotebookInstance
API.When you delete a notebook instance, you lose all of your data. SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
Service Reference:
Examples:
Calling the deleteNotebookInstance operation
var params = { NotebookInstanceName: 'STRING_VALUE' /* required */ }; sagemaker.deleteNotebookInstance(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceName
— (String
)The name of the SageMaker notebook instance to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Deletes a notebook instance lifecycle configuration.
Service Reference:
Examples:
Calling the deleteNotebookInstanceLifecycleConfig operation
var params = { NotebookInstanceLifecycleConfigName: 'STRING_VALUE' /* required */ }; sagemaker.deleteNotebookInstanceLifecycleConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceLifecycleConfigName
— (String
)The name of the lifecycle configuration to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteOptimizationJob(params = {}, callback) ⇒ AWS.Request
Deletes an optimization job.
Service Reference:
Examples:
Calling the deleteOptimizationJob operation
var params = { OptimizationJobName: 'STRING_VALUE' /* required */ }; sagemaker.deleteOptimizationJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
OptimizationJobName
— (String
)The name that you assigned to the optimization job.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deletePipeline(params = {}, callback) ⇒ AWS.Request
Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the
StopPipelineExecution
API. When you delete a pipeline, all instances of the pipeline are deleted.Service Reference:
Examples:
Calling the deletePipeline operation
var params = { ClientRequestToken: 'STRING_VALUE', /* required */ PipelineName: 'STRING_VALUE' /* required */ }; sagemaker.deletePipeline(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineName
— (String
)The name of the pipeline to delete.
ClientRequestToken
— (String
)A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
If a token is not provided, the SDK will use a version 4 UUID.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineArn
— (String
)The Amazon Resource Name (ARN) of the pipeline to delete.
-
(AWS.Response)
—
Returns:
deleteProject(params = {}, callback) ⇒ AWS.Request
Delete the specified project.
Service Reference:
Examples:
Calling the deleteProject operation
var params = { ProjectName: 'STRING_VALUE' /* required */ }; sagemaker.deleteProject(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ProjectName
— (String
)The name of the project to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteSpace(params = {}, callback) ⇒ AWS.Request
Used to delete a space.
Service Reference:
Examples:
Calling the deleteSpace operation
var params = { DomainId: 'STRING_VALUE', /* required */ SpaceName: 'STRING_VALUE' /* required */ }; sagemaker.deleteSpace(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The ID of the associated domain.
SpaceName
— (String
)The name of the space.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteStudioLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Deletes the Amazon SageMaker Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
Service Reference:
Examples:
Calling the deleteStudioLifecycleConfig operation
var params = { StudioLifecycleConfigName: 'STRING_VALUE' /* required */ }; sagemaker.deleteStudioLifecycleConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
StudioLifecycleConfigName
— (String
)The name of the Amazon SageMaker Studio Lifecycle Configuration to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteTags(params = {}, callback) ⇒ AWS.Request
Deletes the specified tags from an SageMaker resource.
To list a resource's tags, use the
ListTags
API.Note: When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.Note: When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API.Service Reference:
Examples:
Calling the deleteTags operation
var params = { ResourceArn: 'STRING_VALUE', /* required */ TagKeys: [ /* required */ 'STRING_VALUE', /* more items */ ] }; sagemaker.deleteTags(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ResourceArn
— (String
)The Amazon Resource Name (ARN) of the resource whose tags you want to delete.
TagKeys
— (Array<String>
)An array or one or more tag keys to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteTrial(params = {}, callback) ⇒ AWS.Request
Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
Service Reference:
Examples:
Calling the deleteTrial operation
var params = { TrialName: 'STRING_VALUE' /* required */ }; sagemaker.deleteTrial(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialName
— (String
)The name of the trial to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialArn
— (String
)The Amazon Resource Name (ARN) of the trial that is being deleted.
-
(AWS.Response)
—
Returns:
deleteTrialComponent(params = {}, callback) ⇒ AWS.Request
Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
Service Reference:
Examples:
Calling the deleteTrialComponent operation
var params = { TrialComponentName: 'STRING_VALUE' /* required */ }; sagemaker.deleteTrialComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialComponentName
— (String
)The name of the component to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialComponentArn
— (String
)The Amazon Resource Name (ARN) of the component is being deleted.
-
(AWS.Response)
—
Returns:
deleteUserProfile(params = {}, callback) ⇒ AWS.Request
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
Service Reference:
Examples:
Calling the deleteUserProfile operation
var params = { DomainId: 'STRING_VALUE', /* required */ UserProfileName: 'STRING_VALUE' /* required */ }; sagemaker.deleteUserProfile(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The user profile name.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteWorkforce(params = {}, callback) ⇒ AWS.Request
Use this operation to delete a workforce.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will receive a
ResourceInUse
error.Service Reference:
Examples:
Calling the deleteWorkforce operation
var params = { WorkforceName: 'STRING_VALUE' /* required */ }; sagemaker.deleteWorkforce(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
WorkforceName
— (String
)The name of the workforce.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
deleteWorkteam(params = {}, callback) ⇒ AWS.Request
Deletes an existing work team. This operation can't be undone.
Service Reference:
Examples:
Calling the deleteWorkteam operation
var params = { WorkteamName: 'STRING_VALUE' /* required */ }; sagemaker.deleteWorkteam(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
WorkteamName
— (String
)The name of the work team to delete.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Success
— (Boolean
)Returns
true
if the work team was successfully deleted; otherwise, returnsfalse
.
-
(AWS.Response)
—
Returns:
deregisterDevices(params = {}, callback) ⇒ AWS.Request
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
Service Reference:
Examples:
Calling the deregisterDevices operation
var params = { DeviceFleetName: 'STRING_VALUE', /* required */ DeviceNames: [ /* required */ 'STRING_VALUE', /* more items */ ] }; sagemaker.deregisterDevices(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DeviceFleetName
— (String
)The name of the fleet the devices belong to.
DeviceNames
— (Array<String>
)The unique IDs of the devices.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
describeAction(params = {}, callback) ⇒ AWS.Request
Describes an action.
Service Reference:
Examples:
Calling the describeAction operation
var params = { ActionName: 'STRING_VALUE' /* required */ }; sagemaker.describeAction(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ActionName
— (String
)The name of the action to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ActionName
— (String
)The name of the action.
ActionArn
— (String
)The Amazon Resource Name (ARN) of the action.
Source
— (map
)The source of the action.
SourceUri
— required — (String
)The URI of the source.
SourceType
— (String
)The type of the source.
SourceId
— (String
)The ID of the source.
ActionType
— (String
)The type of the action.
Description
— (String
)The description of the action.
Status
— (String
)The status of the action.
Possible values include:"Unknown"
"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
Properties
— (map<String>
)A list of the action's properties.
CreationTime
— (Date
)When the action was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)When the action was last modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
LineageGroupArn
— (String
)The Amazon Resource Name (ARN) of the lineage group.
-
(AWS.Response)
—
Returns:
describeAlgorithm(params = {}, callback) ⇒ AWS.Request
Returns a description of the specified algorithm that is in your account.
Service Reference:
Examples:
Calling the describeAlgorithm operation
var params = { AlgorithmName: 'STRING_VALUE' /* required */ }; sagemaker.describeAlgorithm(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AlgorithmName
— (String
)The name of the algorithm to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AlgorithmName
— (String
)The name of the algorithm being described.
AlgorithmArn
— (String
)The Amazon Resource Name (ARN) of the algorithm.
AlgorithmDescription
— (String
)A brief summary about the algorithm.
CreationTime
— (Date
)A timestamp specifying when the algorithm was created.
TrainingSpecification
— (map
)Details about training jobs run by this algorithm.
TrainingImage
— required — (String
)The Amazon ECR registry path of the Docker image that contains the training algorithm.
TrainingImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
SupportedHyperParameters
— (Array<map>
)A list of the
HyperParameterSpecification
objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>Name
— required — (String
)The name of this hyperparameter. The name must be unique.
Description
— (String
)A brief description of the hyperparameter.
Type
— required — (String
)The type of this hyperparameter. The valid types are
Possible values include:Integer
,Continuous
,Categorical
, andFreeText
."Integer"
"Continuous"
"Categorical"
"FreeText"
Range
— (map
)The allowed range for this hyperparameter.
IntegerParameterRangeSpecification
— (map
)A
IntegerParameterRangeSpecification
object that defines the possible values for an integer hyperparameter.MinValue
— required — (String
)The minimum integer value allowed.
MaxValue
— required — (String
)The maximum integer value allowed.
ContinuousParameterRangeSpecification
— (map
)A
ContinuousParameterRangeSpecification
object that defines the possible values for a continuous hyperparameter.MinValue
— required — (String
)The minimum floating-point value allowed.
MaxValue
— required — (String
)The maximum floating-point value allowed.
CategoricalParameterRangeSpecification
— (map
)A
CategoricalParameterRangeSpecification
object that defines the possible values for a categorical hyperparameter.Values
— required — (Array<String>
)The allowed categories for the hyperparameter.
IsTunable
— (Boolean
)Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
IsRequired
— (Boolean
)Indicates whether this hyperparameter is required.
DefaultValue
— (String
)The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
SupportedTrainingInstanceTypes
— required — (Array<String>
)A list of the instance types that this algorithm can use for training.
SupportsDistributedTraining
— (Boolean
)Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
MetricDefinitions
— (Array<map>
)A list of
MetricDefinition
objects, which are used for parsing metrics generated by the algorithm.Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
TrainingChannels
— required — (Array<map>
)A list of
ChannelSpecification
objects, which specify the input sources to be used by the algorithm.Name
— required — (String
)The name of the channel.
Description
— (String
)A brief description of the channel.
IsRequired
— (Boolean
)Indicates whether the channel is required by the algorithm.
SupportedContentTypes
— required — (Array<String>
)The supported MIME types for the data.
SupportedCompressionTypes
— (Array<String>
)The allowed compression types, if data compression is used.
SupportedInputModes
— required — (Array<String>
)The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
SupportedTuningJobObjectiveMetrics
— (Array<map>
)A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
AdditionalS3DataSource
— (map
)The additional data source used during the training job.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
InferenceSpecification
— (map
)Details about inference jobs that the algorithm runs.
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
ValidationSpecification
— (map
)Details about configurations for one or more training jobs that SageMaker runs to test the algorithm.
ValidationRole
— required — (String
)The IAM roles that SageMaker uses to run the training jobs.
ValidationProfiles
— required — (Array<map>
)An array of
AlgorithmValidationProfile
objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.ProfileName
— required — (String
)The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
TrainingJobDefinition
— required — (map
)The
TrainingJobDefinition
object that describes the training job that SageMaker runs to validate your algorithm.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
HyperParameters
— (map<String>
)The hyperparameters used for the training job.
InputDataConfig
— required — (Array<map>
)An array of
Channel
objects, each of which specifies an input source.ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
OutputDataConfig
— required — (map
)the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— required — (map
)The resources, including the ML compute instances and ML storage volumes, to use for model training.
InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
StoppingCondition
— required — (map
)Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
TransformJobDefinition
— (map
)The
TransformJobDefinition
object that describes the transform job that SageMaker runs to validate your algorithm.MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB
— (Integer
)The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy
— (String
)A string that determines the number of records included in a single mini-batch.
SingleRecord
means only one record is used per mini-batch.MultiRecord
means a mini-batch is set to contain as many records that can fit within theMaxPayloadInMB
limit."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— required — (map
)A description of the input source and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— required — (map
)Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
TransformResources
— required — (map
)Identifies the ML compute instances for the transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. The default value is
1
, and the maximum is100
. For distributed transform jobs, specify a value greater than1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
AlgorithmStatus
— (String
)The current status of the algorithm.
Possible values include:"Pending"
"InProgress"
"Completed"
"Failed"
"Deleting"
AlgorithmStatusDetails
— (map
)Details about the current status of the algorithm.
ValidationStatuses
— (Array<map>
)The status of algorithm validation.
Name
— required — (String
)The name of the algorithm for which the overall status is being reported.
Status
— required — (String
)The current status.
Possible values include:"NotStarted"
"InProgress"
"Completed"
"Failed"
FailureReason
— (String
)if the overall status is
Failed
, the reason for the failure.
ImageScanStatuses
— (Array<map>
)The status of the scan of the algorithm's Docker image container.
Name
— required — (String
)The name of the algorithm for which the overall status is being reported.
Status
— required — (String
)The current status.
Possible values include:"NotStarted"
"InProgress"
"Completed"
"Failed"
FailureReason
— (String
)if the overall status is
Failed
, the reason for the failure.
ProductId
— (String
)The product identifier of the algorithm.
CertifyForMarketplace
— (Boolean
)Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.
-
(AWS.Response)
—
Returns:
describeApp(params = {}, callback) ⇒ AWS.Request
Describes the app.
Service Reference:
Examples:
Calling the describeApp operation
var params = { AppName: 'STRING_VALUE', /* required */ AppType: JupyterServer | KernelGateway | DetailedProfiler | TensorBoard | CodeEditor | JupyterLab | RStudioServerPro | RSessionGateway | Canvas, /* required */ DomainId: 'STRING_VALUE', /* required */ SpaceName: 'STRING_VALUE', UserProfileName: 'STRING_VALUE' }; sagemaker.describeApp(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The user profile name. If this value is not set, then
SpaceName
must be set.SpaceName
— (String
)The name of the space.
AppType
— (String
)The type of app.
Possible values include:"JupyterServer"
"KernelGateway"
"DetailedProfiler"
"TensorBoard"
"CodeEditor"
"JupyterLab"
"RStudioServerPro"
"RSessionGateway"
"Canvas"
AppName
— (String
)The name of the app.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AppArn
— (String
)The Amazon Resource Name (ARN) of the app.
AppType
— (String
)The type of app.
Possible values include:"JupyterServer"
"KernelGateway"
"DetailedProfiler"
"TensorBoard"
"CodeEditor"
"JupyterLab"
"RStudioServerPro"
"RSessionGateway"
"Canvas"
AppName
— (String
)The name of the app.
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The user profile name.
SpaceName
— (String
)The name of the space. If this value is not set, then
UserProfileName
must be set.Status
— (String
)The status.
Possible values include:"Deleted"
"Deleting"
"Failed"
"InService"
"Pending"
LastHealthCheckTimestamp
— (Date
)The timestamp of the last health check.
LastUserActivityTimestamp
— (Date
)The timestamp of the last user's activity.
LastUserActivityTimestamp
is also updated when SageMaker performs health checks without user activity. As a result, this value is set to the same value asLastHealthCheckTimestamp
.CreationTime
— (Date
)The creation time of the application.
Note: After an application has been shut down for 24 hours, SageMaker deletes all metadata for the application. To be considered an update and retain application metadata, applications must be restarted within 24 hours after the previous application has been shut down. After this time window, creation of an application is considered a new application rather than an update of the previous application.FailureReason
— (String
)The failure reason.
ResourceSpec
— (map
)The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
-
(AWS.Response)
—
Returns:
describeAppImageConfig(params = {}, callback) ⇒ AWS.Request
Describes an AppImageConfig.
Service Reference:
Examples:
Calling the describeAppImageConfig operation
var params = { AppImageConfigName: 'STRING_VALUE' /* required */ }; sagemaker.describeAppImageConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AppImageConfigName
— (String
)The name of the AppImageConfig to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AppImageConfigArn
— (String
)The ARN of the AppImageConfig.
AppImageConfigName
— (String
)The name of the AppImageConfig.
CreationTime
— (Date
)When the AppImageConfig was created.
LastModifiedTime
— (Date
)When the AppImageConfig was last modified.
KernelGatewayImageConfig
— (map
)The configuration of a KernelGateway app.
KernelSpecs
— required — (Array<map>
)The specification of the Jupyter kernels in the image.
Name
— required — (String
)The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName
— (String
)The display name of the kernel.
FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
JupyterLabAppImageConfig
— (map
)The configuration of the JupyterLab app.
FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
ContainerConfig
— (map
)The configuration used to run the application image container.
ContainerArguments
— (Array<String>
)The arguments for the container when you're running the application.
ContainerEntrypoint
— (Array<String>
)The entrypoint used to run the application in the container.
ContainerEnvironmentVariables
— (map<String>
)The environment variables to set in the container
CodeEditorAppImageConfig
— (map
)The configuration of the Code Editor app.
FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
ContainerConfig
— (map
)The configuration used to run the application image container.
ContainerArguments
— (Array<String>
)The arguments for the container when you're running the application.
ContainerEntrypoint
— (Array<String>
)The entrypoint used to run the application in the container.
ContainerEnvironmentVariables
— (map<String>
)The environment variables to set in the container
-
(AWS.Response)
—
Returns:
describeArtifact(params = {}, callback) ⇒ AWS.Request
Describes an artifact.
Service Reference:
Examples:
Calling the describeArtifact operation
var params = { ArtifactArn: 'STRING_VALUE' /* required */ }; sagemaker.describeArtifact(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ArtifactArn
— (String
)The Amazon Resource Name (ARN) of the artifact to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ArtifactName
— (String
)The name of the artifact.
ArtifactArn
— (String
)The Amazon Resource Name (ARN) of the artifact.
Source
— (map
)The source of the artifact.
SourceUri
— required — (String
)The URI of the source.
SourceTypes
— (Array<map>
)A list of source types.
SourceIdType
— required — (String
)The type of ID.
Possible values include:"MD5Hash"
"S3ETag"
"S3Version"
"Custom"
Value
— required — (String
)The ID.
ArtifactType
— (String
)The type of the artifact.
Properties
— (map<String>
)A list of the artifact's properties.
CreationTime
— (Date
)When the artifact was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)When the artifact was last modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
LineageGroupArn
— (String
)The Amazon Resource Name (ARN) of the lineage group.
-
(AWS.Response)
—
Returns:
describeAutoMLJob(params = {}, callback) ⇒ AWS.Request
Returns information about an AutoML job created by calling CreateAutoMLJob.
Note: AutoML jobs created by calling CreateAutoMLJobV2 cannot be described byDescribeAutoMLJob
.Service Reference:
Examples:
Calling the describeAutoMLJob operation
var params = { AutoMLJobName: 'STRING_VALUE' /* required */ }; sagemaker.describeAutoMLJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AutoMLJobName
— (String
)Requests information about an AutoML job using its unique name.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AutoMLJobName
— (String
)Returns the name of the AutoML job.
AutoMLJobArn
— (String
)Returns the ARN of the AutoML job.
InputDataConfig
— (Array<map>
)Returns the input data configuration for the AutoML job.
DataSource
— (map
)The data source for an AutoML channel.
S3DataSource
— required — (map
)The Amazon S3 location of the input data.
S3DataType
— required — (String
)The data type.
-
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.The
S3Prefix
should have the following format:s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
-
If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.A
ManifestFile
should have the format shown below:[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",
... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
-
If you choose
AugmentedManifestFile
,S3Uri
identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
is available for V2 API jobs only (for example, for jobs created by callingCreateAutoMLJobV2
).Here is a minimal, single-record example of an
AugmentedManifestFile
:{"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",
"label-metadata": {"class-name": "cat"
}For more information on
AugmentedManifestFile
, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
-
S3Uri
— required — (String
)The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
CompressionType
— (String
)You can use
Possible values include:Gzip
orNone
. The default value isNone
."None"
"Gzip"
TargetAttributeName
— required — (String
)The name of the target variable in supervised learning, usually represented by 'y'.
ContentType
— (String
)The content type of the data from the input source. You can use
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
.ChannelType
— (String
)The channel type (optional) is an
Possible values include:enum
string. The default value istraining
. Channels for training and validation must share the sameContentType
andTargetAttributeName
. For information on specifying training and validation channel types, see How to specify training and validation datasets."training"
"validation"
SampleWeightAttributeName
— (String
)If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
OutputDataConfig
— (map
)Returns the job's output data config.
KmsKeyId
— (String
)The Key Management Service encryption key ID.
S3OutputPath
— required — (String
)The Amazon S3 output path. Must be 512 characters or less.
RoleArn
— (String
)The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
AutoMLJobObjective
— (map
)Returns the job's objective.
MetricName
— required — (String
)The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
-
For tabular problem types:
-
List of available metrics:
-
Regression:
MAE
,MSE
,R2
,RMSE
-
Binary classification:
Accuracy
,AUC
,BalancedAccuracy
,F1
,Precision
,Recall
-
Multiclass classification:
Accuracy
,BalancedAccuracy
,F1macro
,PrecisionMacro
,RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression.
-
-
Default objective metrics:
-
Regression:
MSE
. -
Binary classification:
F1
. -
Multiclass classification:
Accuracy
.
-
-
-
For image or text classification problem types:
-
List of available metrics:
Accuracy
For a description of each metric, see Autopilot metrics for text and image classification.
-
Default objective metrics:
Accuracy
-
-
For time-series forecasting problem types:
-
List of available metrics:
RMSE
,wQL
,Average wQL
,MASE
,MAPE
,WAPE
For a description of each metric, see Autopilot metrics for time-series forecasting.
-
Default objective metrics:
AverageWeightedQuantileLoss
-
-
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the
AutoMLJobObjective
field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
-
ProblemType
— (String
)Returns the job's problem type.
Possible values include:"BinaryClassification"
"MulticlassClassification"
"Regression"
AutoMLJobConfig
— (map
)Returns the configuration for the AutoML job.
CompletionCriteria
— (map
)How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
SecurityConfig
— (map
)The security configuration for traffic encryption or Amazon VPC settings.
VolumeKmsKeyId
— (String
)The key used to encrypt stored data.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to use traffic encryption between the container layers.
VpcConfig
— (map
)The VPC configuration.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
CandidateGenerationConfig
— (map
)The configuration for generating a candidate for an AutoML job (optional).
FeatureSpecificationS3Uri
— (String
)A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input
FeatureAttributeNames
(optional) in JSON format as shown below:{ "FeatureAttributeNames":["col1", "col2", ...] }
.You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Note: These column keys may not include the target column.In ensembling mode, Autopilot only supports the following data types:
numeric
,categorical
,text
, anddatetime
. In HPO mode, Autopilot can supportnumeric
,categorical
,text
,datetime
, andsequence
.If only
FeatureDataTypes
is provided, the column keys (col1
,col2
,..) should be a subset of the column names in the input data.If both
FeatureDataTypes
andFeatureAttributeNames
are provided, then the column keys should be a subset of the column names provided inFeatureAttributeNames
.The key name
FeatureAttributeNames
is fixed. The values listed in["col1", "col2", ...]
are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.AlgorithmsConfig
— (Array<map>
)Stores the configuration information for the selection of algorithms trained on tabular data.
The list of available algorithms to choose from depends on the training mode set in
TabularJobConfig.Mode
.-
AlgorithmsConfig
should not be set if the training mode is set onAUTO
. -
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for the given training mode. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for the given training mode.
For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.
AutoMLAlgorithms
— required — (Array<String>
)The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
-
For the tabular problem type
TabularJobConfig
:Note: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING
orHYPERPARAMETER_TUNING
). Choose a minimum of 1 algorithm.-
In
ENSEMBLING
mode:-
"catboost"
-
"extra-trees"
-
"fastai"
-
"lightgbm"
-
"linear-learner"
-
"nn-torch"
-
"randomforest"
-
"xgboost"
-
-
In
HYPERPARAMETER_TUNING
mode:-
"linear-learner"
-
"mlp"
-
"xgboost"
-
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
:-
Choose your algorithms from this list.
-
"cnn-qr"
-
"deepar"
-
"prophet"
-
"arima"
-
"npts"
-
"ets"
-
-
-
-
DataSplitConfig
— (map
)The configuration for splitting the input training dataset.
Type: AutoMLDataSplitConfig
ValidationFraction
— (Float
)The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
Mode
— (String
)The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting
AUTO
. InAUTO
mode, Autopilot choosesENSEMBLING
for datasets smaller than 100 MB, andHYPERPARAMETER_TUNING
for larger ones.The
ENSEMBLING
mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported byENSEMBLING
mode.The
Possible values include:HYPERPARAMETER_TUNING
(HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported byHYPERPARAMETER_TUNING
mode."AUTO"
"ENSEMBLING"
"HYPERPARAMETER_TUNING"
CreationTime
— (Date
)Returns the creation time of the AutoML job.
EndTime
— (Date
)Returns the end time of the AutoML job.
LastModifiedTime
— (Date
)Returns the job's last modified time.
FailureReason
— (String
)Returns the failure reason for an AutoML job, when applicable.
PartialFailureReasons
— (Array<map>
)Returns a list of reasons for partial failures within an AutoML job.
PartialFailureMessage
— (String
)The message containing the reason for a partial failure of an AutoML job.
BestCandidate
— (map
)The best model candidate selected by SageMaker Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.
CandidateName
— required — (String
)The name of the candidate.
FinalAutoMLJobObjectiveMetric
— (map
)The best candidate result from an AutoML training job.
Type
— (String
)The type of metric with the best result.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.
Possible values include:"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
Value
— required — (Float
)The value of the metric with the best result.
StandardMetricName
— (String
)The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics.
Possible values include:"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
ObjectiveStatus
— required — (String
)The objective's status.
Possible values include:"Succeeded"
"Pending"
"Failed"
CandidateSteps
— required — (Array<map>
)Information about the candidate's steps.
CandidateStepType
— required — (String
)Whether the candidate is at the transform, training, or processing step.
Possible values include:"AWS::SageMaker::TrainingJob"
"AWS::SageMaker::TransformJob"
"AWS::SageMaker::ProcessingJob"
CandidateStepArn
— required — (String
)The ARN for the candidate's step.
CandidateStepName
— required — (String
)The name for the candidate's step.
CandidateStatus
— required — (String
)The candidate's status.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
InferenceContainers
— (Array<map>
)Information about the recommended inference container definitions.
Image
— required — (String
)The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.
ModelDataUrl
— required — (String
)The location of the model artifacts. For more information, see ContainerDefinition.
Environment
— (map<String>
)The environment variables to set in the container. For more information, see ContainerDefinition.
CreationTime
— required — (Date
)The creation time.
EndTime
— (Date
)The end time.
LastModifiedTime
— required — (Date
)The last modified time.
FailureReason
— (String
)The failure reason.
CandidateProperties
— (map
)The properties of an AutoML candidate job.
CandidateArtifactLocations
— (map
)The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
Explainability
— required — (String
)The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
ModelInsights
— (String
)The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
BacktestResults
— (String
)The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.
CandidateMetrics
— (Array<map>
)Information about the candidate metrics for an AutoML job.
MetricName
— (String
)The name of the metric.
Possible values include:"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
Value
— (Float
)The value of the metric.
Set
— (String
)The dataset split from which the AutoML job produced the metric.
Possible values include:"Train"
"Validation"
"Test"
StandardMetricName
— (String
)The name of the standard metric.
Note: For definitions of the standard metrics, seePossible values include:Autopilot candidate metrics
."Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"MAE"
"R2"
"BalancedAccuracy"
"Precision"
"PrecisionMacro"
"Recall"
"RecallMacro"
"LogLoss"
"InferenceLatency"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
"Rouge1"
"Rouge2"
"RougeL"
"RougeLSum"
"Perplexity"
"ValidationLoss"
"TrainingLoss"
InferenceContainerDefinitions
— (map<Array<map>>
)The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling
CreateAutoMLJobV2
) related to image or text classification problem types only.Image
— required — (String
)The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.
ModelDataUrl
— required — (String
)The location of the model artifacts. For more information, see ContainerDefinition.
Environment
— (map<String>
)The environment variables to set in the container. For more information, see ContainerDefinition.
AutoMLJobStatus
— (String
)Returns the status of the AutoML job.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
AutoMLJobSecondaryStatus
— (String
)Returns the secondary status of the AutoML job.
Possible values include:"Starting"
"MaxCandidatesReached"
"Failed"
"Stopped"
"MaxAutoMLJobRuntimeReached"
"Stopping"
"CandidateDefinitionsGenerated"
"Completed"
"ExplainabilityError"
"DeployingModel"
"ModelDeploymentError"
"GeneratingModelInsightsReport"
"ModelInsightsError"
"AnalyzingData"
"FeatureEngineering"
"ModelTuning"
"GeneratingExplainabilityReport"
"TrainingModels"
"PreTraining"
GenerateCandidateDefinitionsOnly
— (Boolean
)Indicates whether the output for an AutoML job generates candidate definitions only.
AutoMLJobArtifacts
— (map
)Returns information on the job's artifacts found in
AutoMLJobArtifacts
.CandidateDefinitionNotebookLocation
— (String
)The URL of the notebook location.
DataExplorationNotebookLocation
— (String
)The URL of the notebook location.
ResolvedAttributes
— (map
)Contains
ProblemType
,AutoMLJobObjective
, andCompletionCriteria
. If you do not provide these values, they are inferred.AutoMLJobObjective
— (map
)Specifies a metric to minimize or maximize as the objective of an AutoML job.
MetricName
— required — (String
)The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
-
For tabular problem types:
-
List of available metrics:
-
Regression:
MAE
,MSE
,R2
,RMSE
-
Binary classification:
Accuracy
,AUC
,BalancedAccuracy
,F1
,Precision
,Recall
-
Multiclass classification:
Accuracy
,BalancedAccuracy
,F1macro
,PrecisionMacro
,RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression.
-
-
Default objective metrics:
-
Regression:
MSE
. -
Binary classification:
F1
. -
Multiclass classification:
Accuracy
.
-
-
-
For image or text classification problem types:
-
List of available metrics:
Accuracy
For a description of each metric, see Autopilot metrics for text and image classification.
-
Default objective metrics:
Accuracy
-
-
For time-series forecasting problem types:
-
List of available metrics:
RMSE
,wQL
,Average wQL
,MASE
,MAPE
,WAPE
For a description of each metric, see Autopilot metrics for time-series forecasting.
-
Default objective metrics:
AverageWeightedQuantileLoss
-
-
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the
AutoMLJobObjective
field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
-
ProblemType
— (String
)The problem type.
Possible values include:"BinaryClassification"
"MulticlassClassification"
"Regression"
CompletionCriteria
— (map
)How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ModelDeployConfig
— (map
)Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
AutoGenerateEndpointName
— (Boolean
)Set to
True
to automatically generate an endpoint name for a one-click Autopilot model deployment; set toFalse
otherwise. The default value isFalse
.Note: If you setAutoGenerateEndpointName
toTrue
, do not specify theEndpointName
; otherwise a 400 error is thrown.EndpointName
— (String
)Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note: Specify theEndpointName
if and only if you setAutoGenerateEndpointName
toFalse
; otherwise a 400 error is thrown.
ModelDeployResult
— (map
)Provides information about endpoint for the model deployment.
EndpointName
— (String
)The name of the endpoint to which the model has been deployed.
Note: If model deployment fails, this field is omitted from the response.
-
(AWS.Response)
—
Returns:
describeAutoMLJobV2(params = {}, callback) ⇒ AWS.Request
Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
Service Reference:
Examples:
Calling the describeAutoMLJobV2 operation
var params = { AutoMLJobName: 'STRING_VALUE' /* required */ }; sagemaker.describeAutoMLJobV2(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AutoMLJobName
— (String
)Requests information about an AutoML job V2 using its unique name.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AutoMLJobName
— (String
)Returns the name of the AutoML job V2.
AutoMLJobArn
— (String
)Returns the Amazon Resource Name (ARN) of the AutoML job V2.
AutoMLJobInputDataConfig
— (Array<map>
)Returns an array of channel objects describing the input data and their location.
ChannelType
— (String
)The type of channel. Defines whether the data are used for training or validation. The default value is
training
. Channels fortraining
andvalidation
must share the sameContentType
Note: The type of channel defaults toPossible values include:training
for the time-series forecasting problem type."training"
"validation"
ContentType
— (String
)The content type of the data from the input source. The following are the allowed content types for different problems:
-
For tabular problem types:
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
. -
For image classification:
image/png
,image/jpeg
, orimage/*
. The default value isimage/*
. -
For text classification:
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
. -
For time-series forecasting:
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
. -
For text generation (LLMs fine-tuning):
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
.
-
CompressionType
— (String
)The allowed compression types depend on the input format and problem type. We allow the compression type
Possible values include:Gzip
forS3Prefix
inputs on tabular data only. For all other inputs, the compression type should beNone
. If no compression type is provided, we default toNone
."None"
"Gzip"
DataSource
— (map
)The data source for an AutoML channel (Required).
S3DataSource
— required — (map
)The Amazon S3 location of the input data.
S3DataType
— required — (String
)The data type.
-
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.The
S3Prefix
should have the following format:s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
-
If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.A
ManifestFile
should have the format shown below:[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",
"DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",
... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
-
If you choose
AugmentedManifestFile
,S3Uri
identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
is available for V2 API jobs only (for example, for jobs created by callingCreateAutoMLJobV2
).Here is a minimal, single-record example of an
AugmentedManifestFile
:{"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",
"label-metadata": {"class-name": "cat"
}For more information on
AugmentedManifestFile
, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
-
S3Uri
— required — (String
)The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
OutputDataConfig
— (map
)Returns the job's output data config.
KmsKeyId
— (String
)The Key Management Service encryption key ID.
S3OutputPath
— required — (String
)The Amazon S3 output path. Must be 512 characters or less.
RoleArn
— (String
)The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
AutoMLJobObjective
— (map
)Returns the job's objective.
MetricName
— required — (String
)The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
-
For tabular problem types:
-
List of available metrics:
-
Regression:
MAE
,MSE
,R2
,RMSE
-
Binary classification:
Accuracy
,AUC
,BalancedAccuracy
,F1
,Precision
,Recall
-
Multiclass classification:
Accuracy
,BalancedAccuracy
,F1macro
,PrecisionMacro
,RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression.
-
-
Default objective metrics:
-
Regression:
MSE
. -
Binary classification:
F1
. -
Multiclass classification:
Accuracy
.
-
-
-
For image or text classification problem types:
-
List of available metrics:
Accuracy
For a description of each metric, see Autopilot metrics for text and image classification.
-
Default objective metrics:
Accuracy
-
-
For time-series forecasting problem types:
-
List of available metrics:
RMSE
,wQL
,Average wQL
,MASE
,MAPE
,WAPE
For a description of each metric, see Autopilot metrics for time-series forecasting.
-
Default objective metrics:
AverageWeightedQuantileLoss
-
-
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the
AutoMLJobObjective
field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
-
AutoMLProblemTypeConfig
— (map
)Returns the configuration settings of the problem type set for the AutoML job V2.
ImageClassificationJobConfig
— (map
)Settings used to configure an AutoML job V2 for the image classification problem type.
CompletionCriteria
— (map
)How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
TextClassificationJobConfig
— (map
)Settings used to configure an AutoML job V2 for the text classification problem type.
CompletionCriteria
— (map
)How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ContentColumn
— required — (String
)The name of the column used to provide the sentences to be classified. It should not be the same as the target column.
TargetLabelColumn
— required — (String
)The name of the column used to provide the class labels. It should not be same as the content column.
TimeSeriesForecastingJobConfig
— (map
)Settings used to configure an AutoML job V2 for the time-series forecasting problem type.
FeatureSpecificationS3Uri
— (String
)A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in
TimeSeriesConfig
. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared inTimeSeriesConfig
. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared inTimeSeriesConfig
.You can input
FeatureAttributeNames
(optional) in JSON format as shown below:{ "FeatureAttributeNames":["col1", "col2", ...] }
.You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Autopilot supports the following data types:
numeric
,categorical
,text
, anddatetime
.Note: These column keys must not include any column set inTimeSeriesConfig
.CompletionCriteria
— (map
)How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ForecastFrequency
— required — (String
)The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example,
1D
indicates every day and15min
indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of1H
instead of60min
.The valid values for each frequency are the following:
-
Minute - 1-59
-
Hour - 1-23
-
Day - 1-6
-
Week - 1-4
-
Month - 1-11
-
Year - 1
-
ForecastHorizon
— required — (Integer
)The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
ForecastQuantiles
— (Array<String>
)The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from
0.01
(p1) to0.99
(p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. WhenForecastQuantiles
is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.Transformations
— (map
)The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
Filling
— (map<map<String>>
)A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.
The supported filling methods and their corresponding options are:
-
frontfill
:none
(Supported only for target column) -
middlefill
:zero
,value
,median
,mean
,min
,max
-
backfill
:zero
,value
,median
,mean
,min
,max
-
futurefill
:zero
,value
,median
,mean
,min
,max
To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example
"backfill" : "value"
), and define the filling value in an additional parameter prefixed with "_value". For example, to setbackfill
to a value of2
, you must include two parameters:"backfill": "value"
and"backfill_value":"2"
.-
Aggregation
— (map<String>
)A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.
The supported aggregation methods are
sum
(default),avg
,first
,min
,max
.Note: Aggregation is only supported for the target column.
TimeSeriesConfig
— required — (map
)The collection of components that defines the time-series.
TargetAttributeName
— required — (String
)The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.
TimestampAttributeName
— required — (String
)The name of the column indicating a point in time at which the target value of a given item is recorded.
ItemIdentifierAttributeName
— required — (String
)The name of the column that represents the set of item identifiers for which you want to predict the target value.
GroupingAttributeNames
— (Array<String>
)A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.
HolidayConfig
— (Array<map>
)The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.
CountryCode
— (String
)The country code for the holiday calendar.
For the list of public holiday calendars supported by AutoML job V2, see Country Codes. Use the country code corresponding to the country of your choice.
CandidateGenerationConfig
— (map
)Stores the configuration information for how model candidates are generated using an AutoML job V2.
AlgorithmsConfig
— (Array<map>
)Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig
stores the customized selection of algorithms to train on your data.-
For the tabular problem type
TabularJobConfig
, the list of available algorithms to choose from depends on the training mode set inAutoMLJobConfig.Mode
.-
AlgorithmsConfig
should not be set when the training modeAutoMLJobConfig.Mode
is set toAUTO
. -
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for the given training mode. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
, choose your algorithms from the list provided in AlgorithmConfig.For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
-
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting.
-
AutoMLAlgorithms
— required — (Array<String>
)The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
-
For the tabular problem type
TabularJobConfig
:Note: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING
orHYPERPARAMETER_TUNING
). Choose a minimum of 1 algorithm.-
In
ENSEMBLING
mode:-
"catboost"
-
"extra-trees"
-
"fastai"
-
"lightgbm"
-
"linear-learner"
-
"nn-torch"
-
"randomforest"
-
"xgboost"
-
-
In
HYPERPARAMETER_TUNING
mode:-
"linear-learner"
-
"mlp"
-
"xgboost"
-
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
:-
Choose your algorithms from this list.
-
"cnn-qr"
-
"deepar"
-
"prophet"
-
"arima"
-
"npts"
-
"ets"
-
-
-
-
TabularJobConfig
— (map
)Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).
CandidateGenerationConfig
— (map
)The configuration information of how model candidates are generated.
AlgorithmsConfig
— (Array<map>
)Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.
AlgorithmsConfig
stores the customized selection of algorithms to train on your data.-
For the tabular problem type
TabularJobConfig
, the list of available algorithms to choose from depends on the training mode set inAutoMLJobConfig.Mode
.-
AlgorithmsConfig
should not be set when the training modeAutoMLJobConfig.Mode
is set toAUTO
. -
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for the given training mode. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for the given training mode.
For the list of all algorithms per training mode, see AlgorithmConfig.
For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
, choose your algorithms from the list provided in AlgorithmConfig.For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.
-
When
AlgorithmsConfig
is provided, oneAutoMLAlgorithms
attribute must be set and one only.If the list of algorithms provided as values for
AutoMLAlgorithms
is empty,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting. -
When
AlgorithmsConfig
is not provided,CandidateGenerationConfig
uses the full set of algorithms for time-series forecasting.
-
AutoMLAlgorithms
— required — (Array<String>
)The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
-
For the tabular problem type
TabularJobConfig
:Note: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING
orHYPERPARAMETER_TUNING
). Choose a minimum of 1 algorithm.-
In
ENSEMBLING
mode:-
"catboost"
-
"extra-trees"
-
"fastai"
-
"lightgbm"
-
"linear-learner"
-
"nn-torch"
-
"randomforest"
-
"xgboost"
-
-
In
HYPERPARAMETER_TUNING
mode:-
"linear-learner"
-
"mlp"
-
"xgboost"
-
-
-
For the time-series forecasting problem type
TimeSeriesForecastingJobConfig
:-
Choose your algorithms from this list.
-
"cnn-qr"
-
"deepar"
-
"prophet"
-
"arima"
-
"npts"
-
"ets"
-
-
-
-
CompletionCriteria
— (map
)How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
FeatureSpecificationS3Uri
— (String
)A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input
FeatureAttributeNames
(optional) in JSON format as shown below:{ "FeatureAttributeNames":["col1", "col2", ...] }
.You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Note: These column keys may not include the target column.In ensembling mode, Autopilot only supports the following data types:
numeric
,categorical
,text
, anddatetime
. In HPO mode, Autopilot can supportnumeric
,categorical
,text
,datetime
, andsequence
.If only
FeatureDataTypes
is provided, the column keys (col1
,col2
,..) should be a subset of the column names in the input data.If both
FeatureDataTypes
andFeatureAttributeNames
are provided, then the column keys should be a subset of the column names provided inFeatureAttributeNames
.The key name
FeatureAttributeNames
is fixed. The values listed in["col1", "col2", ...]
are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.Mode
— (String
)The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting
AUTO
. InAUTO
mode, Autopilot choosesENSEMBLING
for datasets smaller than 100 MB, andHYPERPARAMETER_TUNING
for larger ones.The
ENSEMBLING
mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported byENSEMBLING
mode.The
Possible values include:HYPERPARAMETER_TUNING
(HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported byHYPERPARAMETER_TUNING
mode."AUTO"
"ENSEMBLING"
"HYPERPARAMETER_TUNING"
GenerateCandidateDefinitionsOnly
— (Boolean
)Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
ProblemType
— (String
)The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.
Note: You must either specify the type of supervised learning problem inPossible values include:ProblemType
and provide the AutoMLJobObjective metric, or none at all."BinaryClassification"
"MulticlassClassification"
"Regression"
TargetAttributeName
— required — (String
)The name of the target variable in supervised learning, usually represented by 'y'.
SampleWeightAttributeName
— (String
)If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
TextGenerationJobConfig
— (map
)Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.
Note: The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.CompletionCriteria
— (map
)How long a fine-tuning job is allowed to run. For
TextGenerationJobConfig
problem types, theMaxRuntimePerTrainingJobInSeconds
attribute ofAutoMLJobCompletionCriteria
defaults to 72h (259200s).MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
BaseModelName
— (String
)The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no
BaseModelName
is provided, the default model used is Falcon7BInstruct.TextGenerationHyperParameters
— (map<String>
)The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.
-
"epochCount"
: The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10". -
"batchSize"
: The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64". -
"learningRate"
: The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1". -
"learningRateWarmupSteps"
: The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".
Here is an example where all four hyperparameters are configured.
{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }
-
ModelAccessConfig
— (map
)The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
.-
If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.
-
If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.
AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
-
AutoMLProblemTypeConfigName
— (String
)Returns the name of the problem type configuration set for the AutoML job V2.
Possible values include:"ImageClassification"
"TextClassification"
"TimeSeriesForecasting"
"Tabular"
"TextGeneration"
CreationTime
— (Date
)Returns the creation time of the AutoML job V2.
EndTime
— (Date
)Returns the end time of the AutoML job V2.
LastModifiedTime
— (Date
)Returns the job's last modified time.
FailureReason
— (String
)Returns the reason for the failure of the AutoML job V2, when applicable.
PartialFailureReasons
— (Array<map>
)Returns a list of reasons for partial failures within an AutoML job V2.
PartialFailureMessage
— (String
)The message containing the reason for a partial failure of an AutoML job.
BestCandidate
— (map
)Information about the candidate produced by an AutoML training job V2, including its status, steps, and other properties.
CandidateName
— required — (String
)The name of the candidate.
FinalAutoMLJobObjectiveMetric
— (map
)The best candidate result from an AutoML training job.
Type
— (String
)The type of metric with the best result.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.
Possible values include:"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
Value
— required — (Float
)The value of the metric with the best result.
StandardMetricName
— (String
)The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics.
Possible values include:"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
ObjectiveStatus
— required — (String
)The objective's status.
Possible values include:"Succeeded"
"Pending"
"Failed"
CandidateSteps
— required — (Array<map>
)Information about the candidate's steps.
CandidateStepType
— required — (String
)Whether the candidate is at the transform, training, or processing step.
Possible values include:"AWS::SageMaker::TrainingJob"
"AWS::SageMaker::TransformJob"
"AWS::SageMaker::ProcessingJob"
CandidateStepArn
— required — (String
)The ARN for the candidate's step.
CandidateStepName
— required — (String
)The name for the candidate's step.
CandidateStatus
— required — (String
)The candidate's status.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
InferenceContainers
— (Array<map>
)Information about the recommended inference container definitions.
Image
— required — (String
)The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.
ModelDataUrl
— required — (String
)The location of the model artifacts. For more information, see ContainerDefinition.
Environment
— (map<String>
)The environment variables to set in the container. For more information, see ContainerDefinition.
CreationTime
— required — (Date
)The creation time.
EndTime
— (Date
)The end time.
LastModifiedTime
— required — (Date
)The last modified time.
FailureReason
— (String
)The failure reason.
CandidateProperties
— (map
)The properties of an AutoML candidate job.
CandidateArtifactLocations
— (map
)The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
Explainability
— required — (String
)The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
ModelInsights
— (String
)The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
BacktestResults
— (String
)The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.
CandidateMetrics
— (Array<map>
)Information about the candidate metrics for an AutoML job.
MetricName
— (String
)The name of the metric.
Possible values include:"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
Value
— (Float
)The value of the metric.
Set
— (String
)The dataset split from which the AutoML job produced the metric.
Possible values include:"Train"
"Validation"
"Test"
StandardMetricName
— (String
)The name of the standard metric.
Note: For definitions of the standard metrics, seePossible values include:Autopilot candidate metrics
."Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"MAE"
"R2"
"BalancedAccuracy"
"Precision"
"PrecisionMacro"
"Recall"
"RecallMacro"
"LogLoss"
"InferenceLatency"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
"Rouge1"
"Rouge2"
"RougeL"
"RougeLSum"
"Perplexity"
"ValidationLoss"
"TrainingLoss"
InferenceContainerDefinitions
— (map<Array<map>>
)The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling
CreateAutoMLJobV2
) related to image or text classification problem types only.Image
— required — (String
)The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.
ModelDataUrl
— required — (String
)The location of the model artifacts. For more information, see ContainerDefinition.
Environment
— (map<String>
)The environment variables to set in the container. For more information, see ContainerDefinition.
AutoMLJobStatus
— (String
)Returns the status of the AutoML job V2.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
AutoMLJobSecondaryStatus
— (String
)Returns the secondary status of the AutoML job V2.
Possible values include:"Starting"
"MaxCandidatesReached"
"Failed"
"Stopped"
"MaxAutoMLJobRuntimeReached"
"Stopping"
"CandidateDefinitionsGenerated"
"Completed"
"ExplainabilityError"
"DeployingModel"
"ModelDeploymentError"
"GeneratingModelInsightsReport"
"ModelInsightsError"
"AnalyzingData"
"FeatureEngineering"
"ModelTuning"
"GeneratingExplainabilityReport"
"TrainingModels"
"PreTraining"
AutoMLJobArtifacts
— (map
)The artifacts that are generated during an AutoML job.
CandidateDefinitionNotebookLocation
— (String
)The URL of the notebook location.
DataExplorationNotebookLocation
— (String
)The URL of the notebook location.
ResolvedAttributes
— (map
)Returns the resolved attributes used by the AutoML job V2.
AutoMLJobObjective
— (map
)Specifies a metric to minimize or maximize as the objective of an AutoML job.
MetricName
— required — (String
)The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
-
For tabular problem types:
-
List of available metrics:
-
Regression:
MAE
,MSE
,R2
,RMSE
-
Binary classification:
Accuracy
,AUC
,BalancedAccuracy
,F1
,Precision
,Recall
-
Multiclass classification:
Accuracy
,BalancedAccuracy
,F1macro
,PrecisionMacro
,RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression.
-
-
Default objective metrics:
-
Regression:
MSE
. -
Binary classification:
F1
. -
Multiclass classification:
Accuracy
.
-
-
-
For image or text classification problem types:
-
List of available metrics:
Accuracy
For a description of each metric, see Autopilot metrics for text and image classification.
-
Default objective metrics:
Accuracy
-
-
For time-series forecasting problem types:
-
List of available metrics:
RMSE
,wQL
,Average wQL
,MASE
,MAPE
,WAPE
For a description of each metric, see Autopilot metrics for time-series forecasting.
-
Default objective metrics:
AverageWeightedQuantileLoss
-
-
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the
AutoMLJobObjective
field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
-
CompletionCriteria
— (map
)How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates
— (Integer
)The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds
— (Integer
)The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling
CreateAutoMLJobV2
), this field controls the runtime of the job candidate.For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds
— (Integer
)The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
AutoMLProblemTypeResolvedAttributes
— (map
)Defines the resolved attributes specific to a problem type.
TabularResolvedAttributes
— (map
)The resolved attributes for the tabular problem type.
ProblemType
— (String
)The type of supervised learning problem available for the model candidates of the AutoML job V2 (Binary Classification, Multiclass Classification, Regression). For more information, see SageMaker Autopilot problem types.
Possible values include:"BinaryClassification"
"MulticlassClassification"
"Regression"
TextGenerationResolvedAttributes
— (map
)The resolved attributes for the text generation problem type.
BaseModelName
— (String
)The name of the base model to fine-tune.
ModelDeployConfig
— (map
)Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
AutoGenerateEndpointName
— (Boolean
)Set to
True
to automatically generate an endpoint name for a one-click Autopilot model deployment; set toFalse
otherwise. The default value isFalse
.Note: If you setAutoGenerateEndpointName
toTrue
, do not specify theEndpointName
; otherwise a 400 error is thrown.EndpointName
— (String
)Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note: Specify theEndpointName
if and only if you setAutoGenerateEndpointName
toFalse
; otherwise a 400 error is thrown.
ModelDeployResult
— (map
)Provides information about endpoint for the model deployment.
EndpointName
— (String
)The name of the endpoint to which the model has been deployed.
Note: If model deployment fails, this field is omitted from the response.
DataSplitConfig
— (map
)Returns the configuration settings of how the data are split into train and validation datasets.
ValidationFraction
— (Float
)The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
SecurityConfig
— (map
)Returns the security configuration for traffic encryption or Amazon VPC settings.
VolumeKmsKeyId
— (String
)The key used to encrypt stored data.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to use traffic encryption between the container layers.
VpcConfig
— (map
)The VPC configuration.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
AutoMLComputeConfig
— (map
)The compute configuration used for the AutoML job V2.
EmrServerlessComputeConfig
— (map
)The configuration for using EMR Serverless to run the AutoML job V2.
To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an
EmrServerlessComputeConfig
object, which includes anExecutionRoleARN
attribute, to theAutoMLComputeConfig
of the AutoML job V2 input request.By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you.
EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.
ExecutionRoleARN
— required — (String
)The ARN of the IAM role granting the AutoML job V2 the necessary permissions access policies to list, connect to, or manage EMR Serverless jobs. For detailed information about the required permissions of this role, see "How to configure AutoML to initiate a remote job on EMR Serverless for large datasets" in Create a regression or classification job for tabular data using the AutoML API or Create an AutoML job for time-series forecasting using the API.
-
(AWS.Response)
—
Returns:
describeCluster(params = {}, callback) ⇒ AWS.Request
Retrieves information of a SageMaker HyperPod cluster.
Service Reference:
Examples:
Calling the describeCluster operation
var params = { ClusterName: 'STRING_VALUE' /* required */ }; sagemaker.describeCluster(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ClusterName
— (String
)The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ClusterArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.
ClusterName
— (String
)The name of the SageMaker HyperPod cluster.
ClusterStatus
— (String
)The status of the SageMaker HyperPod cluster.
Possible values include:"Creating"
"Deleting"
"Failed"
"InService"
"RollingBack"
"SystemUpdating"
"Updating"
CreationTime
— (Date
)The time when the SageMaker Cluster is created.
FailureMessage
— (String
)The failure message of the SageMaker HyperPod cluster.
InstanceGroups
— (Array<map>
)The instance groups of the SageMaker HyperPod cluster.
CurrentCount
— (Integer
)The number of instances that are currently in the instance group of a SageMaker HyperPod cluster.
TargetCount
— (Integer
)The number of instances you specified to add to the instance group of a SageMaker HyperPod cluster.
InstanceGroupName
— (String
)The name of the instance group of a SageMaker HyperPod cluster.
InstanceType
— (String
)The instance type of the instance group of a SageMaker HyperPod cluster.
Possible values include:"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.c5n.large"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
LifeCycleConfig
— (map
)Details of LifeCycle configuration for the instance group.
SourceS3Uri
— required — (String
)An Amazon S3 bucket path where your lifecycle scripts are stored.
Make sure that the S3 bucket path starts with
s3://sagemaker-
. The IAM role for SageMaker HyperPod has the managedAmazonSageMakerClusterInstanceRolePolicy
attached, which allows access to S3 buckets with the specific prefixsagemaker-
.OnCreate
— required — (String
)The file name of the entrypoint script of lifecycle scripts under
SourceS3Uri
. This entrypoint script runs during cluster creation.
ExecutionRole
— (String
)The execution role for the instance group to assume.
ThreadsPerCore
— (Integer
)The number you specified to
TreadsPerCore
inCreateCluster
for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.InstanceStorageConfigs
— (Array<map>
)The additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.
EbsVolumeConfig
— (map
)Defines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to
/opt/sagemaker
.VolumeSizeInGB
— required — (Integer
)The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to
/opt/sagemaker
.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
-
(AWS.Response)
—
Returns:
describeClusterNode(params = {}, callback) ⇒ AWS.Request
Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
Service Reference:
Examples:
Calling the describeClusterNode operation
var params = { ClusterName: 'STRING_VALUE', /* required */ NodeId: 'STRING_VALUE' /* required */ }; sagemaker.describeClusterNode(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ClusterName
— (String
)The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which the node is.
NodeId
— (String
)The ID of the SageMaker HyperPod cluster node.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NodeDetails
— (map
)The details of the SageMaker HyperPod cluster node.
InstanceGroupName
— (String
)The instance group name in which the instance is.
InstanceId
— (String
)The ID of the instance.
InstanceStatus
— (map
)The status of the instance.
Status
— required — (String
)The status of an instance in a SageMaker HyperPod cluster.
Possible values include:"Running"
"Failure"
"Pending"
"ShuttingDown"
"SystemUpdating"
Message
— (String
)The message from an instance in a SageMaker HyperPod cluster.
InstanceType
— (String
)The type of the instance.
Possible values include:"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.c5n.large"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
LaunchTime
— (Date
)The time when the instance is launched.
LifeCycleConfig
— (map
)The LifeCycle configuration applied to the instance.
SourceS3Uri
— required — (String
)An Amazon S3 bucket path where your lifecycle scripts are stored.
Make sure that the S3 bucket path starts with
s3://sagemaker-
. The IAM role for SageMaker HyperPod has the managedAmazonSageMakerClusterInstanceRolePolicy
attached, which allows access to S3 buckets with the specific prefixsagemaker-
.OnCreate
— required — (String
)The file name of the entrypoint script of lifecycle scripts under
SourceS3Uri
. This entrypoint script runs during cluster creation.
ThreadsPerCore
— (Integer
)The number of threads per CPU core you specified under
CreateCluster
.InstanceStorageConfigs
— (Array<map>
)The configurations of additional storage specified to the instance group where the instance (node) is launched.
EbsVolumeConfig
— (map
)Defines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to
/opt/sagemaker
.VolumeSizeInGB
— required — (Integer
)The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to
/opt/sagemaker
.
PrivatePrimaryIp
— (String
)The private primary IP address of the SageMaker HyperPod cluster node.
PrivateDnsHostname
— (String
)The private DNS hostname of the SageMaker HyperPod cluster node.
Placement
— (map
)The placement details of the SageMaker HyperPod cluster node.
AvailabilityZone
— (String
)The Availability Zone where the node in the SageMaker HyperPod cluster is launched.
AvailabilityZoneId
— (String
)The unique identifier (ID) of the Availability Zone where the node in the SageMaker HyperPod cluster is launched.
-
(AWS.Response)
—
Returns:
describeCodeRepository(params = {}, callback) ⇒ AWS.Request
Gets details about the specified Git repository.
Service Reference:
Examples:
Calling the describeCodeRepository operation
var params = { CodeRepositoryName: 'STRING_VALUE' /* required */ }; sagemaker.describeCodeRepository(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CodeRepositoryName
— (String
)The name of the Git repository to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CodeRepositoryName
— (String
)The name of the Git repository.
CodeRepositoryArn
— (String
)The Amazon Resource Name (ARN) of the Git repository.
CreationTime
— (Date
)The date and time that the repository was created.
LastModifiedTime
— (Date
)The date and time that the repository was last changed.
GitConfig
— (map
)Configuration details about the repository, including the URL where the repository is located, the default branch, and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
RepositoryUrl
— required — (String
)The URL where the Git repository is located.
Branch
— (String
)The default branch for the Git repository.
SecretArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of
AWSCURRENT
and must be in the following format:{"username": UserName, "password": Password}
-
(AWS.Response)
—
Returns:
describeCompilationJob(params = {}, callback) ⇒ AWS.Request
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
Service Reference:
Examples:
Calling the describeCompilationJob operation
var params = { CompilationJobName: 'STRING_VALUE' /* required */ }; sagemaker.describeCompilationJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CompilationJobName
— (String
)The name of the model compilation job that you want information about.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CompilationJobName
— (String
)The name of the model compilation job.
CompilationJobArn
— (String
)The Amazon Resource Name (ARN) of the model compilation job.
CompilationJobStatus
— (String
)The status of the model compilation job.
Possible values include:"INPROGRESS"
"COMPLETED"
"FAILED"
"STARTING"
"STOPPING"
"STOPPED"
CompilationStartTime
— (Date
)The time when the model compilation job started the
CompilationJob
instances.You are billed for the time between this timestamp and the timestamp in the
CompilationEndTime
field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container.CompilationEndTime
— (Date
)The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.
StoppingCondition
— (map
)Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
InferenceImage
— (String
)The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance.
ModelPackageVersionArn
— (String
)The Amazon Resource Name (ARN) of the versioned model package that was provided to SageMaker Neo when you initiated a compilation job.
CreationTime
— (Date
)The time that the model compilation job was created.
LastModifiedTime
— (Date
)The time that the status of the model compilation job was last modified.
FailureReason
— (String
)If a model compilation job failed, the reason it failed.
ModelArtifacts
— (map
)Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.
S3ModelArtifacts
— required — (String
)The path of the S3 object that contains the model artifacts. For example,
s3://bucket-name/keynameprefix/model.tar.gz
.
ModelDigests
— (map
)Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.
ArtifactDigest
— (String
)Provides a hash value that uniquely identifies the stored model artifacts.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.
InputConfig
— (map
)Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
S3Uri
— required — (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
DataInputConfig
— (String
)Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are
Framework
specific.-
TensorFlow
: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"input":[1,1024,1024,3]}
-
If using the CLI,
{\"input\":[1,1024,1024,3]}
-
-
Examples for two inputs:
-
If using the console,
{"data1": [1,28,28,1], "data2":[1,28,28,1]}
-
If using the CLI,
{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
-
-
-
KERAS
: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format,DataInputConfig
should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"input_1":[1,3,224,224]}
-
If using the CLI,
{\"input_1\":[1,3,224,224]}
-
-
Examples for two inputs:
-
If using the console,
{"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
-
If using the CLI,
{\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
-
-
-
MXNET/ONNX/DARKNET
: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"data":[1,3,1024,1024]}
-
If using the CLI,
{\"data\":[1,3,1024,1024]}
-
-
Examples for two inputs:
-
If using the console,
{"var1": [1,1,28,28], "var2":[1,1,28,28]}
-
If using the CLI,
{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
-
-
-
PyTorch
: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.-
Examples for one input in dictionary format:
-
If using the console,
{"input0":[1,3,224,224]}
-
If using the CLI,
{\"input0\":[1,3,224,224]}
-
-
Example for one input in list format:
[[1,3,224,224]]
-
Examples for two inputs in dictionary format:
-
If using the console,
{"input0":[1,3,224,224], "input1":[1,3,224,224]}
-
If using the CLI,
{\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
-
-
Example for two inputs in list format:
[[1,3,224,224], [1,3,224,224]]
-
-
XGBOOST
: input data name and shape are not needed.
DataInputConfig
supports the following parameters forCoreML
TargetDevice
(ML Model format):-
shape
: Input shape, for example{"input_1": {"shape": [1,224,224,3]}}
. In addition to static input shapes, CoreML converter supports Flexible input shapes:-
Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example:
{"input_1": {"shape": ["1..10", 224, 224, 3]}}
-
Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example:
{"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
-
-
default_shape
: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example{"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
-
type
: Input type. Allowed values:Image
andTensor
. By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such asbias
andscale
. -
bias
: If the input type is an Image, you need to provide the bias vector. -
scale
: If the input type is an Image, you need to provide a scale factor.
CoreML
ClassifierConfig
parameters can be specified using OutputConfigCompilerOptions
. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:-
Tensor type input:
-
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
-
-
Tensor type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
-
-
Image type input:
-
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
-
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
-
-
Image type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
-
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
-
Depending on the model format,
DataInputConfig
requires the following parameters forml_eia2
OutputConfig:TargetDevice.-
For TensorFlow models saved in the SavedModel format, specify the input names from
signature_def_key
and the input model shapes forDataInputConfig
. Specify thesignature_def_key
inOutputConfig:CompilerOptions
if the model does not use TensorFlow's default signature def key. For example:-
"DataInputConfig": {"inputs": [1, 224, 224, 3]}
-
"CompilerOptions": {"signature_def_key": "serving_custom"}
-
-
For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
DataInputConfig
and the output tensor names foroutput_names
inOutputConfig:CompilerOptions
. For example:-
"DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
-
"CompilerOptions": {"output_names": ["output_tensor:0"]}
-
-
Framework
— required — (String
)Identifies the framework in which the model was trained. For example: TENSORFLOW.
Possible values include:"TENSORFLOW"
"KERAS"
"MXNET"
"ONNX"
"PYTORCH"
"XGBOOST"
"TFLITE"
"DARKNET"
"SKLEARN"
FrameworkVersion
— (String
)Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.
For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.
OutputConfig
— (map
)Information about the output location for the compiled model and the target device that the model runs on.
S3OutputLocation
— required — (String
)Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.TargetDevice
— (String
)Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of
TargetPlatform
.Note: CurrentlyPossible values include:ml_trn1
is available only in US East (N. Virginia) Region, andml_inf2
is available only in US East (Ohio) Region."lambda"
"ml_m4"
"ml_m5"
"ml_m6g"
"ml_c4"
"ml_c5"
"ml_c6g"
"ml_p2"
"ml_p3"
"ml_g4dn"
"ml_inf1"
"ml_inf2"
"ml_trn1"
"ml_eia2"
"jetson_tx1"
"jetson_tx2"
"jetson_nano"
"jetson_xavier"
"rasp3b"
"rasp4b"
"imx8qm"
"deeplens"
"rk3399"
"rk3288"
"aisage"
"sbe_c"
"qcs605"
"qcs603"
"sitara_am57x"
"amba_cv2"
"amba_cv22"
"amba_cv25"
"x86_win32"
"x86_win64"
"coreml"
"jacinto_tda4vm"
"imx8mplus"
TargetPlatform
— (map
)Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of
TargetDevice
.The following examples show how to configure the
TargetPlatform
andCompilerOptions
JSON strings for popular target platforms:-
Raspberry Pi 3 Model B+
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
"CompilerOptions": {'mattr': ['+neon']}
-
Jetson TX2
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
-
EC2 m5.2xlarge instance OS
"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'mcpu': 'skylake-avx512'}
-
RK3399
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
-
ARMv7 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
"CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
-
ARMv8 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
"CompilerOptions": {'ANDROID_PLATFORM': 29}
Os
— required — (String
)Specifies a target platform OS.
-
LINUX
: Linux-based operating systems. -
ANDROID
: Android operating systems. Android API level can be specified using theANDROID_PLATFORM
compiler option. For example,"CompilerOptions": {'ANDROID_PLATFORM': 28}
"ANDROID"
"LINUX"
-
Arch
— required — (String
)Specifies a target platform architecture.
-
X86_64
: 64-bit version of the x86 instruction set. -
X86
: 32-bit version of the x86 instruction set. -
ARM64
: ARMv8 64-bit CPU. -
ARM_EABIHF
: ARMv7 32-bit, Hard Float. -
ARM_EABI
: ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
"X86_64"
"X86"
"ARM64"
"ARM_EABI"
"ARM_EABIHF"
-
Accelerator
— (String
)Specifies a target platform accelerator (optional).
-
NVIDIA
: Nvidia graphics processing unit. It also requiresgpu-code
,trt-ver
,cuda-ver
compiler options -
MALI
: ARM Mali graphics processor -
INTEL_GRAPHICS
: Integrated Intel graphics
"INTEL_GRAPHICS"
"MALI"
"NVIDIA"
"NNA"
-
-
CompilerOptions
— (String
)Specifies additional parameters for compiler options in JSON format. The compiler options are
TargetPlatform
specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specifyCompilerOptions.
-
DTYPE
: Specifies the data type for the input. When compiling forml_*
(except forml_inf
) instances using PyTorch framework, provide the data type (dtype) of the model's input."float32"
is used if"DTYPE"
is not specified. Options for data type are:-
float32: Use either
"float"
or"float32"
. -
int64: Use either
"int64"
or"long"
.
For example,
{"dtype" : "float32"}
. -
-
CPU
: Compilation for CPU supports the following compiler options.-
mcpu
: CPU micro-architecture. For example,{'mcpu': 'skylake-avx512'}
-
mattr
: CPU flags. For example,{'mattr': ['+neon', '+vfpv4']}
-
-
ARM
: Details of ARM CPU compilations.-
NEON
: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.For example, add
{'mattr': ['+neon']}
to the compiler options if compiling for ARM 32-bit platform with the NEON support.
-
-
NVIDIA
: Compilation for NVIDIA GPU supports the following compiler options.-
gpu_code
: Specifies the targeted architecture. -
trt-ver
: Specifies the TensorRT versions in x.y.z. format. -
cuda-ver
: Specifies the CUDA version in x.y format.
For example,
{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
-
-
ANDROID
: Compilation for the Android OS supports the following compiler options:-
ANDROID_PLATFORM
: Specifies the Android API levels. Available levels range from 21 to 29. For example,{'ANDROID_PLATFORM': 28}
. -
mattr
: Add{'mattr': ['+neon']}
to compiler options if compiling for ARM 32-bit platform with NEON support.
-
-
INFERENTIA
: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example,"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""
.For information about supported compiler options, see Neuron Compiler CLI Reference Guide.
-
CoreML
: Compilation for the CoreML OutputConfigTargetDevice
supports the following compiler options:-
class_labels
: Specifies the classification labels file name inside input tar.gz file. For example,{"class_labels": "imagenet_labels_1000.txt"}
. Labels inside the txt file should be separated by newlines.
-
-
EIA
: Compilation for the Elastic Inference Accelerator supports the following compiler options:-
precision_mode
: Specifies the precision of compiled artifacts. Supported values are"FP16"
and"FP32"
. Default is"FP32"
. -
signature_def_key
: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key. -
output_names
: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either:signature_def_key
oroutput_names
.
For example:
{"precision_mode": "FP32", "output_names": ["output:0"]}
-
-
KmsKeyId
— (String
)The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
VpcConfig
— (map
)A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs. IDs have the form of
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.
DerivedInformation
— (map
)Information that SageMaker Neo automatically derived about the model.
DerivedDataInputConfig
— (String
)The data input configuration that SageMaker Neo automatically derived for the model. When SageMaker Neo derives this information, you don't need to specify the data input configuration when you create a compilation job.
-
(AWS.Response)
—
Returns:
describeContext(params = {}, callback) ⇒ AWS.Request
Describes a context.
Service Reference:
Examples:
Calling the describeContext operation
var params = { ContextName: 'STRING_VALUE' /* required */ }; sagemaker.describeContext(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ContextName
— (String
)The name of the context to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ContextName
— (String
)The name of the context.
ContextArn
— (String
)The Amazon Resource Name (ARN) of the context.
Source
— (map
)The source of the context.
SourceUri
— required — (String
)The URI of the source.
SourceType
— (String
)The type of the source.
SourceId
— (String
)The ID of the source.
ContextType
— (String
)The type of the context.
Description
— (String
)The description of the context.
Properties
— (map<String>
)A list of the context's properties.
CreationTime
— (Date
)When the context was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)When the context was last modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LineageGroupArn
— (String
)The Amazon Resource Name (ARN) of the lineage group.
-
(AWS.Response)
—
Returns:
describeDataQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Gets the details of a data quality monitoring job definition.
Service Reference:
Examples:
Calling the describeDataQualityJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE' /* required */ }; sagemaker.describeDataQualityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the data quality monitoring job definition to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the data quality monitoring job definition.
JobDefinitionName
— (String
)The name of the data quality monitoring job definition.
CreationTime
— (Date
)The time that the data quality monitoring job definition was created.
DataQualityBaselineConfig
— (map
)The constraints and baselines for the data quality monitoring job definition.
BaseliningJobName
— (String
)The name of the job that performs baselining for the data quality monitoring job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
StatisticsResource
— (map
)The statistics resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the statistics resource.
DataQualityAppSpecification
— (map
)Information about the container that runs the data quality monitoring job.
ImageUri
— required — (String
)The container image that the data quality monitoring job runs.
ContainerEntrypoint
— (Array<String>
)The entrypoint for a container used to run a monitoring job.
ContainerArguments
— (Array<String>
)The arguments to send to the container that the monitoring job runs.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
Environment
— (map<String>
)Sets the environment variables in the container that the monitoring job runs.
DataQualityJobInput
— (map
)The list of inputs for the data quality monitoring job. Currently endpoints are supported.
EndpointInput
— (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
DataQualityJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
NetworkConfig
— (map
)The networking configuration for the data quality monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
-
(AWS.Response)
—
Returns:
describeDevice(params = {}, callback) ⇒ AWS.Request
Describes the device.
Service Reference:
Examples:
Calling the describeDevice operation
var params = { DeviceFleetName: 'STRING_VALUE', /* required */ DeviceName: 'STRING_VALUE', /* required */ NextToken: 'STRING_VALUE' }; sagemaker.describeDevice(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)Next token of device description.
DeviceName
— (String
)The unique ID of the device.
DeviceFleetName
— (String
)The name of the fleet the devices belong to.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DeviceArn
— (String
)The Amazon Resource Name (ARN) of the device.
DeviceName
— (String
)The unique identifier of the device.
Description
— (String
)A description of the device.
DeviceFleetName
— (String
)The name of the fleet the device belongs to.
IotThingName
— (String
)The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.
RegistrationTime
— (Date
)The timestamp of the last registration or de-reregistration.
LatestHeartbeat
— (Date
)The last heartbeat received from the device.
Models
— (Array<map>
)Models on the device.
ModelName
— required — (String
)The name of the model.
ModelVersion
— required — (String
)The model version.
LatestSampleTime
— (Date
)The timestamp of the last data sample taken.
LatestInference
— (Date
)The timestamp of the last inference that was made.
MaxModels
— (Integer
)The maximum number of models.
NextToken
— (String
)The response from the last list when returning a list large enough to need tokening.
AgentVersion
— (String
)Edge Manager agent version.
-
(AWS.Response)
—
Returns:
describeDeviceFleet(params = {}, callback) ⇒ AWS.Request
A description of the fleet the device belongs to.
Service Reference:
Examples:
Calling the describeDeviceFleet operation
var params = { DeviceFleetName: 'STRING_VALUE' /* required */ }; sagemaker.describeDeviceFleet(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DeviceFleetName
— (String
)The name of the fleet.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DeviceFleetName
— (String
)The name of the fleet.
DeviceFleetArn
— (String
)The The Amazon Resource Name (ARN) of the fleet.
OutputConfig
— (map
)The output configuration for storing sampled data.
S3OutputLocation
— required — (String
)The Amazon Simple Storage (S3) bucker URI.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
PresetDeploymentType
— (String
)The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
Possible values include:"GreengrassV2Component"
PresetDeploymentConfig
— (String
)The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
-
ComponentName
(optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job. -
ComponentDescription
(optional) - Description of the component. -
ComponentVersion
(optional) - The version of the component.Note: Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification. -
PlatformOS
(optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux. -
PlatformArchitecture
(optional) - The processor architecture for the platform.Supported architectures Windows include: Windows32_x86, Windows64_x64.
Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
-
Description
— (String
)A description of the fleet.
CreationTime
— (Date
)Timestamp of when the device fleet was created.
LastModifiedTime
— (Date
)Timestamp of when the device fleet was last updated.
RoleArn
— (String
)The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
IotRoleAlias
— (String
)The Amazon Resource Name (ARN) alias created in Amazon Web Services Internet of Things (IoT).
-
(AWS.Response)
—
Returns:
describeDomain(params = {}, callback) ⇒ AWS.Request
The description of the domain.
Service Reference:
Examples:
Calling the describeDomain operation
var params = { DomainId: 'STRING_VALUE' /* required */ }; sagemaker.describeDomain(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DomainArn
— (String
)The domain's Amazon Resource Name (ARN).
DomainId
— (String
)The domain ID.
DomainName
— (String
)The domain name.
HomeEfsFileSystemId
— (String
)The ID of the Amazon Elastic File System managed by this Domain.
SingleSignOnManagedApplicationInstanceId
— (String
)The IAM Identity Center managed application instance ID.
SingleSignOnApplicationArn
— (String
)The ARN of the application managed by SageMaker in IAM Identity Center. This value is only returned for domains created after October 1, 2023.
Status
— (String
)The status.
Possible values include:"Deleting"
"Failed"
"InService"
"Pending"
"Updating"
"Update_Failed"
"Delete_Failed"
CreationTime
— (Date
)The creation time.
LastModifiedTime
— (Date
)The last modified time.
FailureReason
— (String
)The failure reason.
SecurityGroupIdForDomainBoundary
— (String
)The ID of the security group that authorizes traffic between the
RSessionGateway
apps and theRStudioServerPro
app.AuthMode
— (String
)The domain's authentication mode.
Possible values include:"SSO"
"IAM"
DefaultUserSettings
— (map
)Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile.
ExecutionRole
— (String
)The execution role for the user.
SecurityGroups
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the
CreateDomain.AppNetworkAccessType
parameter is set toPublicInternetOnly
.Required when the
CreateDomain.AppNetworkAccessType
parameter is set toVpcOnly
, unless specified as part of theDefaultUserSettings
for the domain.Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SharingSettings
— (map
)Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption
— (String
)Whether to include the notebook cell output when sharing the notebook. The default is
Possible values include:Disabled
."Allowed"
"Disabled"
S3OutputPath
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon S3 bucket used to store the shared notebook snapshots.S3KmsKeyId
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings
— (map
)The Jupyter server's app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The kernel gateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
TensorBoardAppSettings
— (map
)The TensorBoard app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings
— (map
)A collection of settings that configure user interaction with the
RStudioServerPro
app.AccessStatus
— (String
)Indicates whether the current user has access to the
Possible values include:RStudioServerPro
app."ENABLED"
"DISABLED"
UserGroup
— (String
)The level of permissions that the user has within the
Possible values include:RStudioServerPro
app. This value defaults toUser
. TheAdmin
value allows the user access to the RStudio Administrative Dashboard."R_STUDIO_ADMIN"
"R_STUDIO_USER"
RSessionAppSettings
— (map
)A collection of settings that configure the
RSessionGateway
app.DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a RSession app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
CanvasAppSettings
— (map
)The Canvas app settings.
TimeSeriesForecastingSettings
— (map
)Time series forecast settings for the SageMaker Canvas application.
Status
— (String
)Describes whether time series forecasting is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
AmazonForecastRoleArn
— (String
)The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the
UserProfile
that launches the Canvas application. If an execution role is not specified in theUserProfile
, Canvas uses the execution role specified in the Domain that owns theUserProfile
. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached andforecast.amazonaws.com
added in the trust relationship as a service principal.
ModelRegisterSettings
— (map
)The model registry settings for the SageMaker Canvas application.
Status
— (String
)Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CrossAccountModelRegisterRoleArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings
— (map
)The workspace settings for the SageMaker Canvas application.
S3ArtifactPath
— (String
)The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings
— (Array<map>
)The settings for connecting to an external data source with OAuth.
DataSourceName
— (String
)The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Possible values include:"SalesforceGenie"
"Snowflake"
Status
— (String
)Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
SecretArn
— (String
)The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings
— (map
)The model deployment settings for the SageMaker Canvas application.
Status
— (String
)Describes whether model deployment permissions are enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
KendraSettings
— (map
)The settings for document querying.
Status
— (String
)Describes whether the document querying feature is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
GenerativeAiSettings
— (map
)The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn
— (String
)The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes
bedrock.amazonaws.com
as a service principal.
EmrServerlessSettings
— (map
)The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status
— (String
)Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CodeEditorAppSettings
— (map
)The Code Editor application settings.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a Code Editor app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a JupyterLab app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set
LifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings
— (map
)The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
ExecutionRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
SpaceStorageSettings
— (map
)The storage settings for a space.
DefaultEbsStorageSettings
— (map
)The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb
— required — (Integer
)The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb
— required — (Integer
)The maximum size of the EBS storage volume for a space.
DefaultLandingUri
— (String
)The default experience that the user is directed to when accessing the domain. The supported values are:
-
studio::
: Indicates that Studio is the default experience. This value can only be passed ifStudioWebPortal
is set toENABLED
. -
app:JupyterServer:
: Indicates that Studio Classic is the default experience.
-
StudioWebPortal
— (String
)Whether the user can access Studio. If this value is set to
Possible values include:DISABLED
, the user cannot access Studio, even if that is the default experience for the domain."ENABLED"
"DISABLED"
CustomPosixUserConfig
— (map
)Details about the POSIX identity that is used for file system operations.
Uid
— required — (Integer
)The POSIX user ID.
Gid
— required — (Integer
)The POSIX group ID.
CustomFileSystemConfigs
— (Array<map>
)The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystemConfig
— (map
)The settings for a custom Amazon EFS file system.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
FileSystemPath
— (String
)The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings
— (map
)Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools
— (Array<String>
)The machine learning tools that are hidden from the Studio left navigation pane.
HiddenAppTypes
— (Array<String>
)The Applications supported in Studio that are hidden from the Studio left navigation pane.
AutoMountHomeEFS
— (String
)Indicates whether auto-mounting of an EFS volume is supported for the user profile. The
Possible values include:DefaultAsDomain
value is only supported for user profiles. Do not use theDefaultAsDomain
value when setting this parameter for a domain."Enabled"
"Disabled"
"DefaultAsDomain"
DomainSettings
— (map
)A collection of
Domain
settings.SecurityGroupIds
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud that the
Domain
uses for communication between Domain-level apps and user apps.RStudioServerProDomainSettings
— (map
)A collection of settings that configure the
RStudioServerPro
Domain-level app.DomainExecutionRoleArn
— required — (String
)The ARN of the execution role for the
RStudioServerPro
Domain-level app.RStudioConnectUrl
— (String
)A URL pointing to an RStudio Connect server.
RStudioPackageManagerUrl
— (String
)A URL pointing to an RStudio Package Manager server.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
ExecutionRoleIdentityConfig
— (String
)The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key.
Possible values include:"USER_PROFILE_NAME"
"DISABLED"
DockerSettings
— (map
)A collection of settings that configure the domain's Docker interaction.
EnableDockerAccess
— (String
)Indicates whether the domain can access Docker.
Possible values include:"ENABLED"
"DISABLED"
VpcOnlyTrustedAccounts
— (Array<String>
)The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
AmazonQSettings
— (map
)A collection of settings that configure the Amazon Q experience within the domain. The
AuthMode
that you use to create the domain must beSSO
.Status
— (String
)Whether Amazon Q has been enabled within the domain.
Possible values include:"ENABLED"
"DISABLED"
QProfileArn
— (String
)The ARN of the Amazon Q profile used within the domain.
AppNetworkAccessType
— (String
)Specifies the VPC used for non-EFS traffic. The default value is
PublicInternetOnly
.-
PublicInternetOnly
- Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access -
VpcOnly
- All traffic is through the specified VPC and subnets
"PublicInternetOnly"
"VpcOnly"
-
HomeEfsFileSystemKmsKeyId
— (String
)Use
KmsKeyId
.SubnetIds
— (Array<String>
)The VPC subnets that the domain uses for communication.
Url
— (String
)The domain's URL.
VpcId
— (String
)The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
KmsKeyId
— (String
)The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.
AppSecurityGroupManagement
— (String
)The entity that creates and manages the required security groups for inter-app communication in
Possible values include:VPCOnly
mode. Required whenCreateDomain.AppNetworkAccessType
isVPCOnly
andDomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn
is provided."Service"
"Customer"
DefaultSpaceSettings
— (map
)The default settings used to create a space.
ExecutionRole
— (String
)The ARN of the execution role for the space.
SecurityGroups
— (Array<String>
)The security group IDs for the Amazon VPC that the space uses for communication.
JupyterServerAppSettings
— (map
)The JupyterServer app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The KernelGateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a JupyterLab app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set
LifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings
— (map
)The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
ExecutionRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
SpaceStorageSettings
— (map
)The default storage settings for a space.
DefaultEbsStorageSettings
— (map
)The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb
— required — (Integer
)The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb
— required — (Integer
)The maximum size of the EBS storage volume for a space.
CustomPosixUserConfig
— (map
)Details about the POSIX identity that is used for file system operations.
Uid
— required — (Integer
)The POSIX user ID.
Gid
— required — (Integer
)The POSIX group ID.
CustomFileSystemConfigs
— (Array<map>
)The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystemConfig
— (map
)The settings for a custom Amazon EFS file system.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
FileSystemPath
— (String
)The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
-
(AWS.Response)
—
Returns:
describeEdgeDeploymentPlan(params = {}, callback) ⇒ AWS.Request
Describes an edge deployment plan with deployment status per stage.
Service Reference:
Examples:
Calling the describeEdgeDeploymentPlan operation
var params = { EdgeDeploymentPlanName: 'STRING_VALUE', /* required */ MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE' }; sagemaker.describeEdgeDeploymentPlan(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgeDeploymentPlanName
— (String
)The name of the deployment plan to describe.
NextToken
— (String
)If the edge deployment plan has enough stages to require tokening, then this is the response from the last list of stages returned.
MaxResults
— (Integer
)The maximum number of results to select (50 by default).
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EdgeDeploymentPlanArn
— (String
)The ARN of edge deployment plan.
EdgeDeploymentPlanName
— (String
)The name of the edge deployment plan.
ModelConfigs
— (Array<map>
)List of models associated with the edge deployment plan.
ModelHandle
— required — (String
)The name the device application uses to reference this model.
EdgePackagingJobName
— required — (String
)The edge packaging job associated with this deployment.
DeviceFleetName
— (String
)The device fleet used for this edge deployment plan.
EdgeDeploymentSuccess
— (Integer
)The number of edge devices with the successful deployment.
EdgeDeploymentPending
— (Integer
)The number of edge devices yet to pick up deployment, or in progress.
EdgeDeploymentFailed
— (Integer
)The number of edge devices that failed the deployment.
Stages
— (Array<map>
)List of stages in the edge deployment plan.
StageName
— required — (String
)The name of the stage.
DeviceSelectionConfig
— required — (map
)Configuration of the devices in the stage.
DeviceSubsetType
— required — (String
)Type of device subsets to deploy to the current stage.
Possible values include:"PERCENTAGE"
"SELECTION"
"NAMECONTAINS"
Percentage
— (Integer
)Percentage of devices in the fleet to deploy to the current stage.
DeviceNames
— (Array<String>
)List of devices chosen to deploy.
DeviceNameContains
— (String
)A filter to select devices with names containing this name.
DeploymentConfig
— required — (map
)Configuration of the deployment details.
FailureHandlingPolicy
— required — (String
)Toggle that determines whether to rollback to previous configuration if the current deployment fails. By default this is turned on. You may turn this off if you want to investigate the errors yourself.
Possible values include:"ROLLBACK_ON_FAILURE"
"DO_NOTHING"
DeploymentStatus
— required — (map
)General status of the current state.
StageStatus
— required — (String
)The general status of the current stage.
Possible values include:"CREATING"
"READYTODEPLOY"
"STARTING"
"INPROGRESS"
"DEPLOYED"
"FAILED"
"STOPPING"
"STOPPED"
EdgeDeploymentSuccessInStage
— required — (Integer
)The number of edge devices with the successful deployment in the current stage.
EdgeDeploymentPendingInStage
— required — (Integer
)The number of edge devices yet to pick up the deployment in current stage, or in progress.
EdgeDeploymentFailedInStage
— required — (Integer
)The number of edge devices that failed the deployment in current stage.
EdgeDeploymentStatusMessage
— (String
)A detailed message about deployment status in current stage.
EdgeDeploymentStageStartTime
— (Date
)The time when the deployment API started.
NextToken
— (String
)Token to use when calling the next set of stages in the edge deployment plan.
CreationTime
— (Date
)The time when the edge deployment plan was created.
LastModifiedTime
— (Date
)The time when the edge deployment plan was last updated.
-
(AWS.Response)
—
Returns:
describeEdgePackagingJob(params = {}, callback) ⇒ AWS.Request
A description of edge packaging jobs.
Service Reference:
Examples:
Calling the describeEdgePackagingJob operation
var params = { EdgePackagingJobName: 'STRING_VALUE' /* required */ }; sagemaker.describeEdgePackagingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgePackagingJobName
— (String
)The name of the edge packaging job.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EdgePackagingJobArn
— (String
)The Amazon Resource Name (ARN) of the edge packaging job.
EdgePackagingJobName
— (String
)The name of the edge packaging job.
CompilationJobName
— (String
)The name of the SageMaker Neo compilation job that is used to locate model artifacts that are being packaged.
ModelName
— (String
)The name of the model.
ModelVersion
— (String
)The version of the model.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact Neo.
OutputConfig
— (map
)The output configuration for the edge packaging job.
S3OutputLocation
— required — (String
)The Amazon Simple Storage (S3) bucker URI.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
PresetDeploymentType
— (String
)The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
Possible values include:"GreengrassV2Component"
PresetDeploymentConfig
— (String
)The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
-
ComponentName
(optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job. -
ComponentDescription
(optional) - Description of the component. -
ComponentVersion
(optional) - The version of the component.Note: Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification. -
PlatformOS
(optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux. -
PlatformArchitecture
(optional) - The processor architecture for the platform.Supported architectures Windows include: Windows32_x86, Windows64_x64.
Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
-
ResourceKey
— (String
)The Amazon Web Services KMS key to use when encrypting the EBS volume the job run on.
EdgePackagingJobStatus
— (String
)The current status of the packaging job.
Possible values include:"STARTING"
"INPROGRESS"
"COMPLETED"
"FAILED"
"STOPPING"
"STOPPED"
EdgePackagingJobStatusMessage
— (String
)Returns a message describing the job status and error messages.
CreationTime
— (Date
)The timestamp of when the packaging job was created.
LastModifiedTime
— (Date
)The timestamp of when the job was last updated.
ModelArtifact
— (String
)The Amazon Simple Storage (S3) URI where model artifacts ares stored.
ModelSignature
— (String
)The signature document of files in the model artifact.
PresetDeploymentOutput
— (map
)The output of a SageMaker Edge Manager deployable resource.
Type
— required — (String
)The deployment type created by SageMaker Edge Manager. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
Possible values include:"GreengrassV2Component"
Artifact
— (String
)The Amazon Resource Name (ARN) of the generated deployable resource.
Status
— (String
)The status of the deployable resource.
Possible values include:"COMPLETED"
"FAILED"
StatusMessage
— (String
)Returns a message describing the status of the deployed resource.
-
(AWS.Response)
—
Returns:
describeEndpoint(params = {}, callback) ⇒ AWS.Request
Returns the description of an endpoint.
Service Reference:
Examples:
Calling the describeEndpoint operation
var params = { EndpointName: 'STRING_VALUE' /* required */ }; sagemaker.describeEndpoint(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (String
)The name of the endpoint.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointName
— (String
)Name of the endpoint.
EndpointArn
— (String
)The Amazon Resource Name (ARN) of the endpoint.
EndpointConfigName
— (String
)The name of the endpoint configuration associated with this endpoint.
ProductionVariants
— (Array<map>
)An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.
VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight, as specified in the
UpdateEndpointWeightsAndCapacities
request.CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in the
UpdateEndpointWeightsAndCapacities
request.VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for the endpoint update.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
DataCaptureConfig
— (map
)The currently active data capture configuration used by your Endpoint.
EnableCapture
— required — (Boolean
)Whether data capture is enabled or disabled.
CaptureStatus
— required — (String
)Whether data capture is currently functional.
Possible values include:"Started"
"Stopped"
CurrentSamplingPercentage
— required — (Integer
)The percentage of requests being captured by your Endpoint.
DestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
KmsKeyId
— required — (String
)The KMS key being used to encrypt the data in Amazon S3.
EndpointStatus
— (String
)The status of the endpoint.
-
OutOfService
: Endpoint is not available to take incoming requests. -
Creating
: CreateEndpoint is executing. -
Updating
: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing. -
SystemUpdating
: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count. -
RollingBack
: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to anInService
status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly. -
InService
: Endpoint is available to process incoming requests. -
Deleting
: DeleteEndpoint is executing. -
Failed
: Endpoint could not be created, updated, or re-scaled. Use theFailureReason
value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint. -
UpdateRollbackFailed
: Both the rolling deployment and auto-rollback failed. Your endpoint is in service with a mix of the old and new endpoint configurations. For information about how to remedy this issue and restore the endpoint's status toInService
, see Rolling Deployments.
"OutOfService"
"Creating"
"Updating"
"SystemUpdating"
"RollingBack"
"InService"
"Deleting"
"Failed"
"UpdateRollbackFailed"
-
FailureReason
— (String
)If the status of the endpoint is
Failed
, the reason why it failed.CreationTime
— (Date
)A timestamp that shows when the endpoint was created.
LastModifiedTime
— (Date
)A timestamp that shows when the endpoint was last modified.
LastDeploymentConfig
— (map
)The most recent deployment configuration for the endpoint.
BlueGreenUpdatePolicy
— (map
)Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
TrafficRoutingConfiguration
— required — (map
)Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.
Type
— required — (String
)Traffic routing strategy type.
-
ALL_AT_ONCE
: Endpoint traffic shifts to the new fleet in a single step. -
CANARY
: Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic. -
LINEAR
: Endpoint traffic shifts to the new fleet in n steps of a configurable size.
"ALL_AT_ONCE"
"CANARY"
"LINEAR"
-
WaitIntervalInSeconds
— required — (Integer
)The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.
CanarySize
— (map
)Batch size for the first step to turn on traffic on the new endpoint fleet.
Value
must be less than or equal to 50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
LinearStepSize
— (map
)Batch size for each step to turn on traffic on the new endpoint fleet.
Value
must be 10-50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
TerminationWaitInSeconds
— (Integer
)Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.
MaximumExecutionTimeoutInSeconds
— (Integer
)Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in
TerminationWaitInSeconds
andWaitIntervalInSeconds
.
RollingUpdatePolicy
— (map
)Specifies a rolling deployment strategy for updating a SageMaker endpoint.
MaximumBatchSize
— required — (map
)Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.
Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
WaitIntervalInSeconds
— required — (Integer
)The length of the baking period, during which SageMaker monitors alarms for each batch on the new fleet.
MaximumExecutionTimeoutInSeconds
— (Integer
)The time limit for the total deployment. Exceeding this limit causes a timeout.
RollbackMaximumBatchSize
— (map
)Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.
Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
AutoRollbackConfiguration
— (map
)Automatic rollback configuration for handling endpoint deployment failures and recovery.
Alarms
— (Array<map>
)List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.
AlarmName
— (String
)The name of a CloudWatch alarm in your account.
AsyncInferenceConfig
— (map
)Returns the description of an endpoint configuration created using the
CreateEndpointConfig
API.ClientConfig
— (map
)Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
MaxConcurrentInvocationsPerInstance
— (Integer
)The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
OutputConfig
— required — (map
)Specifies the configuration for asynchronous inference invocation outputs.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
S3OutputPath
— (String
)The Amazon S3 location to upload inference responses to.
NotificationConfig
— (map
)Specifies the configuration for notifications of inference results for asynchronous inference.
SuccessTopic
— (String
)Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
ErrorTopic
— (String
)Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
IncludeInferenceResponseIn
— (Array<String>
)The Amazon SNS topics where you want the inference response to be included.
Note: The inference response is included only if the response size is less than or equal to 128 KB.
S3FailurePath
— (String
)The Amazon S3 location to upload failure inference responses to.
PendingDeploymentSummary
— (map
)Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.
EndpointConfigName
— required — (String
)The name of the endpoint configuration used in the deployment.
ProductionVariants
— (Array<map>
)An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint for the in-progress deployment.
VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
InstanceType
— (String
)The type of instances associated with the variant.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
StartTime
— (Date
)The start time of the deployment.
ShadowProductionVariants
— (Array<map>
)An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint in shadow mode with production traffic replicated from the model specified on
ProductionVariants
for the in-progress deployment.VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
InstanceType
— (String
)The type of instances associated with the variant.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
ExplainerConfig
— (map
)The configuration parameters for an explainer.
ClarifyExplainerConfig
— (map
)A member of
ExplainerConfig
that contains configuration parameters for the SageMaker Clarify explainer.EnableExplanations
— (String
)A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See
EnableExplanations
for additional information.InferenceConfig
— (map
)The inference configuration parameter for the model container.
FeaturesAttribute
— (String
)Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if
FeaturesAttribute
is the JMESPath expression'myfeatures'
, it extracts a list of features[1,2,3]
from request data'{"myfeatures":[1,2,3]}'
.ContentTemplate
— (String
)A template string used to format a JSON record into an acceptable model container input. For example, a
ContentTemplate
string'{"myfeatures":$features}'
will format a list of features[1,2,3]
into the record string'{"myfeatures":[1,2,3]}'
. Required only when the model container input is in JSON Lines format.MaxRecordCount
— (Integer
)The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If
MaxRecordCount
is1
, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.MaxPayloadInMB
— (Integer
)The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
6
MB.ProbabilityIndex
— (Integer
)A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.
Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability:
'1,0.6'
, setProbabilityIndex
to1
to select the probability value0.6
.Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, setProbabilityIndex
to1
to select the probability values[0.1,0.6,0.3]
.LabelIndex
— (Integer
)A zero-based index used to extract a label header or list of label headers from model container output in CSV format.
Example for a multiclass model: If the model container output consists of label headers followed by probabilities:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, setLabelIndex
to0
to select the label headers['cat','dog','fish']
.ProbabilityAttribute
— (String
)A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.
Example: If the model container output of a single request is
'{"predicted_label":1,"probability":0.6}'
, then setProbabilityAttribute
to'probability'
.LabelAttribute
— (String
)A JMESPath expression used to locate the list of label headers in the model container output.
Example: If the model container output of a batch request is
'{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}'
, then setLabelAttribute
to'labels'
to extract the list of label headers["cat","dog","fish"]
LabelHeaders
— (Array<String>
)For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the
InvokeEndpoint
API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.FeatureHeaders
— (Array<String>
)The names of the features. If provided, these are included in the endpoint response payload to help readability of the
InvokeEndpoint
output. See the Response section under Invoke the endpoint in the Developer Guide for more information.FeatureTypes
— (Array<String>
)A list of data types of the features (optional). Applicable only to NLP explainability. If provided,
FeatureTypes
must have at least one'text'
string (for example,['text']
). IfFeatureTypes
is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.
ShapConfig
— required — (map
)The configuration for SHAP analysis.
ShapBaselineConfig
— required — (map
)The configuration for the SHAP baseline of the Kernal SHAP algorithm.
MimeType
— (String
)The MIME type of the baseline data. Choose from
'text/csv'
or'application/jsonlines'
. Defaults to'text/csv'
.ShapBaseline
— (String
)The inline SHAP baseline data in string format.
ShapBaseline
can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by theGranularity
of theTextConfig
parameter. The size limit forShapBasline
is 4 KB. Use theShapBaselineUri
parameter if you want to provide more than 4 KB of baseline data.ShapBaselineUri
— (String
)The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the
ShapBaselineUri
should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.
NumberOfSamples
— (Integer
)The number of samples to be used for analysis by the Kernal SHAP algorithm.
Note: The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.UseLogit
— (Boolean
)A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.
Seed
— (Integer
)The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.
TextConfig
— (map
)A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.
Language
— required — (String
)Specifies the language of the text features in ISO 639-1 or ISO 639-3 code of a supported language.
Note: For a mix of multiple languages, use codePossible values include:'xx'
."af"
"sq"
"ar"
"hy"
"eu"
"bn"
"bg"
"ca"
"zh"
"hr"
"cs"
"da"
"nl"
"en"
"et"
"fi"
"fr"
"de"
"el"
"gu"
"he"
"hi"
"hu"
"is"
"id"
"ga"
"it"
"kn"
"ky"
"lv"
"lt"
"lb"
"mk"
"ml"
"mr"
"ne"
"nb"
"fa"
"pl"
"pt"
"ro"
"ru"
"sa"
"sr"
"tn"
"si"
"sk"
"sl"
"es"
"sv"
"tl"
"ta"
"tt"
"te"
"tr"
"uk"
"ur"
"yo"
"lij"
"xx"
Granularity
— required — (String
)The unit of granularity for the analysis of text features. For example, if the unit is
Possible values include:'token'
, then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature."token"
"sentence"
"paragraph"
ShadowProductionVariants
— (Array<map>
)An array of ProductionVariantSummary objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on
ProductionVariants
.VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight, as specified in the
UpdateEndpointWeightsAndCapacities
request.CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in the
UpdateEndpointWeightsAndCapacities
request.VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for the endpoint update.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
-
(AWS.Response)
—
Returns:
Waiter Resource States:
describeEndpointConfig(params = {}, callback) ⇒ AWS.Request
Returns the description of an endpoint configuration created using the
CreateEndpointConfig
API.Service Reference:
Examples:
Calling the describeEndpointConfig operation
var params = { EndpointConfigName: 'STRING_VALUE' /* required */ }; sagemaker.describeEndpointConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointConfigName
— (String
)The name of the endpoint configuration.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointConfigName
— (String
)Name of the SageMaker endpoint configuration.
EndpointConfigArn
— (String
)The Amazon Resource Name (ARN) of the endpoint configuration.
ProductionVariants
— (Array<map>
)An array of
ProductionVariant
objects, one for each model that you want to host at this endpoint.VariantName
— required — (String
)The name of the production variant.
ModelName
— (String
)The name of the model that you want to host. This is the name that you specified when creating the model.
InitialInstanceCount
— (Integer
)Number of instances to launch initially.
InstanceType
— (String
)The ML compute instance type.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
InitialVariantWeight
— (Float
)Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the
VariantWeight
to the sum of allVariantWeight
values across all ProductionVariants. If unspecified, it defaults to 1.0.AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
CoreDumpConfig
— (map
)Specifies configuration for a core dump from the model container when the process crashes.
DestinationS3Uri
— required — (String
)The Amazon S3 bucket to send the core dump to.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys forOutputDataConfig
. If you use a bucket policy with ans3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint
andUpdateEndpoint
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
ServerlessConfig
— (map
)The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.
ModelDataDownloadTimeoutInSeconds
— (Integer
)The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.
ContainerStartupHealthCheckTimeoutInSeconds
— (Integer
)The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
EnableSSMAccess
— (Boolean
)You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling
UpdateEndpoint
.ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
InferenceAmiVersion
— (String
)Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions. Amazon Web Services optimizes these configurations for different machine learning workloads.
By selecting an AMI version, you can ensure that your inference environment is compatible with specific software requirements, such as CUDA driver versions, Linux kernel versions, or Amazon Web Services Neuron driver versions.
The AMI version names, and their configurations, are the following:
- al2-ami-sagemaker-inference-gpu-2
-
-
Accelerator: GPU
-
NVIDIA driver version: 535.54.03
-
CUDA driver version: 12.2
-
Supported instance types: ml.g4dn., ml.g5., ml.g6., ml.p3., ml.p4d., ml.p4de., ml.p5.*
-
"al2-ami-sagemaker-inference-gpu-2"
DataCaptureConfig
— (map
)Configuration to control how SageMaker captures inference data.
EnableCapture
— (Boolean
)Whether data capture should be enabled or disabled (defaults to enabled).
InitialSamplingPercentage
— required — (Integer
)The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.
DestinationS3Uri
— required — (String
)The Amazon S3 location used to capture the data.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker uses to encrypt the captured data at rest using Amazon S3 server-side encryption.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
CaptureOptions
— required — (Array<map>
)Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both
CaptureMode
— required — (String
)Specify the boundary of data to capture.
Possible values include:"Input"
"Output"
"InputAndOutput"
CaptureContentTypeHeader
— (map
)Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.
CsvContentTypes
— (Array<String>
)The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.
JsonContentTypes
— (Array<String>
)The list of all content type headers that SageMaker will treat as JSON and capture accordingly.
KmsKeyId
— (String
)Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
CreationTime
— (Date
)A timestamp that shows when the endpoint configuration was created.
AsyncInferenceConfig
— (map
)Returns the description of an endpoint configuration created using the
CreateEndpointConfig
API.ClientConfig
— (map
)Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
MaxConcurrentInvocationsPerInstance
— (Integer
)The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
OutputConfig
— required — (map
)Specifies the configuration for asynchronous inference invocation outputs.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
S3OutputPath
— (String
)The Amazon S3 location to upload inference responses to.
NotificationConfig
— (map
)Specifies the configuration for notifications of inference results for asynchronous inference.
SuccessTopic
— (String
)Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
ErrorTopic
— (String
)Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
IncludeInferenceResponseIn
— (Array<String>
)The Amazon SNS topics where you want the inference response to be included.
Note: The inference response is included only if the response size is less than or equal to 128 KB.
S3FailurePath
— (String
)The Amazon S3 location to upload failure inference responses to.
ExplainerConfig
— (map
)The configuration parameters for an explainer.
ClarifyExplainerConfig
— (map
)A member of
ExplainerConfig
that contains configuration parameters for the SageMaker Clarify explainer.EnableExplanations
— (String
)A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See
EnableExplanations
for additional information.InferenceConfig
— (map
)The inference configuration parameter for the model container.
FeaturesAttribute
— (String
)Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if
FeaturesAttribute
is the JMESPath expression'myfeatures'
, it extracts a list of features[1,2,3]
from request data'{"myfeatures":[1,2,3]}'
.ContentTemplate
— (String
)A template string used to format a JSON record into an acceptable model container input. For example, a
ContentTemplate
string'{"myfeatures":$features}'
will format a list of features[1,2,3]
into the record string'{"myfeatures":[1,2,3]}'
. Required only when the model container input is in JSON Lines format.MaxRecordCount
— (Integer
)The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If
MaxRecordCount
is1
, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.MaxPayloadInMB
— (Integer
)The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
6
MB.ProbabilityIndex
— (Integer
)A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.
Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability:
'1,0.6'
, setProbabilityIndex
to1
to select the probability value0.6
.Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, setProbabilityIndex
to1
to select the probability values[0.1,0.6,0.3]
.LabelIndex
— (Integer
)A zero-based index used to extract a label header or list of label headers from model container output in CSV format.
Example for a multiclass model: If the model container output consists of label headers followed by probabilities:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, setLabelIndex
to0
to select the label headers['cat','dog','fish']
.ProbabilityAttribute
— (String
)A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.
Example: If the model container output of a single request is
'{"predicted_label":1,"probability":0.6}'
, then setProbabilityAttribute
to'probability'
.LabelAttribute
— (String
)A JMESPath expression used to locate the list of label headers in the model container output.
Example: If the model container output of a batch request is
'{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}'
, then setLabelAttribute
to'labels'
to extract the list of label headers["cat","dog","fish"]
LabelHeaders
— (Array<String>
)For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the
InvokeEndpoint
API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.FeatureHeaders
— (Array<String>
)The names of the features. If provided, these are included in the endpoint response payload to help readability of the
InvokeEndpoint
output. See the Response section under Invoke the endpoint in the Developer Guide for more information.FeatureTypes
— (Array<String>
)A list of data types of the features (optional). Applicable only to NLP explainability. If provided,
FeatureTypes
must have at least one'text'
string (for example,['text']
). IfFeatureTypes
is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.
ShapConfig
— required — (map
)The configuration for SHAP analysis.
ShapBaselineConfig
— required — (map
)The configuration for the SHAP baseline of the Kernal SHAP algorithm.
MimeType
— (String
)The MIME type of the baseline data. Choose from
'text/csv'
or'application/jsonlines'
. Defaults to'text/csv'
.ShapBaseline
— (String
)The inline SHAP baseline data in string format.
ShapBaseline
can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by theGranularity
of theTextConfig
parameter. The size limit forShapBasline
is 4 KB. Use theShapBaselineUri
parameter if you want to provide more than 4 KB of baseline data.ShapBaselineUri
— (String
)The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the
ShapBaselineUri
should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.
NumberOfSamples
— (Integer
)The number of samples to be used for analysis by the Kernal SHAP algorithm.
Note: The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.UseLogit
— (Boolean
)A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.
Seed
— (Integer
)The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.
TextConfig
— (map
)A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.
Language
— required — (String
)Specifies the language of the text features in ISO 639-1 or ISO 639-3 code of a supported language.
Note: For a mix of multiple languages, use codePossible values include:'xx'
."af"
"sq"
"ar"
"hy"
"eu"
"bn"
"bg"
"ca"
"zh"
"hr"
"cs"
"da"
"nl"
"en"
"et"
"fi"
"fr"
"de"
"el"
"gu"
"he"
"hi"
"hu"
"is"
"id"
"ga"
"it"
"kn"
"ky"
"lv"
"lt"
"lb"
"mk"
"ml"
"mr"
"ne"
"nb"
"fa"
"pl"
"pt"
"ro"
"ru"
"sa"
"sr"
"tn"
"si"
"sk"
"sl"
"es"
"sv"
"tl"
"ta"
"tt"
"te"
"tr"
"uk"
"ur"
"yo"
"lij"
"xx"
Granularity
— required — (String
)The unit of granularity for the analysis of text features. For example, if the unit is
Possible values include:'token'
, then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature."token"
"sentence"
"paragraph"
ShadowProductionVariants
— (Array<map>
)An array of
ProductionVariant
objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified onProductionVariants
.VariantName
— required — (String
)The name of the production variant.
ModelName
— (String
)The name of the model that you want to host. This is the name that you specified when creating the model.
InitialInstanceCount
— (Integer
)Number of instances to launch initially.
InstanceType
— (String
)The ML compute instance type.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
InitialVariantWeight
— (Float
)Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the
VariantWeight
to the sum of allVariantWeight
values across all ProductionVariants. If unspecified, it defaults to 1.0.AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
CoreDumpConfig
— (map
)Specifies configuration for a core dump from the model container when the process crashes.
DestinationS3Uri
— required — (String
)The Amazon S3 bucket to send the core dump to.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys forOutputDataConfig
. If you use a bucket policy with ans3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your
CreateEndpoint
andUpdateEndpoint
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
ServerlessConfig
— (map
)The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.
ModelDataDownloadTimeoutInSeconds
— (Integer
)The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.
ContainerStartupHealthCheckTimeoutInSeconds
— (Integer
)The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
EnableSSMAccess
— (Boolean
)You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling
UpdateEndpoint
.ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
InferenceAmiVersion
— (String
)Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions. Amazon Web Services optimizes these configurations for different machine learning workloads.
By selecting an AMI version, you can ensure that your inference environment is compatible with specific software requirements, such as CUDA driver versions, Linux kernel versions, or Amazon Web Services Neuron driver versions.
The AMI version names, and their configurations, are the following:
- al2-ami-sagemaker-inference-gpu-2
-
-
Accelerator: GPU
-
NVIDIA driver version: 535.54.03
-
CUDA driver version: 12.2
-
Supported instance types: ml.g4dn., ml.g5., ml.g6., ml.p3., ml.p4d., ml.p4de., ml.p5.*
-
"al2-ami-sagemaker-inference-gpu-2"
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role that you assigned to the endpoint configuration.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
EnableNetworkIsolation
— (Boolean
)Indicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.
-
(AWS.Response)
—
Returns:
describeExperiment(params = {}, callback) ⇒ AWS.Request
Provides a list of an experiment's properties.
Service Reference:
Examples:
Calling the describeExperiment operation
var params = { ExperimentName: 'STRING_VALUE' /* required */ }; sagemaker.describeExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ExperimentName
— (String
)The name of the experiment to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ExperimentName
— (String
)The name of the experiment.
ExperimentArn
— (String
)The Amazon Resource Name (ARN) of the experiment.
DisplayName
— (String
)The name of the experiment as displayed. If
DisplayName
isn't specified,ExperimentName
is displayed.Source
— (map
)The Amazon Resource Name (ARN) of the source and, optionally, the type.
SourceArn
— required — (String
)The Amazon Resource Name (ARN) of the source.
SourceType
— (String
)The source type.
Description
— (String
)The description of the experiment.
CreationTime
— (Date
)When the experiment was created.
CreatedBy
— (map
)Who created the experiment.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)When the experiment was last modified.
LastModifiedBy
— (map
)Who last modified the experiment.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
-
(AWS.Response)
—
Returns:
describeFeatureGroup(params = {}, callback) ⇒ AWS.Request
Use this operation to describe a
FeatureGroup
. The response includes information on the creation time,FeatureGroup
name, the unique identifier for eachFeatureGroup
, and more.Service Reference:
Examples:
Calling the describeFeatureGroup operation
var params = { FeatureGroupName: 'STRING_VALUE', /* required */ NextToken: 'STRING_VALUE' }; sagemaker.describeFeatureGroup(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FeatureGroupName
— (String
)The name or Amazon Resource Name (ARN) of the
FeatureGroup
you want described.NextToken
— (String
)A token to resume pagination of the list of
Features
(FeatureDefinitions
). 2,500Features
are returned by default.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:FeatureGroupArn
— (String
)The Amazon Resource Name (ARN) of the
FeatureGroup
.FeatureGroupName
— (String
)he name of the
FeatureGroup
.RecordIdentifierFeatureName
— (String
)The name of the
Feature
used forRecordIdentifier
, whose value uniquely identifies a record stored in the feature store.EventTimeFeatureName
— (String
)The name of the feature that stores the
EventTime
of a Record in aFeatureGroup
.An
EventTime
is a point in time when a new event occurs that corresponds to the creation or update of aRecord
in aFeatureGroup
. AllRecords
in theFeatureGroup
have a correspondingEventTime
.FeatureDefinitions
— (Array<map>
)A list of the
Features
in theFeatureGroup
. Each feature is defined by aFeatureName
andFeatureType
.FeatureName
— required — (String
)The name of a feature. The type must be a string.
FeatureName
cannot be any of the following:is_deleted
,write_time
,api_invocation_time
.The name:
-
Must start with an alphanumeric character.
-
Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.
-
FeatureType
— required — (String
)The value type of a feature. Valid values are Integral, Fractional, or String.
Possible values include:"Integral"
"Fractional"
"String"
CollectionType
— (String
)A grouping of elements where each element within the collection must have the same feature type (
String
,Integral
, orFractional
).-
List
: An ordered collection of elements. -
Set
: An unordered collection of unique elements. -
Vector
: A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.
"List"
"Set"
"Vector"
-
CollectionConfig
— (map
)Configuration for your collection.
VectorConfig
— (map
)Configuration for your vector collection type.
-
Dimension
: The number of elements in your vector.
Dimension
— required — (Integer
)The number of elements in your vector.
-
CreationTime
— (Date
)A timestamp indicating when SageMaker created the
FeatureGroup
.LastModifiedTime
— (Date
)A timestamp indicating when the feature group was last updated.
OnlineStoreConfig
— (map
)The configuration for the
OnlineStore
.SecurityConfig
— (map
)Use to specify KMS Key ID (
KMSKeyId
) for at-rest encryption of yourOnlineStore
.KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either user or IAM role) of
CreateFeatureGroup
must have below permissions to theOnlineStore
KmsKeyId
:-
"kms:Encrypt"
-
"kms:Decrypt"
-
"kms:DescribeKey"
-
"kms:CreateGrant"
-
"kms:RetireGrant"
-
"kms:ReEncryptFrom"
-
"kms:ReEncryptTo"
-
"kms:GenerateDataKey"
-
"kms:ListAliases"
-
"kms:ListGrants"
-
"kms:RevokeGrant"
The caller (either user or IAM role) to all DataPlane operations (
PutRecord
,GetRecord
,DeleteRecord
) must have the following permissions to theKmsKeyId
:-
"kms:Decrypt"
-
EnableOnlineStore
— (Boolean
)Turn
OnlineStore
off by specifyingFalse
for theEnableOnlineStore
flag. TurnOnlineStore
on by specifyingTrue
for theEnableOnlineStore
flag.The default value is
False
.TtlDuration
— (map
)Time to live duration, where the record is hard deleted after the expiration time is reached;
ExpiresAt
=EventTime
+TtlDuration
. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.Unit
— (String
)TtlDuration
time unit."Seconds"
"Minutes"
"Hours"
"Days"
"Weeks"
Value
— (Integer
)TtlDuration
time value.
StorageType
— (String
)Option for different tiers of low latency storage for real-time data retrieval.
-
Standard
: A managed low latency data store for feature groups. -
InMemory
: A managed data store for feature groups that supports very low latency retrieval.
"Standard"
"InMemory"
-
OfflineStoreConfig
— (map
)The configuration of the offline store. It includes the following configurations:
-
Amazon S3 location of the offline store.
-
Configuration of the Glue data catalog.
-
Table format of the offline store.
-
Option to disable the automatic creation of a Glue table for the offline store.
-
Encryption configuration.
S3StorageConfig
— required — (map
)The Amazon Simple Storage (Amazon S3) location of
OfflineStore
.S3Uri
— required — (String
)The S3 URI, or location in Amazon S3, of
OfflineStore
.S3 URIs have a format similar to the following:
s3://example-bucket/prefix/
.KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the
OfflineStore
S3 location.The IAM
roleARN
that is passed as a parameter toCreateFeatureGroup
must have below permissions to theKmsKeyId
:-
"kms:GenerateDataKey"
-
ResolvedOutputS3Uri
— (String
)The S3 path where offline records are written.
DisableGlueTableCreation
— (Boolean
)Set to
True
to disable the automatic creation of an Amazon Web Services Glue table when configuring anOfflineStore
. If set toFalse
, Feature Store will name theOfflineStore
Glue table following Athena's naming recommendations.The default value is
False
.DataCatalogConfig
— (map
)The meta data of the Glue table that is autogenerated when an
OfflineStore
is created.TableName
— required — (String
)The name of the Glue table.
Catalog
— required — (String
)The name of the Glue table catalog.
Database
— required — (String
)The name of the Glue table database.
TableFormat
— (String
)Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg.
Possible values include:"Default"
"Glue"
"Iceberg"
-
ThroughputConfig
— (map
)Active throughput configuration of the feature group. There are two modes:
ON_DEMAND
andPROVISIONED
. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled.Note:
PROVISIONED
throughput mode is supported only for feature groups that are offline-only, or use theStandard
tier online store.ThroughputMode
— required — (String
)The mode used for your feature group throughput:
Possible values include:ON_DEMAND
orPROVISIONED
."OnDemand"
"Provisioned"
ProvisionedReadCapacityUnits
— (Integer
)For provisioned feature groups with online store enabled, this indicates the read throughput you are billed for and can consume without throttling.
This field is not applicable for on-demand feature groups.
ProvisionedWriteCapacityUnits
— (Integer
)For provisioned feature groups, this indicates the write throughput you are billed for and can consume without throttling.
This field is not applicable for on-demand feature groups.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
FeatureGroupStatus
— (String
)The status of the feature group.
Possible values include:"Creating"
"Created"
"CreateFailed"
"Deleting"
"DeleteFailed"
OfflineStoreStatus
— (map
)The status of the
OfflineStore
. Notifies you if replicating data into theOfflineStore
has failed. Returns either:Active
orBlocked
Status
— required — (String
)An
Possible values include:OfflineStore
status."Active"
"Blocked"
"Disabled"
BlockedReason
— (String
)The justification for why the OfflineStoreStatus is Blocked (if applicable).
LastUpdateStatus
— (map
)A value indicating whether the update made to the feature group was successful.
Status
— required — (String
)A value that indicates whether the update was made successful.
Possible values include:"Successful"
"Failed"
"InProgress"
FailureReason
— (String
)If the update wasn't successful, indicates the reason why it failed.
FailureReason
— (String
)The reason that the
FeatureGroup
failed to be replicated in theOfflineStore
. This is failure can occur because:-
The
FeatureGroup
could not be created in theOfflineStore
. -
The
FeatureGroup
could not be deleted from theOfflineStore
.
-
Description
— (String
)A free form description of the feature group.
NextToken
— (String
)A token to resume pagination of the list of
Features
(FeatureDefinitions
).OnlineStoreTotalSizeBytes
— (Integer
)The size of the
OnlineStore
in bytes.
-
(AWS.Response)
—
Returns:
describeFeatureMetadata(params = {}, callback) ⇒ AWS.Request
Shows the metadata for a feature within a feature group.
Service Reference:
Examples:
Calling the describeFeatureMetadata operation
var params = { FeatureGroupName: 'STRING_VALUE', /* required */ FeatureName: 'STRING_VALUE' /* required */ }; sagemaker.describeFeatureMetadata(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FeatureGroupName
— (String
)The name or Amazon Resource Name (ARN) of the feature group containing the feature.
FeatureName
— (String
)The name of the feature.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:FeatureGroupArn
— (String
)The Amazon Resource Number (ARN) of the feature group that contains the feature.
FeatureGroupName
— (String
)The name of the feature group that you've specified.
FeatureName
— (String
)The name of the feature that you've specified.
FeatureType
— (String
)The data type of the feature.
Possible values include:"Integral"
"Fractional"
"String"
CreationTime
— (Date
)A timestamp indicating when the feature was created.
LastModifiedTime
— (Date
)A timestamp indicating when the metadata for the feature group was modified. For example, if you add a parameter describing the feature, the timestamp changes to reflect the last time you
Description
— (String
)The description you added to describe the feature.
Parameters
— (Array<map>
)The key-value pairs that you added to describe the feature.
Key
— (String
)A key that must contain a value to describe the feature.
Value
— (String
)The value that belongs to a key.
-
(AWS.Response)
—
Returns:
describeFlowDefinition(params = {}, callback) ⇒ AWS.Request
Returns information about the specified flow definition.
Service Reference:
Examples:
Calling the describeFlowDefinition operation
var params = { FlowDefinitionName: 'STRING_VALUE' /* required */ }; sagemaker.describeFlowDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FlowDefinitionName
— (String
)The name of the flow definition.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:FlowDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the flow defintion.
FlowDefinitionName
— (String
)The Amazon Resource Name (ARN) of the flow definition.
FlowDefinitionStatus
— (String
)The status of the flow definition. Valid values are listed below.
Possible values include:"Initializing"
"Active"
"Failed"
"Deleting"
CreationTime
— (Date
)The timestamp when the flow definition was created.
HumanLoopRequestSource
— (map
)Container for configuring the source of human task requests. Used to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
AwsManagedHumanLoopRequestSource
— required — (String
)Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:
Possible values include:"AWS/Rekognition/DetectModerationLabels/Image/V3"
"AWS/Textract/AnalyzeDocument/Forms/V1"
HumanLoopActivationConfig
— (map
)An object containing information about what triggers a human review workflow.
HumanLoopActivationConditionsConfig
— required — (map
)Container structure for defining under what conditions SageMaker creates a human loop.
HumanLoopActivationConditions
— required — (String
)JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide.
HumanLoopConfig
— (map
)An object containing information about who works on the task, the workforce task price, and other task details.
WorkteamArn
— required — (String
)Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces.
HumanTaskUiArn
— required — (String
)The Amazon Resource Name (ARN) of the human task user interface.
You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI.
To learn how to create a custom HTML template, see Create Custom Worker Task Template.
To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates.
TaskTitle
— required — (String
)A title for the human worker task.
TaskDescription
— required — (String
)A description for the human worker task.
TaskCount
— required — (Integer
)The number of distinct workers who will perform the same task on each object. For example, if
TaskCount
is set to3
for an image classification labeling job, three workers will classify each input image. IncreasingTaskCount
can improve label accuracy.TaskAvailabilityLifetimeInSeconds
— (Integer
)The length of time that a task remains available for review by human workers.
TaskTimeLimitInSeconds
— (Integer
)The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).
TaskKeywords
— (Array<String>
)Keywords used to describe the task so that workers can discover the task.
PublicWorkforceTaskPrice
— (map
)Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.
-
0.036
-
0.048
-
0.060
-
0.072
-
0.120
-
0.240
-
0.360
-
0.480
-
0.600
-
0.720
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.
-
0.012
-
0.024
-
0.036
-
0.048
-
0.060
-
0.072
-
0.120
-
0.240
-
0.360
-
0.480
-
0.600
-
0.720
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.
-
2.400
-
2.280
-
2.160
-
2.040
-
1.920
-
1.800
-
1.680
-
1.560
-
1.440
-
1.320
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
AmountInUsd
— (map
)Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
Dollars
— (Integer
)The whole number of dollars in the amount.
Cents
— (Integer
)The fractional portion, in cents, of the amount.
TenthFractionsOfACent
— (Integer
)Fractions of a cent, in tenths.
-
OutputConfig
— (map
)An object containing information about the output file.
S3OutputPath
— required — (String
)The Amazon S3 path where the object containing human output will be made available.
To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data.
KmsKeyId
— (String
)The Amazon Key Management Service (KMS) key ID for server-side encryption.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.
FailureReason
— (String
)The reason your flow definition failed.
-
(AWS.Response)
—
Returns:
describeHub(params = {}, callback) ⇒ AWS.Request
Describes a hub.
Service Reference:
Examples:
Calling the describeHub operation
var params = { HubName: 'STRING_VALUE' /* required */ }; sagemaker.describeHub(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubName
— (String
)The name of the hub to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HubName
— (String
)The name of the hub.
HubArn
— (String
)The Amazon Resource Name (ARN) of the hub.
HubDisplayName
— (String
)The display name of the hub.
HubDescription
— (String
)A description of the hub.
HubSearchKeywords
— (Array<String>
)The searchable keywords for the hub.
S3StorageConfig
— (map
)The Amazon S3 storage configuration for the hub.
S3OutputPath
— (String
)The Amazon S3 bucket prefix for hosting hub content.
HubStatus
— (String
)The status of the hub.
Possible values include:"InService"
"Creating"
"Updating"
"Deleting"
"CreateFailed"
"UpdateFailed"
"DeleteFailed"
FailureReason
— (String
)The failure reason if importing hub content failed.
CreationTime
— (Date
)The date and time that the hub was created.
LastModifiedTime
— (Date
)The date and time that the hub was last modified.
-
(AWS.Response)
—
Returns:
describeHubContent(params = {}, callback) ⇒ AWS.Request
Describe the content of a hub.
Service Reference:
Examples:
Calling the describeHubContent operation
var params = { HubContentName: 'STRING_VALUE', /* required */ HubContentType: Model | Notebook | ModelReference, /* required */ HubName: 'STRING_VALUE', /* required */ HubContentVersion: 'STRING_VALUE' }; sagemaker.describeHubContent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubName
— (String
)The name of the hub that contains the content to describe.
HubContentType
— (String
)The type of content in the hub.
Possible values include:"Model"
"Notebook"
"ModelReference"
HubContentName
— (String
)The name of the content to describe.
HubContentVersion
— (String
)The version of the content to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HubContentName
— (String
)The name of the hub content.
HubContentArn
— (String
)The Amazon Resource Name (ARN) of the hub content.
HubContentVersion
— (String
)The version of the hub content.
HubContentType
— (String
)The type of hub content.
Possible values include:"Model"
"Notebook"
"ModelReference"
DocumentSchemaVersion
— (String
)The document schema version for the hub content.
HubName
— (String
)The name of the hub that contains the content.
HubArn
— (String
)The Amazon Resource Name (ARN) of the hub that contains the content.
HubContentDisplayName
— (String
)The display name of the hub content.
HubContentDescription
— (String
)A description of the hub content.
HubContentMarkdown
— (String
)A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.
HubContentDocument
— (String
)The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.
SageMakerPublicHubContentArn
— (String
)The ARN of the public hub content.
ReferenceMinVersion
— (String
)The minimum version of the hub content.
SupportStatus
— (String
)The support status of the hub content.
Possible values include:"Supported"
"Deprecated"
HubContentSearchKeywords
— (Array<String>
)The searchable keywords for the hub content.
HubContentDependencies
— (Array<map>
)The location of any dependencies that the hub content has, such as scripts, model artifacts, datasets, or notebooks.
DependencyOriginPath
— (String
)The hub content dependency origin path.
DependencyCopyPath
— (String
)The hub content dependency copy path.
HubContentStatus
— (String
)The status of the hub content.
Possible values include:"Available"
"Importing"
"Deleting"
"ImportFailed"
"DeleteFailed"
FailureReason
— (String
)The failure reason if importing hub content failed.
CreationTime
— (Date
)The date and time that hub content was created.
-
(AWS.Response)
—
Returns:
describeHumanTaskUi(params = {}, callback) ⇒ AWS.Request
Returns information about the requested human task user interface (worker task template).
Service Reference:
Examples:
Calling the describeHumanTaskUi operation
var params = { HumanTaskUiName: 'STRING_VALUE' /* required */ }; sagemaker.describeHumanTaskUi(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HumanTaskUiName
— (String
)The name of the human task user interface (worker task template) you want information about.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HumanTaskUiArn
— (String
)The Amazon Resource Name (ARN) of the human task user interface (worker task template).
HumanTaskUiName
— (String
)The name of the human task user interface (worker task template).
HumanTaskUiStatus
— (String
)The status of the human task user interface (worker task template). Valid values are listed below.
Possible values include:"Active"
"Deleting"
CreationTime
— (Date
)The timestamp when the human task user interface was created.
UiTemplate
— (map
)Container for user interface template information.
Url
— (String
)The URL for the user interface template.
ContentSha256
— (String
)The SHA-256 digest of the contents of the template.
-
(AWS.Response)
—
Returns:
describeHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.
Service Reference:
Examples:
Calling the describeHyperParameterTuningJob operation
var params = { HyperParameterTuningJobName: 'STRING_VALUE' /* required */ }; sagemaker.describeHyperParameterTuningJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HyperParameterTuningJobName
— (String
)The name of the tuning job.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HyperParameterTuningJobName
— (String
)The name of the hyperparameter tuning job.
HyperParameterTuningJobArn
— (String
)The Amazon Resource Name (ARN) of the tuning job.
HyperParameterTuningJobConfig
— (map
)The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.
Strategy
— required — (String
)Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.
Possible values include:"Bayesian"
"Random"
"Hyperband"
"Grid"
StrategyConfig
— (map
)The configuration for the
Hyperband
optimization strategy. This parameter should be provided only ifHyperband
is selected as the strategy forHyperParameterTuningJobConfig
.HyperbandStrategyConfig
— (map
)The configuration for the object that specifies the
Hyperband
strategy. This parameter is only supported for theHyperband
selection forStrategy
within theHyperParameterTuningJobConfig
API.MinResource
— (Integer
)The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for
MinResource
has not been reached, the training job is not stopped byHyperband
.MaxResource
— (Integer
)The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the
MaxResource
value, it is stopped. If a value forMaxResource
is not provided, andHyperband
is selected as the hyperparameter tuning strategy,HyperbandTraining
attempts to inferMaxResource
from the following keys (if present) in StaticsHyperParameters:-
epochs
-
numepochs
-
n-epochs
-
n_epochs
-
num_epochs
If
HyperbandStrategyConfig
is unable to infer a value forMaxResource
, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions. For distributed training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.-
HyperParameterTuningJobObjective
— (map
)The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
ResourceLimits
— required — (map
)The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
MaxNumberOfTrainingJobs
— (Integer
)The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs
— required — (Integer
)The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds
— (Integer
)The maximum time in seconds that a hyperparameter tuning job can run.
ParameterRanges
— (map
)The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
AutoParameters
— (Array<map>
)A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
Name
— required — (String
)The name of the hyperparameter to optimize using Autotune.
ValueHint
— required — (String
)An example value of the hyperparameter to optimize using Autotune.
TrainingJobEarlyStoppingType
— (String
)Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the
Hyperband
strategy has its own advanced internal early stopping mechanism,TrainingJobEarlyStoppingType
must beOFF
to useHyperband
. This parameter can take on one of the following values (the default value isOFF
):- OFF
-
Training jobs launched by the hyperparameter tuning job do not use early stopping.
- AUTO
-
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.
"Off"
"Auto"
TuningJobCompletionCriteria
— (map
)The tuning job's completion criteria.
TargetObjectiveMetricValue
— (Float
)The value of the objective metric.
BestObjectiveNotImproving
— (map
)A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
MaxNumberOfTrainingJobsNotImproving
— (Integer
)The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
ConvergenceDetected
— (map
)A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
CompleteOnConvergence
— (String
)A flag to stop a tuning job once AMT has detected that the job has converged.
Possible values include:"Disabled"
"Enabled"
RandomSeed
— (Integer
)A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
TrainingJobDefinition
— (map
)The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
DefinitionName
— (String
)The job definition name.
TuningObjective
— (map
)Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the
Type
parameter. If you want to define a custom objective metric, see Define metrics and environment variables.Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
HyperParameterRanges
— (map
)Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note: The maximum number of items specified forArray Members
refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
AutoParameters
— (Array<map>
)A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
Name
— required — (String
)The name of the hyperparameter to optimize using Autotune.
ValueHint
— required — (String
)An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters
— (map<String>
)Specifies the values of hyperparameters that do not change for the tuning job.
AlgorithmSpecification
— required — (map
)The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
AlgorithmName
— (String
)The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for
TrainingImage
.MetricDefinitions
— (Array<map>
)An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig
— (Array<map>
)An array of Channel objects that specify the input for the training jobs that the tuning job launches.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
VpcConfig
— (map
)The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
OutputDataConfig
— required — (map
)Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.Note: If you want to use hyperparameter optimization with instance type flexibility, useHyperParameterTuningResourceConfig
instead.InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
HyperParameterTuningResourceConfig
— (map
)The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose
File
forTrainingInputMode
in theAlgorithmSpecification
parameter to additionally store training data in the storage volume (optional).InstanceType
— (String
)The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of compute instances of type
InstanceType
to use. For distributed training, select a value greater than 1.VolumeSizeInGB
— (Integer
)The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for
InstanceConfigs
is also specified.Some instance types have a fixed total local storage size. If you select one of these instances for training,
VolumeSizeInGB
cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.Note: SageMaker supports only the General Purpose SSD (gp2) storage volume type.VolumeKmsKeyId
— (String
)A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a
VolumeKmsKeyId
. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.AllocationStrategy
— (String
)The strategy that determines the order of preference for resources specified in
Possible values include:InstanceConfigs
used in hyperparameter optimization."Prioritized"
InstanceConfigs
— (Array<map>
)A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The
AllocationStrategy
controls the order in which multiple configurations provided inInstanceConfigs
are used.Note: If you only want to use a single instance configuration inside theHyperParameterTuningResourceConfig
API, do not provide a value forInstanceConfigs
. Instead, useInstanceType
,VolumeSizeInGB
andInstanceCount
. If you useInstanceConfigs
, do not provide values forInstanceType
,VolumeSizeInGB
orInstanceCount
.InstanceType
— required — (String
)The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)The number of instances of the type specified by
InstanceType
. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.VolumeSizeInGB
— required — (Integer
)The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition
— required — (map
)Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation
— (Boolean
)Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.EnableManagedSpotTraining
— (Boolean
)A Boolean indicating whether managed spot training is enabled (
True
) or not (False
).CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
Environment
— (map<String>
)An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note: The maximum number of items specified forMap Entries
refers to the maximum number of environment variables for eachTrainingJobDefinition
and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
TrainingJobDefinitions
— (Array<map>
)A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
DefinitionName
— (String
)The job definition name.
TuningObjective
— (map
)Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the
Type
parameter. If you want to define a custom objective metric, see Define metrics and environment variables.Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
HyperParameterRanges
— (map
)Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note: The maximum number of items specified forArray Members
refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
AutoParameters
— (Array<map>
)A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
Name
— required — (String
)The name of the hyperparameter to optimize using Autotune.
ValueHint
— required — (String
)An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters
— (map<String>
)Specifies the values of hyperparameters that do not change for the tuning job.
AlgorithmSpecification
— required — (map
)The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
AlgorithmName
— (String
)The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for
TrainingImage
.MetricDefinitions
— (Array<map>
)An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig
— (Array<map>
)An array of Channel objects that specify the input for the training jobs that the tuning job launches.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
VpcConfig
— (map
)The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
OutputDataConfig
— required — (map
)Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.Note: If you want to use hyperparameter optimization with instance type flexibility, useHyperParameterTuningResourceConfig
instead.InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
HyperParameterTuningResourceConfig
— (map
)The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose
File
forTrainingInputMode
in theAlgorithmSpecification
parameter to additionally store training data in the storage volume (optional).InstanceType
— (String
)The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of compute instances of type
InstanceType
to use. For distributed training, select a value greater than 1.VolumeSizeInGB
— (Integer
)The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for
InstanceConfigs
is also specified.Some instance types have a fixed total local storage size. If you select one of these instances for training,
VolumeSizeInGB
cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.Note: SageMaker supports only the General Purpose SSD (gp2) storage volume type.VolumeKmsKeyId
— (String
)A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a
VolumeKmsKeyId
. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.AllocationStrategy
— (String
)The strategy that determines the order of preference for resources specified in
Possible values include:InstanceConfigs
used in hyperparameter optimization."Prioritized"
InstanceConfigs
— (Array<map>
)A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The
AllocationStrategy
controls the order in which multiple configurations provided inInstanceConfigs
are used.Note: If you only want to use a single instance configuration inside theHyperParameterTuningResourceConfig
API, do not provide a value forInstanceConfigs
. Instead, useInstanceType
,VolumeSizeInGB
andInstanceCount
. If you useInstanceConfigs
, do not provide values forInstanceType
,VolumeSizeInGB
orInstanceCount
.InstanceType
— required — (String
)The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)The number of instances of the type specified by
InstanceType
. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.VolumeSizeInGB
— required — (Integer
)The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition
— required — (map
)Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation
— (Boolean
)Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.EnableManagedSpotTraining
— (Boolean
)A Boolean indicating whether managed spot training is enabled (
True
) or not (False
).CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
Environment
— (map<String>
)An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note: The maximum number of items specified forMap Entries
refers to the maximum number of environment variables for eachTrainingJobDefinition
and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
HyperParameterTuningJobStatus
— (String
)The status of the tuning job.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
"Deleting"
"DeleteFailed"
CreationTime
— (Date
)The date and time that the tuning job started.
HyperParameterTuningEndTime
— (Date
)The date and time that the tuning job ended.
LastModifiedTime
— (Date
)The date and time that the status of the tuning job was modified.
TrainingJobStatusCounters
— (map
)The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.
Completed
— (Integer
)The number of completed training jobs launched by the hyperparameter tuning job.
InProgress
— (Integer
)The number of in-progress training jobs launched by a hyperparameter tuning job.
RetryableError
— (Integer
)The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
NonRetryableError
— (Integer
)The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Stopped
— (Integer
)The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
ObjectiveStatusCounters
— (map
)The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.
Succeeded
— (Integer
)The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending
— (Integer
)The number of training jobs that are in progress and pending evaluation of their final objective metric.
Failed
— (Integer
)The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
BestTrainingJob
— (map
)A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.
TrainingJobDefinitionName
— (String
)The training job definition name.
TrainingJobName
— required — (String
)The name of the training job.
TrainingJobArn
— required — (String
)The Amazon Resource Name (ARN) of the training job.
TuningJobName
— (String
)The HyperParameter tuning job that launched the training job.
CreationTime
— required — (Date
)The date and time that the training job was created.
TrainingStartTime
— (Date
)The date and time that the training job started.
TrainingEndTime
— (Date
)Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of
TrainingStartTime
and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.TrainingJobStatus
— required — (String
)The status of the training job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
TunedHyperParameters
— required — (map<String>
)A list of the hyperparameters for which you specified ranges to search.
FailureReason
— (String
)The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric
— (map
)The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type
— (String
)Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.
Value
— required — (Float
)The value of the objective metric.
ObjectiveStatus
— (String
)The status of the objective metric for the training job:
-
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
-
Pending: The training job is in progress and evaluation of its final objective metric is pending.
-
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
"Succeeded"
"Pending"
"Failed"
-
OverallBestTrainingJob
— (map
)If the hyperparameter tuning job is an warm start tuning job with a
WarmStartType
ofIDENTICAL_DATA_AND_ALGORITHM
, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.TrainingJobDefinitionName
— (String
)The training job definition name.
TrainingJobName
— required — (String
)The name of the training job.
TrainingJobArn
— required — (String
)The Amazon Resource Name (ARN) of the training job.
TuningJobName
— (String
)The HyperParameter tuning job that launched the training job.
CreationTime
— required — (Date
)The date and time that the training job was created.
TrainingStartTime
— (Date
)The date and time that the training job started.
TrainingEndTime
— (Date
)Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of
TrainingStartTime
and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.TrainingJobStatus
— required — (String
)The status of the training job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
TunedHyperParameters
— required — (map<String>
)A list of the hyperparameters for which you specified ranges to search.
FailureReason
— (String
)The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric
— (map
)The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type
— (String
)Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.
Value
— required — (Float
)The value of the objective metric.
ObjectiveStatus
— (String
)The status of the objective metric for the training job:
-
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
-
Pending: The training job is in progress and evaluation of its final objective metric is pending.
-
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
"Succeeded"
"Pending"
"Failed"
-
WarmStartConfig
— (map
)The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
ParentHyperParameterTuningJobs
— required — (Array<map>
)An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
HyperParameterTuningJobName
— (String
)The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
WarmStartType
— required — (String
)Specifies one of the following:
- IDENTICAL_DATA_AND_ALGORITHM
-
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
- TRANSFER_LEARNING
-
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
"IdenticalDataAndAlgorithm"
"TransferLearning"
Autotune
— (map
)A flag to indicate if autotune is enabled for the hyperparameter tuning job.
Mode
— required — (String
)Set
Possible values include:Mode
toEnabled
if you want to use Autotune."Enabled"
FailureReason
— (String
)If the tuning job failed, the reason it failed.
TuningJobCompletionDetails
— (map
)Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.
NumberOfTrainingJobsObjectiveNotImproving
— (Integer
)The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.
ConvergenceDetectedTime
— (Date
)The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.
ConsumedResources
— (map
)The total resources consumed by your hyperparameter tuning job.
RuntimeInSeconds
— (Integer
)The wall clock runtime in seconds used by your hyperparameter tuning job.
-
(AWS.Response)
—
Returns:
describeImage(params = {}, callback) ⇒ AWS.Request
Describes a SageMaker image.
Service Reference:
Examples:
Calling the describeImage operation
var params = { ImageName: 'STRING_VALUE' /* required */ }; sagemaker.describeImage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ImageName
— (String
)The name of the image to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CreationTime
— (Date
)When the image was created.
Description
— (String
)The description of the image.
DisplayName
— (String
)The name of the image as displayed.
FailureReason
— (String
)When a create, update, or delete operation fails, the reason for the failure.
ImageArn
— (String
)The ARN of the image.
ImageName
— (String
)The name of the image.
ImageStatus
— (String
)The status of the image.
Possible values include:"CREATING"
"CREATED"
"CREATE_FAILED"
"UPDATING"
"UPDATE_FAILED"
"DELETING"
"DELETE_FAILED"
LastModifiedTime
— (Date
)When the image was last modified.
RoleArn
— (String
)The ARN of the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
-
(AWS.Response)
—
Returns:
Waiter Resource States:
describeImageVersion(params = {}, callback) ⇒ AWS.Request
Describes a version of a SageMaker image.
Service Reference:
Examples:
Calling the describeImageVersion operation
var params = { ImageName: 'STRING_VALUE', /* required */ Alias: 'STRING_VALUE', Version: 'NUMBER_VALUE' }; sagemaker.describeImageVersion(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ImageName
— (String
)The name of the image.
Version
— (Integer
)The version of the image. If not specified, the latest version is described.
Alias
— (String
)The alias of the image version.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:BaseImage
— (String
)The registry path of the container image on which this image version is based.
ContainerImage
— (String
)The registry path of the container image that contains this image version.
CreationTime
— (Date
)When the version was created.
FailureReason
— (String
)When a create or delete operation fails, the reason for the failure.
ImageArn
— (String
)The ARN of the image the version is based on.
ImageVersionArn
— (String
)The ARN of the version.
ImageVersionStatus
— (String
)The status of the version.
Possible values include:"CREATING"
"CREATED"
"CREATE_FAILED"
"DELETING"
"DELETE_FAILED"
LastModifiedTime
— (Date
)When the version was last modified.
Version
— (Integer
)The version number.
VendorGuidance
— (String
)The stability of the image version specified by the maintainer.
-
NOT_PROVIDED
: The maintainers did not provide a status for image version stability. -
STABLE
: The image version is stable. -
TO_BE_ARCHIVED
: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months. -
ARCHIVED
: The image version is archived. Archived image versions are not searchable and are no longer actively supported.
"NOT_PROVIDED"
"STABLE"
"TO_BE_ARCHIVED"
"ARCHIVED"
-
JobType
— (String
)Indicates SageMaker job type compatibility.
-
TRAINING
: The image version is compatible with SageMaker training jobs. -
INFERENCE
: The image version is compatible with SageMaker inference jobs. -
NOTEBOOK_KERNEL
: The image version is compatible with SageMaker notebook kernels.
"TRAINING"
"INFERENCE"
"NOTEBOOK_KERNEL"
-
MLFramework
— (String
)The machine learning framework vended in the image version.
ProgrammingLang
— (String
)The supported programming language and its version.
Processor
— (String
)Indicates CPU or GPU compatibility.
-
CPU
: The image version is compatible with CPU. -
GPU
: The image version is compatible with GPU.
"CPU"
"GPU"
-
Horovod
— (Boolean
)Indicates Horovod compatibility.
ReleaseNotes
— (String
)The maintainer description of the image version.
-
(AWS.Response)
—
Returns:
Waiter Resource States:
describeInferenceComponent(params = {}, callback) ⇒ AWS.Request
Returns information about an inference component.
Service Reference:
Examples:
Calling the describeInferenceComponent operation
var params = { InferenceComponentName: 'STRING_VALUE' /* required */ }; sagemaker.describeInferenceComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
InferenceComponentName
— (String
)The name of the inference component.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceComponentName
— (String
)The name of the inference component.
InferenceComponentArn
— (String
)The Amazon Resource Name (ARN) of the inference component.
EndpointName
— (String
)The name of the endpoint that hosts the inference component.
EndpointArn
— (String
)The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
VariantName
— (String
)The name of the production variant that hosts the inference component.
FailureReason
— (String
)If the inference component status is
Failed
, the reason for the failure.Specification
— (map
)Details about the resources that are deployed with this inference component.
ModelName
— (String
)The name of the SageMaker model object that is deployed with the inference component.
Container
— (map
)Details about the container that provides the runtime environment for the model that is deployed with the inference component.
DeployedImage
— (map
)Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant.
If you used the
registry/repository[:tag]
form to specify the image path of the primary container when you created the model hosted in thisProductionVariant
, the path resolves to a path of the formregistry/repository[@digest]
. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
ArtifactUrl
— (String
)The Amazon S3 path where the model artifacts are stored.
Environment
— (map<String>
)The environment variables to set in the Docker container.
StartupParameters
— (map
)Settings that take effect while the model container starts up.
ModelDataDownloadTimeoutInSeconds
— (Integer
)The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.
ContainerStartupHealthCheckTimeoutInSeconds
— (Integer
)The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
ComputeResourceRequirements
— (map
)The compute resources allocated to run the model assigned to the inference component.
NumberOfCpuCoresRequired
— (Float
)The number of CPU cores to allocate to run a model that you assign to an inference component.
NumberOfAcceleratorDevicesRequired
— (Float
)The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia.
MinMemoryRequiredInMb
— required — (Integer
)The minimum MB of memory to allocate to run a model that you assign to an inference component.
MaxMemoryRequiredInMb
— (Integer
)The maximum MB of memory to allocate to run a model that you assign to an inference component.
RuntimeConfig
— (map
)Details about the runtime settings for the model that is deployed with the inference component.
DesiredCopyCount
— (Integer
)The number of runtime copies of the model container that you requested to deploy with the inference component.
CurrentCopyCount
— (Integer
)The number of runtime copies of the model container that are currently deployed.
CreationTime
— (Date
)The time when the inference component was created.
LastModifiedTime
— (Date
)The time when the inference component was last updated.
InferenceComponentStatus
— (String
)The status of the inference component.
Possible values include:"InService"
"Creating"
"Updating"
"Failed"
"Deleting"
-
(AWS.Response)
—
Returns:
describeInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Returns details about an inference experiment.
Service Reference:
Examples:
Calling the describeInferenceExperiment operation
var params = { Name: 'STRING_VALUE' /* required */ }; sagemaker.describeInferenceExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
Name
— (String
)The name of the inference experiment to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Arn
— (String
)The ARN of the inference experiment being described.
Name
— (String
)The name of the inference experiment.
Type
— (String
)The type of the inference experiment.
Possible values include:"ShadowMode"
Schedule
— (map
)The duration for which the inference experiment ran or will run.
StartTime
— (Date
)The timestamp at which the inference experiment started or will start.
EndTime
— (Date
)The timestamp at which the inference experiment ended or will end.
Status
— (String
)The status of the inference experiment. The following are the possible statuses for an inference experiment:
-
Creating
- Amazon SageMaker is creating your experiment. -
Created
- Amazon SageMaker has finished the creation of your experiment and will begin the experiment at the scheduled time. -
Updating
- When you make changes to your experiment, your experiment shows as updating. -
Starting
- Amazon SageMaker is beginning your experiment. -
Running
- Your experiment is in progress. -
Stopping
- Amazon SageMaker is stopping your experiment. -
Completed
- Your experiment has completed. -
Cancelled
- When you conclude your experiment early using the StopInferenceExperiment API, or if any operation fails with an unexpected error, it shows as cancelled.
"Creating"
"Created"
"Updating"
"Running"
"Starting"
"Stopping"
"Completed"
"Cancelled"
-
StatusReason
— (String
)The error message or client-specified
Reason
from the StopInferenceExperiment API, that explains the status of the inference experiment.Description
— (String
)The description of the inference experiment.
CreationTime
— (Date
)The timestamp at which you created the inference experiment.
CompletionTime
— (Date
)The timestamp at which the inference experiment was completed.
LastModifiedTime
— (Date
)The timestamp at which you last modified the inference experiment.
RoleArn
— (String
)The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
EndpointMetadata
— (map
)The metadata of the endpoint on which the inference experiment ran.
EndpointName
— required — (String
)The name of the endpoint.
EndpointConfigName
— (String
)The name of the endpoint configuration.
EndpointStatus
— (String
)The status of the endpoint. For possible values of the status of an endpoint, see EndpointSummary.
Possible values include:"OutOfService"
"Creating"
"Updating"
"SystemUpdating"
"RollingBack"
"InService"
"Deleting"
"Failed"
"UpdateRollbackFailed"
FailureReason
— (String
)If the status of the endpoint is
Failed
, or the status isInService
but update operation fails, this provides the reason why it failed.
ModelVariants
— (Array<map>
)An array of
ModelVariantConfigSummary
objects. There is one for each variant in the inference experiment. EachModelVariantConfigSummary
object in the array describes the infrastructure configuration for deploying the corresponding variant.ModelName
— required — (String
)The name of the Amazon SageMaker Model entity.
VariantName
— required — (String
)The name of the variant.
InfrastructureConfig
— required — (map
)The configuration of the infrastructure that the model has been deployed to.
InfrastructureType
— required — (String
)The inference option to which to deploy your model. Possible values are the following:
-
RealTime
: Deploy to real-time inference.
"RealTimeInference"
-
RealTimeInferenceConfig
— required — (map
)The infrastructure configuration for deploying the model to real-time inference.
InstanceType
— required — (String
)The instance type the model is deployed to.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
InstanceCount
— required — (Integer
)The number of instances of the type specified by
InstanceType
.
Status
— required — (String
)The status of deployment for the model variant on the hosted inference endpoint.
-
Creating
- Amazon SageMaker is preparing the model variant on the hosted inference endpoint. -
InService
- The model variant is running on the hosted inference endpoint. -
Updating
- Amazon SageMaker is updating the model variant on the hosted inference endpoint. -
Deleting
- Amazon SageMaker is deleting the model variant on the hosted inference endpoint. -
Deleted
- The model variant has been deleted on the hosted inference endpoint. This can only happen after stopping the experiment.
"Creating"
"Updating"
"InService"
"Deleting"
"Deleted"
-
DataStorageConfig
— (map
)The Amazon S3 location and configuration for storing inference request and response data.
Destination
— required — (String
)The Amazon S3 bucket where the inference request and response data is stored.
KmsKey
— (String
)The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
ContentType
— (map
)Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker will by default base64 encode when capturing the data.
CsvContentTypes
— (Array<String>
)The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.
JsonContentTypes
— (Array<String>
)The list of all content type headers that SageMaker will treat as JSON and capture accordingly.
ShadowModeConfig
— (map
)The configuration of
ShadowMode
inference experiment type, which shows the production variant that takes all the inference requests, and the shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also shows the percentage of requests that Amazon SageMaker replicates.SourceModelVariantName
— required — (String
)The name of the production variant, which takes all the inference requests.
ShadowModelVariants
— required — (Array<map>
)List of shadow variant configurations.
ShadowModelVariantName
— required — (String
)The name of the shadow variant.
SamplingPercentage
— required — (Integer
)The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant.
KmsKey
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. For more information, see CreateInferenceExperiment.
-
(AWS.Response)
—
Returns:
describeInferenceRecommendationsJob(params = {}, callback) ⇒ AWS.Request
Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
Service Reference:
Examples:
Calling the describeInferenceRecommendationsJob operation
var params = { JobName: 'STRING_VALUE' /* required */ }; sagemaker.describeInferenceRecommendationsJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobName
— (String
)The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobName
— (String
)The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
JobDescription
— (String
)The job description that you provided when you initiated the job.
JobType
— (String
)The job type that you provided when you initiated the job.
Possible values include:"Default"
"Advanced"
JobArn
— (String
)The Amazon Resource Name (ARN) of the job.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role you provided when you initiated the job.
Status
— (String
)The status of the job.
Possible values include:"PENDING"
"IN_PROGRESS"
"COMPLETED"
"FAILED"
"STOPPING"
"STOPPED"
"DELETING"
"DELETED"
CreationTime
— (Date
)A timestamp that shows when the job was created.
CompletionTime
— (Date
)A timestamp that shows when the job completed.
LastModifiedTime
— (Date
)A timestamp that shows when the job was last modified.
FailureReason
— (String
)If the job fails, provides information why the job failed.
InputConfig
— (map
)Returns information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations you provided when you initiated the job.
ModelPackageVersionArn
— (String
)The Amazon Resource Name (ARN) of a versioned model package.
ModelName
— (String
)The name of the created model.
JobDurationInSeconds
— (Integer
)Specifies the maximum duration of the job, in seconds. The maximum value is 18,000 seconds.
TrafficPattern
— (map
)Specifies the traffic pattern of the job.
TrafficType
— (String
)Defines the traffic patterns. Choose either
Possible values include:PHASES
orSTAIRS
."PHASES"
"STAIRS"
Phases
— (Array<map>
)Defines the phases traffic specification.
InitialNumberOfUsers
— (Integer
)Specifies how many concurrent users to start with. The value should be between 1 and 3.
SpawnRate
— (Integer
)Specified how many new users to spawn in a minute.
DurationInSeconds
— (Integer
)Specifies how long a traffic phase should be. For custom load tests, the value should be between 120 and 3600. This value should not exceed
JobDurationInSeconds
.
Stairs
— (map
)Defines the stairs traffic pattern.
DurationInSeconds
— (Integer
)Defines how long each traffic step should be.
NumberOfSteps
— (Integer
)Specifies how many steps to perform during traffic.
UsersPerStep
— (Integer
)Specifies how many new users to spawn in each step.
ResourceLimit
— (map
)Defines the resource limit of the job.
MaxNumberOfTests
— (Integer
)Defines the maximum number of load tests.
MaxParallelOfTests
— (Integer
)Defines the maximum number of parallel load tests.
EndpointConfigurations
— (Array<map>
)Specifies the endpoint configuration to use for a job.
InstanceType
— (String
)The instance types to use for the load test.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
ServerlessConfig
— (map
)Specifies the serverless configuration for an endpoint variant.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
EnvironmentParameterRanges
— (map
)The parameter you want to benchmark against.
CategoricalParameterRanges
— (Array<map>
)Specified a list of parameters for each category.
Name
— required — (String
)The Name of the environment variable.
Value
— required — (Array<String>
)The list of values you can pass.
VolumeKmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. This key will be passed to SageMaker Hosting for endpoint creation.
The SageMaker execution role must have
kms:CreateGrant
permission in order to encrypt data on the storage volume of the endpoints created for inference recommendation. The inference recommendation job will fail asynchronously during endpoint configuration creation if the role passed does not havekms:CreateGrant
permission.The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"
For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
-
ContainerConfig
— (map
)Specifies mandatory fields for running an Inference Recommender job. The fields specified in
ContainerConfig
override the corresponding fields in the model package.Domain
— (String
)The machine learning domain of the model and its components.
Valid Values:
COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING
Task
— (String
)The machine learning task that the model accomplishes.
Valid Values:
IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER
Framework
— (String
)The machine learning framework of the container image.
Valid Values:
TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN
FrameworkVersion
— (String
)The framework version of the container image.
PayloadConfig
— (map
)Specifies the
SamplePayloadUrl
and all other sample payload-related fields.SamplePayloadUrl
— (String
)The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
NearestModelName
— (String
)The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.
Valid Values:
efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet
SupportedInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
SupportedEndpointType
— (String
)The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.
Possible values include:"RealTime"
"Serverless"
DataInputConfig
— (String
)Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
Endpoints
— (Array<map>
)Existing customer endpoints on which to run an Inference Recommender job.
EndpointName
— (String
)The name of a customer's endpoint.
VpcConfig
— (map
)Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs. IDs have the form of
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your model.
StoppingConditions
— (map
)The stopping conditions that you provided when you initiated the job.
MaxInvocations
— (Integer
)The maximum number of requests per minute expected for the endpoint.
ModelLatencyThresholds
— (Array<map>
)The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
Percentile
— (String
)The model latency percentile threshold. Acceptable values are
P95
andP99
. For custom load tests, specify the value asP95
.ValueInMilliseconds
— (Integer
)The model latency percentile value in milliseconds.
FlatInvocations
— (String
)Stops a load test when the number of invocations (TPS) peaks and flattens, which means that the instance has reached capacity. The default value is
Possible values include:Stop
. If you want the load test to continue after invocations have flattened, set the value toContinue
."Continue"
"Stop"
InferenceRecommendations
— (Array<map>
)The recommendations made by Inference Recommender.
RecommendationId
— (String
)The recommendation ID which uniquely identifies each recommendation.
Metrics
— (map
)The metrics used to decide what recommendation to make.
CostPerHour
— (Float
)Defines the cost per hour for the instance.
CostPerInference
— (Float
)Defines the cost per inference for the instance .
MaxInvocations
— (Integer
)The expected maximum number of requests per minute for the instance.
ModelLatency
— (Integer
)The expected model latency at maximum invocation per minute for the instance.
CpuUtilization
— (Float
)The expected CPU utilization at maximum invocations per minute for the instance.
NaN
indicates that the value is not available.MemoryUtilization
— (Float
)The expected memory utilization at maximum invocations per minute for the instance.
NaN
indicates that the value is not available.ModelSetupTime
— (Integer
)The time it takes to launch new compute resources for a serverless endpoint. The time can vary depending on the model size, how long it takes to download the model, and the start-up time of the container.
NaN
indicates that the value is not available.
EndpointConfiguration
— required — (map
)Defines the endpoint configuration parameters.
EndpointName
— required — (String
)The name of the endpoint made during a recommendation job.
VariantName
— required — (String
)The name of the production variant (deployed model) made during a recommendation job.
InstanceType
— (String
)The instance type recommended by Amazon SageMaker Inference Recommender.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
InitialInstanceCount
— (Integer
)The number of instances recommended to launch initially.
ServerlessConfig
— (map
)Specifies the serverless configuration for an endpoint variant.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ModelConfiguration
— required — (map
)Defines the model configuration.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
EnvironmentParameters
— (Array<map>
)Defines the environment parameters that includes key, value types, and values.
Key
— required — (String
)The environment key suggested by the Amazon SageMaker Inference Recommender.
ValueType
— required — (String
)The value type suggested by the Amazon SageMaker Inference Recommender.
Value
— required — (String
)The value suggested by the Amazon SageMaker Inference Recommender.
CompilationJobName
— (String
)The name of the compilation job used to create the recommended model artifacts.
InvocationEndTime
— (Date
)A timestamp that shows when the benchmark completed.
InvocationStartTime
— (Date
)A timestamp that shows when the benchmark started.
EndpointPerformances
— (Array<map>
)The performance results from running an Inference Recommender job on an existing endpoint.
Metrics
— required — (map
)The metrics for an existing endpoint.
MaxInvocations
— required — (Integer
)The expected maximum number of requests per minute for the instance.
ModelLatency
— required — (Integer
)The expected model latency at maximum invocations per minute for the instance.
EndpointInfo
— required — (map
)Details about a customer endpoint that was compared in an Inference Recommender job.
EndpointName
— (String
)The name of a customer's endpoint.
-
(AWS.Response)
—
Returns:
describeLabelingJob(params = {}, callback) ⇒ AWS.Request
Gets information about a labeling job.
Service Reference:
Examples:
Calling the describeLabelingJob operation
var params = { LabelingJobName: 'STRING_VALUE' /* required */ }; sagemaker.describeLabelingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
LabelingJobName
— (String
)The name of the labeling job to return information for.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:LabelingJobStatus
— (String
)The processing status of the labeling job.
Possible values include:"Initializing"
"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
LabelCounters
— (map
)Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn't be labeled, and the total number of objects labeled.
TotalLabeled
— (Integer
)The total number of objects labeled.
HumanLabeled
— (Integer
)The total number of objects labeled by a human worker.
MachineLabeled
— (Integer
)The total number of objects labeled by automated data labeling.
FailedNonRetryableError
— (Integer
)The total number of objects that could not be labeled due to an error.
Unlabeled
— (Integer
)The total number of objects not yet labeled.
FailureReason
— (String
)If the job failed, the reason that it failed.
CreationTime
— (Date
)The date and time that the labeling job was created.
LastModifiedTime
— (Date
)The date and time that the labeling job was last updated.
JobReferenceCode
— (String
)A unique identifier for work done as part of a labeling job.
LabelingJobName
— (String
)The name assigned to the labeling job when it was created.
LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the labeling job.
LabelAttributeName
— (String
)The attribute used as the label in the output manifest file.
InputConfig
— (map
)Input configuration information for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
DataSource
— required — (map
)The location of the input data.
S3DataSource
— (map
)The Amazon S3 location of the input data objects.
ManifestS3Uri
— required — (String
)The Amazon S3 location of the manifest file that describes the input data objects.
The input manifest file referenced in
ManifestS3Uri
must contain one of the following keys:source-ref
orsource
. The value of the keys are interpreted as follows:-
source-ref
: The source of the object is the Amazon S3 object specified in the value. Use this value when the object is a binary object, such as an image. -
source
: The source of the object is the value. Use this value when the object is a text value.
If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
-
SnsDataSource
— (map
)An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job.
SnsTopicArn
— required — (String
)The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.
DataAttributes
— (map
)Attributes of the data specified by the customer.
ContentClassifiers
— (Array<String>
)Declares that your content is free of personally identifiable information or adult content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
OutputConfig
— (map
)The location of the job's output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
S3OutputPath
— required — (String
)The Amazon S3 location to write output data.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any.
If you provide your own KMS key ID, you must add the required permissions to your KMS key described in Encrypt Output Data and Storage Volume with Amazon Web Services KMS.
If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your role's account to encrypt your output data.
If you use a bucket policy with an
s3:PutObject
permission that only allows objects with server-side encryption, set the condition key ofs3:x-amz-server-side-encryption
to"aws:kms"
. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.SnsTopicArn
— (String
)An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a
SnsTopicArn
if you want to do real time chaining to another streaming job and receive an Amazon SNS notifications each time a data object is submitted by a worker.If you provide an
SnsTopicArn
inOutputConfig
, when workers complete labeling tasks, Ground Truth will send labeling task output data to the SNS output topic you specify here.To learn more, see Receive Output Data from a Streaming Labeling Job.
RoleArn
— (String
)The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during data labeling.
LabelCategoryConfigS3Uri
— (String
)The S3 location of the JSON file that defines the categories used to label data objects. Please note the following label-category limits:
-
Semantic segmentation labeling jobs using automated labeling: 20 labels
-
Box bounding labeling jobs (all): 10 labels
The file is a JSON structure in the following format:
{
"document-version": "2018-11-28"
"labels": [
{
"label": "label 1"
},
{
"label": "label 2"
},
...
{
"label": "label n"
}
]
}
-
StoppingConditions
— (map
)A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.
MaxHumanLabeledObjectCount
— (Integer
)The maximum number of objects that can be labeled by human workers.
MaxPercentageOfInputDatasetLabeled
— (Integer
)The maximum number of input data objects that should be labeled.
LabelingJobAlgorithmsConfig
— (map
)Configuration information for automated data labeling.
LabelingJobAlgorithmSpecificationArn
— required — (String
)Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:
-
Image classification
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification
-
Text classification
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification
-
Object detection
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection
-
Semantic Segmentation
arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation
-
InitialActiveLearningModelArn
— (String
)At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.
LabelingJobResourceConfig
— (map
)Provides configuration information for a labeling job.
VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training and inference jobs used for automated data labeling.
You can only specify a
VolumeKmsKeyId
when you create a labeling job with automated data labeling enabled using the API operationCreateLabelingJob
. You cannot specify an Amazon Web Services KMS key to encrypt the storage volume used for automated data labeling model training and inference when you create a labeling job using the console. To learn more, see Output Data and Storage Volume Encryption.The
VolumeKmsKeyId
can be any of the following formats:-
KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
HumanTaskConfig
— (map
)Configuration information required for human workers to complete a labeling task.
WorkteamArn
— required — (String
)The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
UiConfig
— required — (map
)Information about the user interface that workers use to complete the labeling task.
UiTemplateS3Uri
— (String
)The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template.
HumanTaskUiArn
— (String
)The ARN of the worker task template used to render the worker UI and tools for labeling job tasks.
Use this parameter when you are creating a labeling job for named entity recognition, 3D point cloud and video frame labeling jobs. Use your labeling job task type to select one of the following ARNs and use it with this parameter when you create a labeling job. Replace
aws-region
with the Amazon Web Services Region you are creating your labeling job in. For example, replaceaws-region
withus-west-1
if you create a labeling job in US West (N. California).Named Entity Recognition
Use the following
HumanTaskUiArn
for named entity recognition labeling jobs:arn:aws:sagemaker:aws-region:394669845002:human-task-ui/NamedEntityRecognition
3D Point Cloud HumanTaskUiArns
Use this
HumanTaskUiArn
for 3D point cloud object detection and 3D point cloud object detection adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection
Use this
HumanTaskUiArn
for 3D point cloud object tracking and 3D point cloud object tracking adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking
Use this
HumanTaskUiArn
for 3D point cloud semantic segmentation and 3D point cloud semantic segmentation adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation
Video Frame HumanTaskUiArns
Use this
HumanTaskUiArn
for video frame object detection and video frame object detection adjustment labeling jobs.-
arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetection
Use this
HumanTaskUiArn
for video frame object tracking and video frame object tracking adjustment labeling jobs.-
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTracking
-
PreHumanTaskLambdaArn
— required — (String
)The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.
For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for
PreHumanTaskLambdaArn
. For custom labeling workflows, see Pre-annotation Lambda.Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking
3D Point Cloud Modalities
Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types to learn more.
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .
Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBox
Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox
Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation
Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking
3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection
3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking
3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud.
-
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation
-
TaskKeywords
— (Array<String>
)Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
TaskTitle
— required — (String
)A title for the task for your human workers.
TaskDescription
— required — (String
)A description of the task for your human workers.
NumberOfHumanWorkersPerDataObject
— required — (Integer
)The number of human workers that will label an object.
TaskTimeLimitInSeconds
— required — (Integer
)The amount of time that a worker has to complete a task.
If you create a custom labeling job, the maximum value for this parameter is 8 hours (28,800 seconds).
If you create a labeling job using a built-in task type the maximum for this parameter depends on the task type you use:
-
For image and text labeling jobs, the maximum is 8 hours (28,800 seconds).
-
For 3D point cloud and video frame labeling jobs, the maximum is 30 days (2952,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
-
TaskAvailabilityLifetimeInSeconds
— (Integer
)The length of time that a task remains available for labeling by human workers. The default and maximum values for this parameter depend on the type of workforce you use.
-
If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43,200 seconds). The default is 6 hours (21,600 seconds).
-
If you choose a private or vendor workforce, the default value is 30 days (2592,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
-
MaxConcurrentTaskCount
— (Integer
)Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects. To increase the maximum value to 5000 objects, contact Amazon Web Services Support.
AnnotationConsolidationConfig
— required — (map
)Configures how labels are consolidated across human workers.
AnnotationConsolidationLambdaArn
— required — (String
)The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.
This parameter is required for all labeling jobs. For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for
AnnotationConsolidationLambdaArn
. For custom labeling workflows, see Post-annotation Lambda.Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClass
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClass
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClass
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClass
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClass
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClass
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClass
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClass
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetection
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .
Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation
Semantic Segmentation Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation
Bounding Box Adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox
Bounding Box Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectDetection
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectTracking
3D Point Cloud Object Detection Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetection
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection
3D Point Cloud Object Tracking Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTracking
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking
3D Point Cloud Semantic Segmentation Adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool.
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation
-
PublicWorkforceTaskPrice
— (map
)The price that you pay for each task performed by an Amazon Mechanical Turk worker.
AmountInUsd
— (map
)Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
Dollars
— (Integer
)The whole number of dollars in the amount.
Cents
— (Integer
)The fractional portion, in cents, of the amount.
TenthFractionsOfACent
— (Integer
)Fractions of a cent, in tenths.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
LabelingJobOutput
— (map
)The location of the output produced by the labeling job.
OutputDatasetS3Uri
— required — (String
)The Amazon S3 bucket location of the manifest file for labeled data.
FinalActiveLearningModelArn
— (String
)The Amazon Resource Name (ARN) for the most recent SageMaker model trained as part of automated data labeling.
-
(AWS.Response)
—
Returns:
describeLineageGroup(params = {}, callback) ⇒ AWS.Request
Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
Service Reference:
Examples:
Calling the describeLineageGroup operation
var params = { LineageGroupName: 'STRING_VALUE' /* required */ }; sagemaker.describeLineageGroup(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
LineageGroupName
— (String
)The name of the lineage group.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:LineageGroupName
— (String
)The name of the lineage group.
LineageGroupArn
— (String
)The Amazon Resource Name (ARN) of the lineage group.
DisplayName
— (String
)The display name of the lineage group.
Description
— (String
)The description of the lineage group.
CreationTime
— (Date
)The creation time of lineage group.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)The last modified time of the lineage group.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
-
(AWS.Response)
—
Returns:
describeMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Returns information about an MLflow Tracking Server.
Service Reference:
Examples:
Calling the describeMlflowTrackingServer operation
var params = { TrackingServerName: 'STRING_VALUE' /* required */ }; sagemaker.describeMlflowTrackingServer(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrackingServerName
— (String
)The name of the MLflow Tracking Server to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrackingServerArn
— (String
)The ARN of the described tracking server.
TrackingServerName
— (String
)The name of the described tracking server.
ArtifactStoreUri
— (String
)The S3 URI of the general purpose bucket used as the MLflow Tracking Server artifact store.
TrackingServerSize
— (String
)The size of the described tracking server.
Possible values include:"Small"
"Medium"
"Large"
MlflowVersion
— (String
)The MLflow version used for the described tracking server.
RoleArn
— (String
)The Amazon Resource Name (ARN) for an IAM role in your account that the described MLflow Tracking Server uses to access the artifact store in Amazon S3.
TrackingServerStatus
— (String
)The current creation status of the described MLflow Tracking Server.
Possible values include:"Creating"
"Created"
"CreateFailed"
"Updating"
"Updated"
"UpdateFailed"
"Deleting"
"DeleteFailed"
"Stopping"
"Stopped"
"StopFailed"
"Starting"
"Started"
"StartFailed"
"MaintenanceInProgress"
"MaintenanceComplete"
"MaintenanceFailed"
IsActive
— (String
)Whether the described MLflow Tracking Server is currently active.
Possible values include:"Active"
"Inactive"
TrackingServerUrl
— (String
)The URL to connect to the MLflow user interface for the described tracking server.
WeeklyMaintenanceWindowStart
— (String
)The day and time of the week when weekly maintenance occurs on the described tracking server.
AutomaticModelRegistration
— (Boolean
)Whether automatic registration of new MLflow models to the SageMaker Model Registry is enabled.
CreationTime
— (Date
)The timestamp of when the described MLflow Tracking Server was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)The timestamp of when the described MLflow Tracking Server was last modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
-
(AWS.Response)
—
Returns:
describeModel(params = {}, callback) ⇒ AWS.Request
Describes a model that you created using the
CreateModel
API.Service Reference:
Examples:
Calling the describeModel operation
var params = { ModelName: 'STRING_VALUE' /* required */ }; sagemaker.describeModel(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelName
— (String
)The name of the model.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelName
— (String
)Name of the SageMaker model.
PrimaryContainer
— (map
)The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.
ContainerHostname
— (String
)This parameter is ignored for models that contain only a
PrimaryContainer
.When a
ContainerDefinition
is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for aContainerDefinition
that is part of an inference pipeline, a unique name is automatically assigned based on the position of theContainerDefinition
in the pipeline. If you specify a value for theContainerHostName
for anyContainerDefinition
that is part of an inference pipeline, you must specify a value for theContainerHostName
parameter of everyContainerDefinition
in that pipeline.Image
— (String
)The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.ImageConfig
— (map
)Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.RepositoryAccessMode
— required — (String
)Set this to one of the following values:
-
Platform
- The model image is hosted in Amazon ECR. -
Vpc
- The model image is hosted in a private Docker registry in your VPC.
"Platform"
"Vpc"
-
RepositoryAuthConfig
— (map
)(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified
Vpc
as the value for theRepositoryAccessMode
field, and the private Docker registry where the model image is hosted requires authentication.RepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide.
Mode
— (String
)Whether the container hosts a single model or multiple models.
Possible values include:"SingleModel"
"MultiModel"
ModelDataUrl
— (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in
ModelDataUrl
.ModelDataSource
— (map
)Specifies the location of ML model data to deploy.
Note: Currently you cannot useModelDataSource
in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
AdditionalModelDataSources
— (Array<map>
)Data sources that are available to your model in addition to the one that you specify for
ModelDataSource
when you use theCreateModel
action.ChannelName
— required — (String
)A custom name for this
AdditionalModelDataSource
object.S3DataSource
— required — (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
Environment
— (map<String>
)The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.
The maximum length of each key and value in the
Environment
map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to aCreateModel
request, then the maximum length of all of their maps, combined, is also 32 KB.ModelPackageName
— (String
)The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
MultiModelConfig
— (map
)Specifies additional configuration for multi-model endpoints.
ModelCacheSetting
— (String
)Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to
Possible values include:Disabled
."Enabled"
"Disabled"
Containers
— (Array<map>
)The containers in the inference pipeline.
ContainerHostname
— (String
)This parameter is ignored for models that contain only a
PrimaryContainer
.When a
ContainerDefinition
is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for aContainerDefinition
that is part of an inference pipeline, a unique name is automatically assigned based on the position of theContainerDefinition
in the pipeline. If you specify a value for theContainerHostName
for anyContainerDefinition
that is part of an inference pipeline, you must specify a value for theContainerHostName
parameter of everyContainerDefinition
in that pipeline.Image
— (String
)The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.ImageConfig
— (map
)Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.RepositoryAccessMode
— required — (String
)Set this to one of the following values:
-
Platform
- The model image is hosted in Amazon ECR. -
Vpc
- The model image is hosted in a private Docker registry in your VPC.
"Platform"
"Vpc"
-
RepositoryAuthConfig
— (map
)(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified
Vpc
as the value for theRepositoryAccessMode
field, and the private Docker registry where the model image is hosted requires authentication.RepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide.
Mode
— (String
)Whether the container hosts a single model or multiple models.
Possible values include:"SingleModel"
"MultiModel"
ModelDataUrl
— (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in
ModelDataUrl
.ModelDataSource
— (map
)Specifies the location of ML model data to deploy.
Note: Currently you cannot useModelDataSource
in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
AdditionalModelDataSources
— (Array<map>
)Data sources that are available to your model in addition to the one that you specify for
ModelDataSource
when you use theCreateModel
action.ChannelName
— required — (String
)A custom name for this
AdditionalModelDataSource
object.S3DataSource
— required — (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
Environment
— (map<String>
)The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.
The maximum length of each key and value in the
Environment
map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to aCreateModel
request, then the maximum length of all of their maps, combined, is also 32 KB.ModelPackageName
— (String
)The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
MultiModelConfig
— (map
)Specifies additional configuration for multi-model endpoints.
ModelCacheSetting
— (String
)Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to
Possible values include:Disabled
."Enabled"
"Disabled"
InferenceExecutionConfig
— (map
)Specifies details of how containers in a multi-container endpoint are called.
Mode
— required — (String
)How containers in a multi-container are run. The following values are valid.
-
SERIAL
- Containers run as a serial pipeline. -
DIRECT
- Only the individual container that you specify is run.
"Serial"
"Direct"
-
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
VpcConfig
— (map
)A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
CreationTime
— (Date
)A timestamp that shows when the model was created.
ModelArn
— (String
)The Amazon Resource Name (ARN) of the model.
EnableNetworkIsolation
— (Boolean
)If
True
, no inbound or outbound network calls can be made to or from the model container.DeploymentRecommendation
— (map
)A set of recommended deployment configurations for the model.
RecommendationStatus
— required — (String
)Status of the deployment recommendation. The status
Possible values include:NOT_APPLICABLE
means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status isIN_PROGRESS
, retry your API call after a few seconds to get aCOMPLETED
deployment recommendation."IN_PROGRESS"
"COMPLETED"
"FAILED"
"NOT_APPLICABLE"
RealTimeInferenceRecommendations
— (Array<map>
)A list of RealTimeInferenceRecommendation items.
RecommendationId
— required — (String
)The recommendation ID which uniquely identifies each recommendation.
InstanceType
— required — (String
)The recommended instance type for Real-Time Inference.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
Environment
— (map<String>
)The recommended environment variables to set in the model container for Real-Time Inference.
-
(AWS.Response)
—
Returns:
describeModelBiasJobDefinition(params = {}, callback) ⇒ AWS.Request
Returns a description of a model bias job definition.
Service Reference:
Examples:
Calling the describeModelBiasJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE' /* required */ }; sagemaker.describeModelBiasJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the model bias job.
JobDefinitionName
— (String
)The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
CreationTime
— (Date
)The time at which the model bias job was created.
ModelBiasBaselineConfig
— (map
)The baseline configuration for a model bias job.
BaseliningJobName
— (String
)The name of the baseline model bias job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
ModelBiasAppSpecification
— (map
)Configures the model bias job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the model bias job.
ConfigUri
— required — (String
)JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
ModelBiasJobInput
— (map
)Inputs for the model bias job.
EndpointInput
— (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
GroundTruthS3Input
— required — (map
)Location of ground truth labels to use in model bias job.
S3Uri
— (String
)The address of the Amazon S3 location of the ground truth labels.
ModelBiasJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
NetworkConfig
— (map
)Networking options for a model bias job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
-
(AWS.Response)
—
Returns:
describeModelCard(params = {}, callback) ⇒ AWS.Request
Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
Service Reference:
Examples:
Calling the describeModelCard operation
var params = { ModelCardName: 'STRING_VALUE', /* required */ ModelCardVersion: 'NUMBER_VALUE' }; sagemaker.describeModelCard(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelCardName
— (String
)The name or Amazon Resource Name (ARN) of the model card to describe.
ModelCardVersion
— (Integer
)The version of the model card to describe. If a version is not provided, then the latest version of the model card is described.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelCardArn
— (String
)The Amazon Resource Name (ARN) of the model card.
ModelCardName
— (String
)The name of the model card.
ModelCardVersion
— (Integer
)The version of the model card.
Content
— (String
)The content of the model card.
ModelCardStatus
— (String
)The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft
: The model card is a work in progress. -
PendingReview
: The model card is pending review. -
Approved
: The model card is approved. -
Archived
: The model card is archived. No more updates should be made to the model card, but it can still be exported.
"Draft"
"PendingReview"
"Approved"
"Archived"
-
SecurityConfig
— (map
)The security configuration used to protect model card content.
KmsKeyId
— (String
)A Key Management Service key ID to use for encrypting a model card.
CreationTime
— (Date
)The date and time the model card was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)The date and time the model card was last modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
ModelCardProcessingStatus
— (String
)The processing status of model card deletion. The
ModelCardProcessingStatus
updates throughout the different deletion steps.-
DeletePending
: Model card deletion request received. -
DeleteInProgress
: Model card deletion is in progress. -
ContentDeleted
: Deleted model card content. -
ExportJobsDeleted
: Deleted all export jobs associated with the model card. -
DeleteCompleted
: Successfully deleted the model card. -
DeleteFailed
: The model card failed to delete.
"DeleteInProgress"
"DeletePending"
"ContentDeleted"
"ExportJobsDeleted"
"DeleteCompleted"
"DeleteFailed"
-
-
(AWS.Response)
—
Returns:
describeModelCardExportJob(params = {}, callback) ⇒ AWS.Request
Describes an Amazon SageMaker Model Card export job.
Service Reference:
Examples:
Calling the describeModelCardExportJob operation
var params = { ModelCardExportJobArn: 'STRING_VALUE' /* required */ }; sagemaker.describeModelCardExportJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelCardExportJobArn
— (String
)The Amazon Resource Name (ARN) of the model card export job to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelCardExportJobName
— (String
)The name of the model card export job to describe.
ModelCardExportJobArn
— (String
)The Amazon Resource Name (ARN) of the model card export job.
Status
— (String
)The completion status of the model card export job.
-
InProgress
: The model card export job is in progress. -
Completed
: The model card export job is complete. -
Failed
: The model card export job failed. To see the reason for the failure, see theFailureReason
field in the response to aDescribeModelCardExportJob
call.
"InProgress"
"Completed"
"Failed"
-
ModelCardName
— (String
)The name or Amazon Resource Name (ARN) of the model card that the model export job exports.
ModelCardVersion
— (Integer
)The version of the model card that the model export job exports.
OutputConfig
— (map
)The export output details for the model card.
S3OutputPath
— required — (String
)The Amazon S3 output path to export your model card PDF.
CreatedAt
— (Date
)The date and time that the model export job was created.
LastModifiedAt
— (Date
)The date and time that the model export job was last modified.
FailureReason
— (String
)The failure reason if the model export job fails.
ExportArtifacts
— (map
)The exported model card artifacts.
S3ExportArtifacts
— required — (String
)The Amazon S3 URI of the exported model artifacts.
-
(AWS.Response)
—
Returns:
describeModelExplainabilityJobDefinition(params = {}, callback) ⇒ AWS.Request
Returns a description of a model explainability job definition.
Service Reference:
Examples:
Calling the describeModelExplainabilityJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE' /* required */ }; sagemaker.describeModelExplainabilityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the model explainability job.
JobDefinitionName
— (String
)The name of the explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
CreationTime
— (Date
)The time at which the model explainability job was created.
ModelExplainabilityBaselineConfig
— (map
)The baseline configuration for a model explainability job.
BaseliningJobName
— (String
)The name of the baseline model explainability job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
ModelExplainabilityAppSpecification
— (map
)Configures the model explainability job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the model explainability job.
ConfigUri
— required — (String
)JSON formatted Amazon S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
ModelExplainabilityJobInput
— (map
)Inputs for the model explainability job.
EndpointInput
— (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
ModelExplainabilityJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
NetworkConfig
— (map
)Networking options for a model explainability job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
-
(AWS.Response)
—
Returns:
describeModelPackage(params = {}, callback) ⇒ AWS.Request
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API.
To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
Service Reference:
Examples:
Calling the describeModelPackage operation
var params = { ModelPackageName: 'STRING_VALUE' /* required */ }; sagemaker.describeModelPackage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageName
— (String
)The name or Amazon Resource Name (ARN) of the model package to describe.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageName
— (String
)The name of the model package being described.
ModelPackageGroupName
— (String
)If the model is a versioned model, the name of the model group that the versioned model belongs to.
ModelPackageVersion
— (Integer
)The version of the model package.
ModelPackageArn
— (String
)The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription
— (String
)A brief summary of the model package.
CreationTime
— (Date
)A timestamp specifying when the model package was created.
InferenceSpecification
— (map
)Details about inference jobs that you can run with models based on this model package.
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
SourceAlgorithmSpecification
— (map
)Details about the algorithm that was used to create the model package.
SourceAlgorithms
— required — (Array<map>
)A list of the algorithms that were used to create a model package.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the algorithm.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
AlgorithmName
— required — (String
)The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ValidationSpecification
— (map
)Configurations for one or more transform jobs that SageMaker runs to test the model package.
ValidationRole
— required — (String
)The IAM roles to be used for the validation of the model package.
ValidationProfiles
— required — (Array<map>
)An array of
ModelPackageValidationProfile
objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.ProfileName
— required — (String
)The name of the profile for the model package.
TransformJobDefinition
— required — (map
)The
TransformJobDefinition
object that describes the transform job used for the validation of the model package.MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB
— (Integer
)The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy
— (String
)A string that determines the number of records included in a single mini-batch.
SingleRecord
means only one record is used per mini-batch.MultiRecord
means a mini-batch is set to contain as many records that can fit within theMaxPayloadInMB
limit."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— required — (map
)A description of the input source and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— required — (map
)Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
TransformResources
— required — (map
)Identifies the ML compute instances for the transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. The default value is
1
, and the maximum is100
. For distributed transform jobs, specify a value greater than1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
ModelPackageStatus
— (String
)The current status of the model package.
Possible values include:"Pending"
"InProgress"
"Completed"
"Failed"
"Deleting"
ModelPackageStatusDetails
— (map
)Details about the current status of the model package.
ValidationStatuses
— required — (Array<map>
)The validation status of the model package.
Name
— required — (String
)The name of the model package for which the overall status is being reported.
Status
— required — (String
)The current status.
Possible values include:"NotStarted"
"InProgress"
"Completed"
"Failed"
FailureReason
— (String
)if the overall status is
Failed
, the reason for the failure.
ImageScanStatuses
— (Array<map>
)The status of the scan of the Docker image container for the model package.
Name
— required — (String
)The name of the model package for which the overall status is being reported.
Status
— required — (String
)The current status.
Possible values include:"NotStarted"
"InProgress"
"Completed"
"Failed"
FailureReason
— (String
)if the overall status is
Failed
, the reason for the failure.
CertifyForMarketplace
— (Boolean
)Whether the model package is certified for listing on Amazon Web Services Marketplace.
ModelApprovalStatus
— (String
)The approval status of the model package.
Possible values include:"Approved"
"Rejected"
"PendingManualApproval"
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
ModelMetrics
— (map
)Metrics for the model.
ModelQuality
— (map
)Metrics that measure the quality of a model.
Statistics
— (map
)Model quality statistics.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)Model quality constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
ModelDataQuality
— (map
)Metrics that measure the quality of the input data for a model.
Statistics
— (map
)Data quality statistics for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)Data quality constraints for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Bias
— (map
)Metrics that measure bias in a model.
Report
— (map
)The bias report for a model
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
PreTrainingReport
— (map
)The pre-training bias report for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
PostTrainingReport
— (map
)The post-training bias report for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Explainability
— (map
)Metrics that help explain a model.
Report
— (map
)The explainability report for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
LastModifiedTime
— (Date
)The last time that the model package was modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
ApprovalDescription
— (String
)A description provided for the model approval.
Domain
— (String
)The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.
Task
— (String
)The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.
SamplePayloadUrl
— (String
)The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).
CustomerMetadataProperties
— (map<String>
)The metadata properties associated with the model package versions.
DriftCheckBaselines
— (map
)Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
Bias
— (map
)Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
ConfigFile
— (map
)The bias config file for a model.
ContentType
— (String
)The type of content stored in the file source.
ContentDigest
— (String
)The digest of the file source.
S3Uri
— required — (String
)The Amazon S3 URI for the file source.
PreTrainingConstraints
— (map
)The pre-training constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
PostTrainingConstraints
— (map
)The post-training constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Explainability
— (map
)Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Constraints
— (map
)The drift check explainability constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
ConfigFile
— (map
)The explainability config file for the model.
ContentType
— (String
)The type of content stored in the file source.
ContentDigest
— (String
)The digest of the file source.
S3Uri
— required — (String
)The Amazon S3 URI for the file source.
ModelQuality
— (map
)Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Statistics
— (map
)The drift check model quality statistics.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)The drift check model quality constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
ModelDataQuality
— (map
)Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
Statistics
— (map
)The drift check model data quality statistics.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)The drift check model data quality constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
AdditionalInferenceSpecifications
— (Array<map>
)An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
Name
— required — (String
)A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description
— (String
)A description of the additional Inference specification
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
SkipModelValidation
— (String
)Indicates if you want to skip model validation.
Possible values include:"All"
"None"
SourceUri
— (String
)The URI of the source for the model package.
SecurityConfig
— (map
)The KMS Key ID (
KMSKeyId
) used for encryption of model package information.KmsKeyId
— required — (String
)The KMS Key ID (
KMSKeyId
) used for encryption of model package information.
ModelCard
— (map
)The model card associated with the model package. Since
ModelPackageModelCard
is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema ofModelCard
. TheModelPackageModelCard
schema does not includemodel_package_details
, andmodel_overview
is composed of themodel_creator
andmodel_artifact
properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.ModelCardContent
— (String
)The content of the model card. The content must follow the schema described in Model Package Model Card Schema.
ModelCardStatus
— (String
)The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft
: The model card is a work in progress. -
PendingReview
: The model card is pending review. -
Approved
: The model card is approved. -
Archived
: The model card is archived. No more updates can be made to the model card content. If you try to update the model card content, you will receive the messageModel Card is in Archived state
.
"Draft"
"PendingReview"
"Approved"
"Archived"
-
-
(AWS.Response)
—
Returns:
describeModelPackageGroup(params = {}, callback) ⇒ AWS.Request
Gets a description for the specified model group.
Service Reference:
Examples:
Calling the describeModelPackageGroup operation
var params = { ModelPackageGroupName: 'STRING_VALUE' /* required */ }; sagemaker.describeModelPackageGroup(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageGroupName
— (String
)The name of the model group to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageGroupName
— (String
)The name of the model group.
ModelPackageGroupArn
— (String
)The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription
— (String
)A description of the model group.
CreationTime
— (Date
)The time that the model group was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
ModelPackageGroupStatus
— (String
)The status of the model group.
Possible values include:"Pending"
"InProgress"
"Completed"
"Failed"
"Deleting"
"DeleteFailed"
-
(AWS.Response)
—
Returns:
describeModelQualityJobDefinition(params = {}, callback) ⇒ AWS.Request
Returns a description of a model quality job definition.
Service Reference:
Examples:
Calling the describeModelQualityJobDefinition operation
var params = { JobDefinitionName: 'STRING_VALUE' /* required */ }; sagemaker.describeModelQualityJobDefinition(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobDefinitionName
— (String
)The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionArn
— (String
)The Amazon Resource Name (ARN) of the model quality job.
JobDefinitionName
— (String
)The name of the quality job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
CreationTime
— (Date
)The time at which the model quality job was created.
ModelQualityBaselineConfig
— (map
)The baseline configuration for a model quality job.
BaseliningJobName
— (String
)The name of the job that performs baselining for the monitoring job.
ConstraintsResource
— (map
)The constraints resource for a monitoring job.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
ModelQualityAppSpecification
— (map
)Configures the model quality job to run a specified Docker container image.
ImageUri
— required — (String
)The address of the container image that the monitoring job runs.
ContainerEntrypoint
— (Array<String>
)Specifies the entrypoint for a container that the monitoring job runs.
ContainerArguments
— (Array<String>
)An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
ProblemType
— (String
)The machine learning problem type of the model that the monitoring job monitors.
Possible values include:"BinaryClassification"
"MulticlassClassification"
"Regression"
Environment
— (map<String>
)Sets the environment variables in the container that the monitoring job runs.
ModelQualityJobInput
— (map
)Inputs for the model quality job.
EndpointInput
— (map
)Input object for the endpoint
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
GroundTruthS3Input
— required — (map
)The ground truth label provided for the model.
S3Uri
— (String
)The address of the Amazon S3 location of the ground truth labels.
ModelQualityJobOutputConfig
— (map
)The output configuration for monitoring jobs.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
JobResources
— (map
)Identifies the resources to deploy for a monitoring job.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
NetworkConfig
— (map
)Networking options for a model quality job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between the instances used for the monitoring jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
— (map
)A time limit for how long the monitoring job is allowed to run before stopping.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
-
(AWS.Response)
—
Returns:
describeMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Describes the schedule for a monitoring job.
Service Reference:
Examples:
Calling the describeMonitoringSchedule operation
var params = { MonitoringScheduleName: 'STRING_VALUE' /* required */ }; sagemaker.describeMonitoringSchedule(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)Name of a previously created monitoring schedule.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:MonitoringScheduleArn
— (String
)The Amazon Resource Name (ARN) of the monitoring schedule.
MonitoringScheduleName
— (String
)Name of the monitoring schedule.
MonitoringScheduleStatus
— (String
)The status of an monitoring job.
Possible values include:"Pending"
"Failed"
"Scheduled"
"Stopped"
MonitoringType
— (String
)The type of the monitoring job that this schedule runs. This is one of the following values.
-
DATA_QUALITY
- The schedule is for a data quality monitoring job. -
MODEL_QUALITY
- The schedule is for a model quality monitoring job. -
MODEL_BIAS
- The schedule is for a bias monitoring job. -
MODEL_EXPLAINABILITY
- The schedule is for an explainability monitoring job.
"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
-
FailureReason
— (String
)A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.
CreationTime
— (Date
)The time at which the monitoring job was created.
LastModifiedTime
— (Date
)The time at which the monitoring job was last modified.
MonitoringScheduleConfig
— (map
)The configuration object that specifies the monitoring schedule and defines the monitoring job.
ScheduleConfig
— (map
)Configures the monitoring schedule.
ScheduleExpression
— required — (String
)A cron expression that describes details about the monitoring schedule.
The supported cron expressions are:
-
If you want to set the job to start every hour, use the following:
Hourly: cron(0 * ? * * *)
-
If you want to start the job daily:
cron(0 [00-23] ? * * *)
-
If you want to run the job one time, immediately, use the following keyword:
NOW
For example, the following are valid cron expressions:
-
Daily at noon UTC:
cron(0 12 ? * * *)
-
Daily at midnight UTC:
cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
-
Every 12 hours, starting at 5pm UTC:
cron(0 17/12 ? * * *)
-
Every two hours starting at midnight:
cron(0 0/2 ? * * *)
Note:- Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
- We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
You can also specify the keyword
NOW
to run the monitoring job immediately, one time, without recurring.-
DataAnalysisStartTime
— (String
)Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the
ScheduleExpression
parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify:"-PT5H"
.The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the
DataAnalysisEndTime
parameter.If you set
ScheduleExpression
toNOW
, this parameter is required.DataAnalysisEndTime
— (String
)Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the
ScheduleExpression
parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify:"-PT1H"
.The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the
DataAnalysisStartTime
parameter.If you set
ScheduleExpression
toNOW
, this parameter is required.
MonitoringJobDefinition
— (map
)Defines the monitoring job.
BaselineConfig
— (map
)Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName
— (String
)The name of the job that performs baselining for the monitoring job.
ConstraintsResource
— (map
)The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
StatisticsResource
— (map
)The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri
— (String
)The Amazon S3 URI for the statistics resource.
MonitoringInputs
— required — (Array<map>
)The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
EndpointInput
— (map
)The endpoint for a monitoring job.
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
MonitoringOutputConfig
— required — (map
)The array of outputs from the monitoring job to be uploaded to Amazon S3.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources
— required — (map
)Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification
— required — (map
)Configures the monitoring job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the monitoring job.
ContainerEntrypoint
— (Array<String>
)Specifies the entrypoint for a container used to run the monitoring job.
ContainerArguments
— (Array<String>
)An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition
— (map
)Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
NetworkConfig
— (map
)Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job definition to schedule.
MonitoringType
— (String
)The type of the monitoring job definition to schedule.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
EndpointName
— (String
)The name of the endpoint for the monitoring job.
LastMonitoringExecutionSummary
— (map
)Describes metadata on the last execution to run, if there was one.
MonitoringScheduleName
— required — (String
)The name of the monitoring schedule.
ScheduledTime
— required — (Date
)The time the monitoring job was scheduled.
CreationTime
— required — (Date
)The time at which the monitoring job was created.
LastModifiedTime
— required — (Date
)A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus
— required — (String
)The status of the monitoring job.
Possible values include:"Pending"
"Completed"
"CompletedWithViolations"
"InProgress"
"Failed"
"Stopping"
"Stopped"
ProcessingJobArn
— (String
)The Amazon Resource Name (ARN) of the monitoring job.
EndpointName
— (String
)The name of the endpoint used to run the monitoring job.
FailureReason
— (String
)Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job.
MonitoringType
— (String
)The type of the monitoring job.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
-
(AWS.Response)
—
Returns:
describeNotebookInstance(params = {}, callback) ⇒ AWS.Request
Returns information about a notebook instance.
Service Reference:
Examples:
Calling the describeNotebookInstance operation
var params = { NotebookInstanceName: 'STRING_VALUE' /* required */ }; sagemaker.describeNotebookInstance(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceName
— (String
)The name of the notebook instance that you want information about.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NotebookInstanceArn
— (String
)The Amazon Resource Name (ARN) of the notebook instance.
NotebookInstanceName
— (String
)The name of the SageMaker notebook instance.
NotebookInstanceStatus
— (String
)The status of the notebook instance.
Possible values include:"Pending"
"InService"
"Stopping"
"Stopped"
"Failed"
"Deleting"
"Updating"
FailureReason
— (String
)If status is
Failed
, the reason it failed.Url
— (String
)The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.
InstanceType
— (String
)The type of ML compute instance running on the notebook instance.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
SubnetId
— (String
)The ID of the VPC subnet.
SecurityGroups
— (Array<String>
)The IDs of the VPC security groups.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role associated with the instance.
KmsKeyId
— (String
)The Amazon Web Services KMS key ID SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
NetworkInterfaceId
— (String
)The network interface IDs that SageMaker created at the time of creating the instance.
LastModifiedTime
— (Date
)A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.
CreationTime
— (Date
)A timestamp. Use this parameter to return the time when the notebook instance was created
NotebookInstanceLifecycleConfigName
— (String
)Returns the name of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance
DirectInternetAccess
— (String
)Describes whether SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to SageMaker training and endpoint services.
For more information, see Notebook Instances Are Internet-Enabled by Default.
Possible values include:"Enabled"
"Disabled"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the notebook instance.
AcceleratorTypes
— (Array<String>
)A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
DefaultCodeRepository
— (String
)The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
AdditionalCodeRepositories
— (Array<String>
)An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
RootAccess
— (String
)Whether root access is enabled or disabled for users of the notebook instance.
Note: Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.Possible values include:"Enabled"
"Disabled"
PlatformIdentifier
— (String
)The platform identifier of the notebook instance runtime environment.
InstanceMetadataServiceConfiguration
— (map
)Information on the IMDS configuration of the notebook instance
MinimumInstanceMetadataServiceVersion
— required — (String
)Indicates the minimum IMDS version that the notebook instance supports. When passed as part of
CreateNotebookInstance
, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part ofUpdateNotebookInstance
, there is no default.
-
(AWS.Response)
—
Returns:
Waiter Resource States:
describeNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Returns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
Service Reference:
Examples:
Calling the describeNotebookInstanceLifecycleConfig operation
var params = { NotebookInstanceLifecycleConfigName: 'STRING_VALUE' /* required */ }; sagemaker.describeNotebookInstanceLifecycleConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceLifecycleConfigName
— (String
)The name of the lifecycle configuration to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NotebookInstanceLifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the lifecycle configuration.
NotebookInstanceLifecycleConfigName
— (String
)The name of the lifecycle configuration.
OnCreate
— (Array<map>
)The shell script that runs only once, when you create a notebook instance.
Content
— (String
)A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
OnStart
— (Array<map>
)The shell script that runs every time you start a notebook instance, including when you create the notebook instance.
Content
— (String
)A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
LastModifiedTime
— (Date
)A timestamp that tells when the lifecycle configuration was last modified.
CreationTime
— (Date
)A timestamp that tells when the lifecycle configuration was created.
-
(AWS.Response)
—
Returns:
describeOptimizationJob(params = {}, callback) ⇒ AWS.Request
Provides the properties of the specified optimization job.
Service Reference:
Examples:
Calling the describeOptimizationJob operation
var params = { OptimizationJobName: 'STRING_VALUE' /* required */ }; sagemaker.describeOptimizationJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
OptimizationJobName
— (String
)The name that you assigned to the optimization job.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:OptimizationJobArn
— (String
)The Amazon Resource Name (ARN) of the optimization job.
OptimizationJobStatus
— (String
)The current status of the optimization job.
Possible values include:"INPROGRESS"
"COMPLETED"
"FAILED"
"STARTING"
"STOPPING"
"STOPPED"
OptimizationStartTime
— (Date
)The time when the optimization job started.
OptimizationEndTime
— (Date
)The time when the optimization job finished processing.
CreationTime
— (Date
)The time when you created the optimization job.
LastModifiedTime
— (Date
)The time when the optimization job was last updated.
FailureReason
— (String
)If the optimization job status is
FAILED
, the reason for the failure.OptimizationJobName
— (String
)The name that you assigned to the optimization job.
ModelSource
— (map
)The location of the source model to optimize with an optimization job.
S3
— (map
)The Amazon S3 location of a source model to optimize with an optimization job.
S3Uri
— (String
)An Amazon S3 URI that locates a source model to optimize with an optimization job.
ModelAccessConfig
— (map
)The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).
AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
OptimizationEnvironment
— (map<String>
)The environment variables to set in the model container.
DeploymentInstanceType
— (String
)The type of instance that hosts the optimized model that you create with the optimization job.
Possible values include:"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
OptimizationConfigs
— (Array<map>
)Settings for each of the optimization techniques that the job applies.
ModelQuantizationConfig
— (map
)Settings for the model quantization technique that's applied by a model optimization job.
Image
— (String
)The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
OverrideEnvironment
— (map<String>
)Environment variables that override the default ones in the model container.
ModelCompilationConfig
— (map
)Settings for the model compilation technique that's applied by a model optimization job.
Image
— (String
)The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
OverrideEnvironment
— (map<String>
)Environment variables that override the default ones in the model container.
OutputConfig
— (map
)Details for where to store the optimized model that you create with the optimization job.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a key in Amazon Web Services KMS. SageMaker uses they key to encrypt the artifacts of the optimized model when SageMaker uploads the model to Amazon S3.
S3OutputLocation
— required — (String
)The Amazon S3 URI for where to store the optimized model that you create with an optimization job.
OptimizationOutput
— (map
)Output values produced by an optimization job.
RecommendedInferenceImage
— (String
)The image that SageMaker recommends that you use to host the optimized model that you created with an optimization job.
RoleArn
— (String
)The ARN of the IAM role that you assigned to the optimization job.
StoppingCondition
— (map
)Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.
To stop a training job, SageMaker sends the algorithm the
SIGTERM
signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with
CreateModel
.Note: The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
VpcConfig
— (map
)A VPC in Amazon VPC that your optimized model has access to.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your optimized model.
-
(AWS.Response)
—
Returns:
describePipeline(params = {}, callback) ⇒ AWS.Request
Describes the details of a pipeline.
Service Reference:
Examples:
Calling the describePipeline operation
var params = { PipelineName: 'STRING_VALUE' /* required */ }; sagemaker.describePipeline(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineName
— (String
)The name or Amazon Resource Name (ARN) of the pipeline to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineArn
— (String
)The Amazon Resource Name (ARN) of the pipeline.
PipelineName
— (String
)The name of the pipeline.
PipelineDisplayName
— (String
)The display name of the pipeline.
PipelineDefinition
— (String
)The JSON pipeline definition.
PipelineDescription
— (String
)The description of the pipeline.
RoleArn
— (String
)The Amazon Resource Name (ARN) that the pipeline uses to execute.
PipelineStatus
— (String
)The status of the pipeline execution.
Possible values include:"Active"
"Deleting"
CreationTime
— (Date
)The time when the pipeline was created.
LastModifiedTime
— (Date
)The time when the pipeline was last modified.
LastRunTime
— (Date
)The time when the pipeline was last run.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
ParallelismConfiguration
— (map
)Lists the parallelism configuration applied to the pipeline.
MaxParallelExecutionSteps
— required — (Integer
)The max number of steps that can be executed in parallel.
-
(AWS.Response)
—
Returns:
describePipelineDefinitionForExecution(params = {}, callback) ⇒ AWS.Request
Describes the details of an execution's pipeline definition.
Service Reference:
Examples:
Calling the describePipelineDefinitionForExecution operation
var params = { PipelineExecutionArn: 'STRING_VALUE' /* required */ }; sagemaker.describePipelineDefinitionForExecution(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineDefinition
— (String
)The JSON pipeline definition.
CreationTime
— (Date
)The time when the pipeline was created.
-
(AWS.Response)
—
Returns:
describePipelineExecution(params = {}, callback) ⇒ AWS.Request
Describes the details of a pipeline execution.
Service Reference:
Examples:
Calling the describePipelineExecution operation
var params = { PipelineExecutionArn: 'STRING_VALUE' /* required */ }; sagemaker.describePipelineExecution(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineArn
— (String
)The Amazon Resource Name (ARN) of the pipeline.
PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionDisplayName
— (String
)The display name of the pipeline execution.
PipelineExecutionStatus
— (String
)The status of the pipeline execution.
Possible values include:"Executing"
"Stopping"
"Stopped"
"Failed"
"Succeeded"
PipelineExecutionDescription
— (String
)The description of the pipeline execution.
PipelineExperimentConfig
— (map
)Specifies the names of the experiment and trial created by a pipeline.
ExperimentName
— (String
)The name of the experiment.
TrialName
— (String
)The name of the trial.
FailureReason
— (String
)If the execution failed, a message describing why.
CreationTime
— (Date
)The time when the pipeline execution was created.
LastModifiedTime
— (Date
)The time when the pipeline execution was modified last.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
ParallelismConfiguration
— (map
)The parallelism configuration applied to the pipeline.
MaxParallelExecutionSteps
— required — (Integer
)The max number of steps that can be executed in parallel.
SelectiveExecutionConfig
— (map
)The selective execution configuration applied to the pipeline run.
SourcePipelineExecutionArn
— (String
)The ARN from a reference execution of the current pipeline. Used to copy input collaterals needed for the selected steps to run. The execution status of the pipeline can be either
Failed
orSuccess
.This field is required if the steps you specify for
SelectedSteps
depend on output collaterals from any non-specified pipeline steps. For more information, see Selective Execution for Pipeline Steps.SelectedSteps
— required — (Array<map>
)A list of pipeline steps to run. All step(s) in all path(s) between two selected steps should be included.
StepName
— required — (String
)The name of the pipeline step.
-
(AWS.Response)
—
Returns:
describeProcessingJob(params = {}, callback) ⇒ AWS.Request
Returns a description of a processing job.
Service Reference:
Examples:
Calling the describeProcessingJob operation
var params = { ProcessingJobName: 'STRING_VALUE' /* required */ }; sagemaker.describeProcessingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ProcessingJobName
— (String
)The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ProcessingInputs
— (Array<map>
)The inputs for a processing job.
InputName
— required — (String
)The name for the processing job input.
AppManaged
— (Boolean
)When
True
, input operations such as data download are managed natively by the processing job application. WhenFalse
(default), input operations are managed by Amazon SageMaker.S3Input
— (map
)Configuration for downloading input data from Amazon S3 into the processing container.
S3Uri
— required — (String
)The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
LocalPath
— (String
)The local path in your container where you want Amazon SageMaker to write input data to.
LocalPath
is an absolute path to the input data and must begin with/opt/ml/processing/
.LocalPath
is a required parameter whenAppManaged
isFalse
(default).S3DataType
— required — (String
)Whether you use an
Possible values include:S3Prefix
or aManifestFile
for the data type. If you chooseS3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you chooseManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job."ManifestFile"
"S3Prefix"
S3InputMode
— (String
)Whether to use
Possible values include:File
orPipe
input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. InPipe
mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume."Pipe"
"File"
S3DataDistributionType
— (String
)Whether to distribute the data from Amazon S3 to all processing instances with
Possible values include:FullyReplicated
, or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance."FullyReplicated"
"ShardedByS3Key"
S3CompressionType
— (String
)Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container.
Possible values include:Gzip
can only be used whenPipe
mode is specified as theS3InputMode
. InPipe
mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume."None"
"Gzip"
DatasetDefinition
— (map
)Configuration for a Dataset Definition input.
AthenaDatasetDefinition
— (map
)Configuration for Athena Dataset Definition input.
Catalog
— required — (String
)The name of the data catalog used in Athena query execution.
Database
— required — (String
)The name of the database used in the Athena query execution.
QueryString
— required — (String
)The SQL query statements, to be executed.
WorkGroup
— (String
)The name of the workgroup in which the Athena query is being started.
OutputS3Uri
— required — (String
)The location in Amazon S3 where Athena query results are stored.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat
— required — (String
)The data storage format for Athena query results.
Possible values include:"PARQUET"
"ORC"
"AVRO"
"JSON"
"TEXTFILE"
OutputCompression
— (String
)The compression used for Athena query results.
Possible values include:"GZIP"
"SNAPPY"
"ZLIB"
RedshiftDatasetDefinition
— (map
)Configuration for Redshift Dataset Definition input.
ClusterId
— required — (String
)The Redshift cluster Identifier.
Database
— required — (String
)The name of the Redshift database used in Redshift query execution.
DbUser
— required — (String
)The database user name used in Redshift query execution.
QueryString
— required — (String
)The SQL query statements to be executed.
ClusterRoleArn
— required — (String
)The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri
— required — (String
)The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat
— required — (String
)The data storage format for Redshift query results.
Possible values include:"PARQUET"
"CSV"
OutputCompression
— (String
)The compression used for Redshift query results.
Possible values include:"None"
"GZIP"
"BZIP2"
"ZSTD"
"SNAPPY"
LocalPath
— (String
)The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job.
LocalPath
is an absolute path to the input data. This is a required parameter whenAppManaged
isFalse
(default).DataDistributionType
— (String
)Whether the generated dataset is
Possible values include:FullyReplicated
orShardedByS3Key
(default)."FullyReplicated"
"ShardedByS3Key"
InputMode
— (String
)Whether to use
Possible values include:File
orPipe
input mode. InFile
(default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. InPipe
mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume."Pipe"
"File"
ProcessingOutputConfig
— (map
)Output configuration for the processing job.
Outputs
— required — (Array<map>
)An array of outputs configuring the data to upload from the processing container.
OutputName
— required — (String
)The name for the processing job output.
S3Output
— (map
)Configuration for processing job outputs in Amazon S3.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath
— (String
)The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3.
LocalPath
is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.S3UploadMode
— required — (String
)Whether to upload the results of the processing job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
FeatureStoreOutput
— (map
)Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when
AppManaged
is specified.FeatureGroupName
— required — (String
)The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
AppManaged
— (Boolean
)When
True
, output operations such as data upload are managed natively by the processing job application. WhenFalse
(default), output operations are managed by Amazon SageMaker.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output.
KmsKeyId
can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. TheKmsKeyId
is applied to all outputs.
ProcessingJobName
— (String
)The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ProcessingResources
— (map
)Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig
— required — (map
)The configuration for the resources in a cluster used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request aVolumeSizeInGB
greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.
StoppingCondition
— (map
)The time limit for how long the processing job is allowed to run.
MaxRuntimeInSeconds
— required — (Integer
)Specifies the maximum runtime in seconds.
AppSpecification
— (map
)Configures the processing job to run a specified container image.
ImageUri
— required — (String
)The container image to be run by the processing job.
ContainerEntrypoint
— (Array<String>
)The entrypoint for a container used to run a processing job.
ContainerArguments
— (Array<String>
)The arguments for a container used to run a processing job.
Environment
— (map<String>
)The environment variables set in the Docker container.
NetworkConfig
— (map
)Networking options for a processing job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
ExperimentConfig
— (map
)The configuration information used to create an experiment.
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
ProcessingJobArn
— (String
)The Amazon Resource Name (ARN) of the processing job.
ProcessingJobStatus
— (String
)Provides the status of a processing job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
ExitMessage
— (String
)An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
FailureReason
— (String
)A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ProcessingEndTime
— (Date
)The time at which the processing job completed.
ProcessingStartTime
— (Date
)The time at which the processing job started.
LastModifiedTime
— (Date
)The time at which the processing job was last modified.
CreationTime
— (Date
)The time at which the processing job was created.
MonitoringScheduleArn
— (String
)The ARN of a monitoring schedule for an endpoint associated with this processing job.
AutoMLJobArn
— (String
)The ARN of an AutoML job associated with this processing job.
TrainingJobArn
— (String
)The ARN of a training job associated with this processing job.
-
(AWS.Response)
—
Returns:
Waiter Resource States:
describeProject(params = {}, callback) ⇒ AWS.Request
Describes the details of a project.
Service Reference:
Examples:
Calling the describeProject operation
var params = { ProjectName: 'STRING_VALUE' /* required */ }; sagemaker.describeProject(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ProjectName
— (String
)The name of the project to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ProjectArn
— (String
)The Amazon Resource Name (ARN) of the project.
ProjectName
— (String
)The name of the project.
ProjectId
— (String
)The ID of the project.
ProjectDescription
— (String
)The description of the project.
ServiceCatalogProvisioningDetails
— (map
)Information used to provision a service catalog product. For information, see What is Amazon Web Services Service Catalog.
ProductId
— required — (String
)The ID of the product to provision.
ProvisioningArtifactId
— (String
)The ID of the provisioning artifact.
PathId
— (String
)The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
ProvisioningParameters
— (Array<map>
)A list of key value pairs that you specify when you provision a product.
Key
— (String
)The key that identifies a provisioning parameter.
Value
— (String
)The value of the provisioning parameter.
ServiceCatalogProvisionedProductDetails
— (map
)Information about a provisioned service catalog product.
ProvisionedProductId
— (String
)The ID of the provisioned product.
ProvisionedProductStatusMessage
— (String
)The current status of the product.
-
AVAILABLE
- Stable state, ready to perform any operation. The most recent operation succeeded and completed. -
UNDER_CHANGE
- Transitive state. Operations performed might not have valid results. Wait for an AVAILABLE status before performing operations. -
TAINTED
- Stable state, ready to perform any operation. The stack has completed the requested operation but is not exactly what was requested. For example, a request to update to a new version failed and the stack rolled back to the current version. -
ERROR
- An unexpected error occurred. The provisioned product exists but the stack is not running. For example, CloudFormation received a parameter value that was not valid and could not launch the stack. -
PLAN_IN_PROGRESS
- Transitive state. The plan operations were performed to provision a new product, but resources have not yet been created. After reviewing the list of resources to be created, execute the plan. Wait for an AVAILABLE status before performing operations.
-
ProjectStatus
— (String
)The status of the project.
Possible values include:"Pending"
"CreateInProgress"
"CreateCompleted"
"CreateFailed"
"DeleteInProgress"
"DeleteFailed"
"DeleteCompleted"
"UpdateInProgress"
"UpdateCompleted"
"UpdateFailed"
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
CreationTime
— (Date
)The time when the project was created.
LastModifiedTime
— (Date
)The timestamp when project was last modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
-
(AWS.Response)
—
Returns:
describeSpace(params = {}, callback) ⇒ AWS.Request
Describes the space.
Service Reference:
Examples:
Calling the describeSpace operation
var params = { DomainId: 'STRING_VALUE', /* required */ SpaceName: 'STRING_VALUE' /* required */ }; sagemaker.describeSpace(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The ID of the associated domain.
SpaceName
— (String
)The name of the space.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DomainId
— (String
)The ID of the associated domain.
SpaceArn
— (String
)The space's Amazon Resource Name (ARN).
SpaceName
— (String
)The name of the space.
HomeEfsFileSystemUid
— (String
)The ID of the space's profile in the Amazon EFS volume.
Status
— (String
)The status.
Possible values include:"Deleting"
"Failed"
"InService"
"Pending"
"Updating"
"Update_Failed"
"Delete_Failed"
LastModifiedTime
— (Date
)The last modified time.
CreationTime
— (Date
)The creation time.
FailureReason
— (String
)The failure reason.
SpaceSettings
— (map
)A collection of space settings.
JupyterServerAppSettings
— (map
)The JupyterServer app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The KernelGateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
CodeEditorAppSettings
— (map
)The Code Editor application settings.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of CodeEditor applications in a space.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of JupyterLab applications in a space.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
AppType
— (String
)The type of app created within the space.
Possible values include:"JupyterServer"
"KernelGateway"
"DetailedProfiler"
"TensorBoard"
"CodeEditor"
"JupyterLab"
"RStudioServerPro"
"RSessionGateway"
"Canvas"
SpaceStorageSettings
— (map
)The storage settings for a space.
EbsStorageSettings
— (map
)A collection of EBS storage settings for a space.
EbsVolumeSizeInGb
— required — (Integer
)The size of an EBS storage volume for a space.
CustomFileSystems
— (Array<map>
)A file system, created by you, that you assign to a space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystem
— (map
)A custom file system in Amazon EFS.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
OwnershipSettings
— (map
)The collection of ownership settings for a space.
OwnerUserProfileName
— required — (String
)The user profile who is the owner of the space.
SpaceSharingSettings
— (map
)The collection of space sharing settings for a space.
SharingType
— required — (String
)Specifies the sharing type of the space.
Possible values include:"Private"
"Shared"
SpaceDisplayName
— (String
)The name of the space that appears in the Amazon SageMaker Studio UI.
Url
— (String
)Returns the URL of the space. If the space is created with Amazon Web Services IAM Identity Center (Successor to Amazon Web Services Single Sign-On) authentication, users can navigate to the URL after appending the respective redirect parameter for the application type to be federated through Amazon Web Services IAM Identity Center.
The following application types are supported:
-
Studio Classic:
&redirect=JupyterServer
-
JupyterLab:
&redirect=JupyterLab
-
Code Editor, based on Code-OSS, Visual Studio Code - Open Source:
&redirect=CodeEditor
-
-
(AWS.Response)
—
Returns:
describeStudioLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Describes the Amazon SageMaker Studio Lifecycle Configuration.
Service Reference:
Examples:
Calling the describeStudioLifecycleConfig operation
var params = { StudioLifecycleConfigName: 'STRING_VALUE' /* required */ }; sagemaker.describeStudioLifecycleConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
StudioLifecycleConfigName
— (String
)The name of the Amazon SageMaker Studio Lifecycle Configuration to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:StudioLifecycleConfigArn
— (String
)The ARN of the Lifecycle Configuration to describe.
StudioLifecycleConfigName
— (String
)The name of the Amazon SageMaker Studio Lifecycle Configuration that is described.
CreationTime
— (Date
)The creation time of the Amazon SageMaker Studio Lifecycle Configuration.
LastModifiedTime
— (Date
)This value is equivalent to CreationTime because Amazon SageMaker Studio Lifecycle Configurations are immutable.
StudioLifecycleConfigContent
— (String
)The content of your Amazon SageMaker Studio Lifecycle Configuration script.
StudioLifecycleConfigAppType
— (String
)The App type that the Lifecycle Configuration is attached to.
Possible values include:"JupyterServer"
"KernelGateway"
"CodeEditor"
"JupyterLab"
-
(AWS.Response)
—
Returns:
describeSubscribedWorkteam(params = {}, callback) ⇒ AWS.Request
Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.
Service Reference:
Examples:
Calling the describeSubscribedWorkteam operation
var params = { WorkteamArn: 'STRING_VALUE' /* required */ }; sagemaker.describeSubscribedWorkteam(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
WorkteamArn
— (String
)The Amazon Resource Name (ARN) of the subscribed work team to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:SubscribedWorkteam
— (map
)A
Workteam
instance that contains information about the work team.WorkteamArn
— required — (String
)The Amazon Resource Name (ARN) of the vendor that you have subscribed.
MarketplaceTitle
— (String
)The title of the service provided by the vendor in the Amazon Marketplace.
SellerName
— (String
)The name of the vendor in the Amazon Marketplace.
MarketplaceDescription
— (String
)The description of the vendor from the Amazon Marketplace.
ListingId
— (String
)Marketplace product listing ID.
-
(AWS.Response)
—
Returns:
describeTrainingJob(params = {}, callback) ⇒ AWS.Request
Returns information about a training job.
Some of the attributes below only appear if the training job successfully starts. If the training job fails,
TrainingJobStatus
isFailed
and, depending on theFailureReason
, attributes likeTrainingStartTime
,TrainingTimeInSeconds
,TrainingEndTime
, andBillableTimeInSeconds
may not be present in the response.Service Reference:
Examples:
Calling the describeTrainingJob operation
var params = { TrainingJobName: 'STRING_VALUE' /* required */ }; sagemaker.describeTrainingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrainingJobName
— (String
)The name of the training job.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrainingJobName
— (String
)Name of the model training job.
TrainingJobArn
— (String
)The Amazon Resource Name (ARN) of the training job.
TuningJobArn
— (String
)The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the transform or training job.
AutoMLJobArn
— (String
)The Amazon Resource Name (ARN) of an AutoML job.
ModelArtifacts
— (map
)Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts
— required — (String
)The path of the S3 object that contains the model artifacts. For example,
s3://bucket-name/keynameprefix/model.tar.gz
.
TrainingJobStatus
— (String
)The status of the training job.
SageMaker provides the following training job statuses:
-
InProgress
- The training is in progress. -
Completed
- The training job has completed. -
Failed
- The training job has failed. To see the reason for the failure, see theFailureReason
field in the response to aDescribeTrainingJobResponse
call. -
Stopping
- The training job is stopping. -
Stopped
- The training job has stopped.
For more detailed information, see
Possible values include:SecondaryStatus
."InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
-
SecondaryStatus
— (String
)Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see
StatusMessage
under SecondaryStatusTransition.SageMaker provides primary statuses and secondary statuses that apply to each of them:
- InProgress
-
-
Starting
- Starting the training job. -
Downloading
- An optional stage for algorithms that supportFile
training input mode. It indicates that data is being downloaded to the ML storage volumes. -
Training
- Training is in progress. -
Interrupted
- The job stopped because the managed spot training instances were interrupted. -
Uploading
- Training is complete and the model artifacts are being uploaded to the S3 location.
-
- Completed
-
-
Completed
- The training job has completed.
-
- Failed
-
-
Failed
- The training job has failed. The reason for the failure is returned in theFailureReason
field ofDescribeTrainingJobResponse
.
-
- Stopped
-
-
MaxRuntimeExceeded
- The job stopped because it exceeded the maximum allowed runtime. -
MaxWaitTimeExceeded
- The job stopped because it exceeded the maximum allowed wait time. -
Stopped
- The training job has stopped.
-
- Stopping
-
-
Stopping
- Stopping the training job.
-
Valid values for
SecondaryStatus
are subject to change.We no longer support the following secondary statuses:
-
LaunchingMLInstances
-
PreparingTraining
-
DownloadingTrainingImage
"Starting"
"LaunchingMLInstances"
"PreparingTrainingStack"
"Downloading"
"DownloadingTrainingImage"
"Training"
"Uploading"
"Stopping"
"Stopped"
"MaxRuntimeExceeded"
"Completed"
"Failed"
"Interrupted"
"MaxWaitTimeExceeded"
"Updating"
"Restarting"
"Pending"
FailureReason
— (String
)If the training job failed, the reason it failed.
HyperParameters
— (map<String>
)Algorithm-specific parameters.
AlgorithmSpecification
— (map
)Information about the algorithm used for training, and algorithm metadata.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. For more information, see the note in theAlgorithmName
parameter description.AlgorithmName
— (String
)The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. Note that theAlgorithmName
parameter is mutually exclusive with theTrainingImage
parameter. If you specify a value for theAlgorithmName
parameter, you can't specify a value forTrainingImage
, and vice versa. If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise anull
error.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
MetricDefinitions
— (Array<map>
)A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
EnableSageMakerMetricsTimeSeries
— (Boolean
)To generate and save time-series metrics during training, set to
true
. The default isfalse
and time-series metrics aren't generated except in the following cases:-
You use one of the SageMaker built-in algorithms
-
You use one of the following Prebuilt SageMaker Docker Images:
-
Tensorflow (version >= 1.15)
-
MXNet (version >= 1.6)
-
PyTorch (version >= 1.3)
-
-
You specify at least one MetricDefinition
-
ContainerEntrypoint
— (Array<String>
)The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
ContainerArguments
— (Array<String>
)The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
TrainingImageConfig
— (map
)The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode
— required — (String
)The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to
Possible values include:Vpc
."Platform"
"Vpc"
TrainingRepositoryAuthConfig
— (map
)An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn
— (String
)The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig
— (Array<map>
)An array of
Channel
objects that describes each data input channel.ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
OutputDataConfig
— (map
)The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
WarmPoolStatus
— (map
)The status of the warm pool associated with the training job.
Status
— required — (String
)The status of the warm pool.
-
InUse
: The warm pool is in use for the training job. -
Available
: The warm pool is available to reuse for a matching training job. -
Reused
: The warm pool moved to a matching training job for reuse. -
Terminated
: The warm pool is no longer available. Warm pools are unavailable if they are terminated by a user, terminated for a patch update, or terminated for exceeding the specifiedKeepAlivePeriodInSeconds
.
"Available"
"Terminated"
"Reused"
"InUse"
-
ResourceRetainedBillableTimeInSeconds
— (Integer
)The billable time in seconds used by the warm pool. Billable time refers to the absolute wall-clock time.
Multiply
ResourceRetainedBillableTimeInSeconds
by the number of instances (InstanceCount
) in your training cluster to get the total compute time SageMaker bills you if you run warm pool training. The formula is as follows:ResourceRetainedBillableTimeInSeconds * InstanceCount
.ReusedByJob
— (String
)The name of the matching training job that reused the warm pool.
VpcConfig
— (map
)A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
StoppingCondition
— (map
)Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the
SIGTERM
signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
CreationTime
— (Date
)A timestamp that indicates when the training job was created.
TrainingStartTime
— (Date
)Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of
TrainingEndTime
. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.TrainingEndTime
— (Date
)Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of
TrainingStartTime
and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.LastModifiedTime
— (Date
)A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions
— (Array<map>
)A history of all of the secondary statuses that the training job has transitioned through.
Status
— required — (String
)Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
- InProgress
-
-
Starting
- Starting the training job. -
Downloading
- An optional stage for algorithms that supportFile
training input mode. It indicates that data is being downloaded to the ML storage volumes. -
Training
- Training is in progress. -
Uploading
- Training is complete and the model artifacts are being uploaded to the S3 location.
-
- Completed
-
-
Completed
- The training job has completed.
-
- Failed
-
-
Failed
- The training job has failed. The reason for the failure is returned in theFailureReason
field ofDescribeTrainingJobResponse
.
-
- Stopped
-
-
MaxRuntimeExceeded
- The job stopped because it exceeded the maximum allowed runtime. -
Stopped
- The training job has stopped.
-
- Stopping
-
-
Stopping
- Stopping the training job.
-
We no longer support the following secondary statuses:
-
LaunchingMLInstances
-
PreparingTrainingStack
-
DownloadingTrainingImage
"Starting"
"LaunchingMLInstances"
"PreparingTrainingStack"
"Downloading"
"DownloadingTrainingImage"
"Training"
"Uploading"
"Stopping"
"Stopped"
"MaxRuntimeExceeded"
"Completed"
"Failed"
"Interrupted"
"MaxWaitTimeExceeded"
"Updating"
"Restarting"
"Pending"
StartTime
— required — (Date
)A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime
— (Date
)A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage
— (String
)A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
- Starting
-
-
Starting the training job.
-
Launching requested ML instances.
-
Insufficient capacity error from EC2 while launching instances, retrying!
-
Launched instance was unhealthy, replacing it!
-
Preparing the instances for training.
-
- Training
-
-
Training image download completed. Training in progress.
-
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view
TrainingJobStatus
andSecondaryStatus
in DescribeTrainingJob, andStatusMessage
together. For example, at the start of a training job, you might see the following:-
TrainingJobStatus
- InProgress -
SecondaryStatus
- Training -
StatusMessage
- Downloading the training image
FinalMetricDataList
— (Array<map>
)A collection of
MetricData
objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.MetricName
— (String
)The name of the metric.
Value
— (Float
)The value of the metric.
Timestamp
— (Date
)The date and time that the algorithm emitted the metric.
EnableNetworkIsolation
— (Boolean
)If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose
True
. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.EnableManagedSpotTraining
— (Boolean
)A Boolean indicating whether managed spot training is enabled (
True
) or not (False
).CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
TrainingTimeInSeconds
— (Integer
)The training time in seconds.
BillableTimeInSeconds
— (Integer
)The billable time in seconds. Billable time refers to the absolute wall-clock time.
Multiply
BillableTimeInSeconds
by the number of instances (InstanceCount
) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows:BillableTimeInSeconds * InstanceCount
.You can calculate the savings from using managed spot training using the formula
(1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100
. For example, ifBillableTimeInSeconds
is 100 andTrainingTimeInSeconds
is 500, the savings is 80%.DebugHookConfig
— (map
)Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the
DebugHookConfig
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.LocalPath
— (String
)Path to local storage location for metrics and tensors. Defaults to
/opt/ml/output/tensors/
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for metrics and tensors.
HookParameters
— (map<String>
)Configuration information for the Amazon SageMaker Debugger hook parameters.
CollectionConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the
CollectionConfiguration
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.CollectionName
— (String
)The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters
— (map<String>
)Parameter values for the tensor collection. The allowed parameters are
"name"
,"include_regex"
,"reduction_config"
,"save_config"
,"tensor_names"
, and"save_histogram"
.
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
DebugRuleConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a custom rule for debugging a training job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
TensorBoardOutputConfig
— (map
)Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath
— (String
)Path to local storage location for tensorBoard output. Defaults to
/opt/ml/output/tensorboard
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses
— (Array<map>
)Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.
RuleConfigurationName
— (String
)The name of the rule configuration.
RuleEvaluationJobArn
— (String
)The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus
— (String
)Status of the rule evaluation.
Possible values include:"InProgress"
"NoIssuesFound"
"IssuesFound"
"Error"
"Stopping"
"Stopped"
StatusDetails
— (String
)Details from the rule evaluation.
LastModifiedTime
— (Date
)Timestamp when the rule evaluation status was last modified.
ProfilerConfig
— (map
)Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath
— (String
)Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds
— (Integer
)A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters
— (map<String>
)Configuration information for capturing framework metrics. Available key strings for different profiling options are
DetailedProfilingConfig
,PythonProfilingConfig
, andDataLoaderProfilingConfig
. The following codes are configuration structures for theProfilingParameters
parameter. To learn more about how to configure theProfilingParameters
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.DisableProfiler
— (Boolean
)Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to
True
.
ProfilerRuleConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container Registry Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a custom rule for profiling a training job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
ProfilerRuleEvaluationStatuses
— (Array<map>
)Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.
RuleConfigurationName
— (String
)The name of the rule configuration.
RuleEvaluationJobArn
— (String
)The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus
— (String
)Status of the rule evaluation.
Possible values include:"InProgress"
"NoIssuesFound"
"IssuesFound"
"Error"
"Stopping"
"Stopped"
StatusDetails
— (String
)Details from the rule evaluation.
LastModifiedTime
— (Date
)Timestamp when the rule evaluation status was last modified.
ProfilingStatus
— (String
)Profiling status of a training job.
Possible values include:"Enabled"
"Disabled"
Environment
— (map<String>
)The environment variables to set in the Docker container.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
RemoteDebugConfig
— (map
)Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
EnableRemoteDebug
— (Boolean
)If set to True, enables remote debugging.
InfraCheckConfig
— (map
)Contains information about the infrastructure health check configuration for the training job.
EnableInfraCheck
— (Boolean
)Enables an infrastructure health check.
-
(AWS.Response)
—
Returns:
Waiter Resource States:
describeTransformJob(params = {}, callback) ⇒ AWS.Request
Returns information about a transform job.
Service Reference:
Examples:
Calling the describeTransformJob operation
var params = { TransformJobName: 'STRING_VALUE' /* required */ }; sagemaker.describeTransformJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TransformJobName
— (String
)The name of the transform job that you want to view details of.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TransformJobName
— (String
)The name of the transform job.
TransformJobArn
— (String
)The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus
— (String
)The status of the transform job. If the transform job failed, the reason is returned in the
Possible values include:FailureReason
field."InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
FailureReason
— (String
)If the transform job failed,
FailureReason
describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch.ModelName
— (String
)The name of the model used in the transform job.
MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.
ModelClientConfig
— (map
)The timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds
— (Integer
)The timeout value in seconds for an invocation request. The default value is 600.
InvocationsMaxRetries
— (Integer
)The maximum number of retries when invocation requests are failing. The default value is 3.
MaxPayloadInMB
— (Integer
)The maximum payload size, in MB, used in the transform job.
BatchStrategy
— (String
)Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
To enable the batch strategy, you must set
Possible values include:SplitType
toLine
,RecordIO
, orTFRecord
."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— (map
)Describes the dataset to be transformed and the Amazon S3 location where it is stored.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— (map
)Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
DataCaptureConfig
— (map
)Configuration to control how SageMaker captures inference data.
DestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
GenerateInferenceId
— (Boolean
)Flag that indicates whether to append inference id to the output.
TransformResources
— (map
)Describes the resources, including ML instance types and ML instance count, to use for the transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. The default value is
1
, and the maximum is100
. For distributed transform jobs, specify a value greater than1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
CreationTime
— (Date
)A timestamp that shows when the transform Job was created.
TransformStartTime
— (Date
)Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of
TransformEndTime
.TransformEndTime
— (Date
)Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of
TransformStartTime
.LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
AutoMLJobArn
— (String
)The Amazon Resource Name (ARN) of the AutoML transform job.
DataProcessing
— (map
)The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
InputFilter
— (String
)A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
InputFilter
parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value$
.Examples:
"$"
,"$[1:]"
,"$.features"
OutputFilter
— (String
)A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value,
$
. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.Examples:
"$"
,"$[0,5:]"
,"$['id','SageMakerOutput']"
JoinSource
— (String
)Specifies the source of the data to join with the transformed data. The valid values are
None
andInput
. The default value isNone
, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, setJoinSource
toInput
. You can specifyOutputFilter
as an additional filter to select a portion of the joined dataset and store it in the output file.For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called
SageMakerOutput
. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under theSageMakerInput
key and the results are stored inSageMakerOutput
.For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
Possible values include:"Input"
"None"
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
-
(AWS.Response)
—
Returns:
Waiter Resource States:
describeTrial(params = {}, callback) ⇒ AWS.Request
Provides a list of a trial's properties.
Service Reference:
Examples:
Calling the describeTrial operation
var params = { TrialName: 'STRING_VALUE' /* required */ }; sagemaker.describeTrial(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialName
— (String
)The name of the trial to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialName
— (String
)The name of the trial.
TrialArn
— (String
)The Amazon Resource Name (ARN) of the trial.
DisplayName
— (String
)The name of the trial as displayed. If
DisplayName
isn't specified,TrialName
is displayed.ExperimentName
— (String
)The name of the experiment the trial is part of.
Source
— (map
)The Amazon Resource Name (ARN) of the source and, optionally, the job type.
SourceArn
— required — (String
)The Amazon Resource Name (ARN) of the source.
SourceType
— (String
)The source job type.
CreationTime
— (Date
)When the trial was created.
CreatedBy
— (map
)Who created the trial.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)When the trial was last modified.
LastModifiedBy
— (map
)Who last modified the trial.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
-
(AWS.Response)
—
Returns:
describeTrialComponent(params = {}, callback) ⇒ AWS.Request
Provides a list of a trials component's properties.
Service Reference:
Examples:
Calling the describeTrialComponent operation
var params = { TrialComponentName: 'STRING_VALUE' /* required */ }; sagemaker.describeTrialComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialComponentName
— (String
)The name of the trial component to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialComponentName
— (String
)The name of the trial component.
TrialComponentArn
— (String
)The Amazon Resource Name (ARN) of the trial component.
DisplayName
— (String
)The name of the component as displayed. If
DisplayName
isn't specified,TrialComponentName
is displayed.Source
— (map
)The Amazon Resource Name (ARN) of the source and, optionally, the job type.
SourceArn
— required — (String
)The source Amazon Resource Name (ARN).
SourceType
— (String
)The source job type.
Status
— (map
)The status of the component. States include:
-
InProgress
-
Completed
-
Failed
PrimaryStatus
— (String
)The status of the trial component.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
Message
— (String
)If the component failed, a message describing why.
-
StartTime
— (Date
)When the component started.
EndTime
— (Date
)When the component ended.
CreationTime
— (Date
)When the component was created.
CreatedBy
— (map
)Who created the trial component.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)When the component was last modified.
LastModifiedBy
— (map
)Who last modified the component.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
Parameters
— (map<map>
)The hyperparameters of the component.
StringValue
— (String
)The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the
NumberValue
parameter.NumberValue
— (Float
)The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the
StringValue
parameter.
InputArtifacts
— (map<map>
)The input artifacts of the component.
MediaType
— (String
)The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value
— required — (String
)The location of the artifact.
OutputArtifacts
— (map<map>
)The output artifacts of the component.
MediaType
— (String
)The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value
— required — (String
)The location of the artifact.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
Metrics
— (Array<map>
)The metrics for the component.
MetricName
— (String
)The name of the metric.
SourceArn
— (String
)The Amazon Resource Name (ARN) of the source.
TimeStamp
— (Date
)When the metric was last updated.
Max
— (Float
)The maximum value of the metric.
Min
— (Float
)The minimum value of the metric.
Last
— (Float
)The most recent value of the metric.
Count
— (Integer
)The number of samples used to generate the metric.
Avg
— (Float
)The average value of the metric.
StdDev
— (Float
)The standard deviation of the metric.
LineageGroupArn
— (String
)The Amazon Resource Name (ARN) of the lineage group.
Sources
— (Array<map>
)A list of ARNs and, if applicable, job types for multiple sources of an experiment run.
SourceArn
— required — (String
)The source Amazon Resource Name (ARN).
SourceType
— (String
)The source job type.
-
(AWS.Response)
—
Returns:
describeUserProfile(params = {}, callback) ⇒ AWS.Request
Describes a user profile. For more information, see
CreateUserProfile
.Service Reference:
Examples:
Calling the describeUserProfile operation
var params = { DomainId: 'STRING_VALUE', /* required */ UserProfileName: 'STRING_VALUE' /* required */ }; sagemaker.describeUserProfile(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The user profile name. This value is not case sensitive.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DomainId
— (String
)The ID of the domain that contains the profile.
UserProfileArn
— (String
)The user profile Amazon Resource Name (ARN).
UserProfileName
— (String
)The user profile name.
HomeEfsFileSystemUid
— (String
)The ID of the user's profile in the Amazon Elastic File System volume.
Status
— (String
)The status.
Possible values include:"Deleting"
"Failed"
"InService"
"Pending"
"Updating"
"Update_Failed"
"Delete_Failed"
LastModifiedTime
— (Date
)The last modified time.
CreationTime
— (Date
)The creation time.
FailureReason
— (String
)The failure reason.
SingleSignOnUserIdentifier
— (String
)The IAM Identity Center user identifier.
SingleSignOnUserValue
— (String
)The IAM Identity Center user value.
UserSettings
— (map
)A collection of settings.
ExecutionRole
— (String
)The execution role for the user.
SecurityGroups
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the
CreateDomain.AppNetworkAccessType
parameter is set toPublicInternetOnly
.Required when the
CreateDomain.AppNetworkAccessType
parameter is set toVpcOnly
, unless specified as part of theDefaultUserSettings
for the domain.Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SharingSettings
— (map
)Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption
— (String
)Whether to include the notebook cell output when sharing the notebook. The default is
Possible values include:Disabled
."Allowed"
"Disabled"
S3OutputPath
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon S3 bucket used to store the shared notebook snapshots.S3KmsKeyId
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings
— (map
)The Jupyter server's app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The kernel gateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
TensorBoardAppSettings
— (map
)The TensorBoard app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings
— (map
)A collection of settings that configure user interaction with the
RStudioServerPro
app.AccessStatus
— (String
)Indicates whether the current user has access to the
Possible values include:RStudioServerPro
app."ENABLED"
"DISABLED"
UserGroup
— (String
)The level of permissions that the user has within the
Possible values include:RStudioServerPro
app. This value defaults toUser
. TheAdmin
value allows the user access to the RStudio Administrative Dashboard."R_STUDIO_ADMIN"
"R_STUDIO_USER"
RSessionAppSettings
— (map
)A collection of settings that configure the
RSessionGateway
app.DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a RSession app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
CanvasAppSettings
— (map
)The Canvas app settings.
TimeSeriesForecastingSettings
— (map
)Time series forecast settings for the SageMaker Canvas application.
Status
— (String
)Describes whether time series forecasting is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
AmazonForecastRoleArn
— (String
)The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the
UserProfile
that launches the Canvas application. If an execution role is not specified in theUserProfile
, Canvas uses the execution role specified in the Domain that owns theUserProfile
. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached andforecast.amazonaws.com
added in the trust relationship as a service principal.
ModelRegisterSettings
— (map
)The model registry settings for the SageMaker Canvas application.
Status
— (String
)Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CrossAccountModelRegisterRoleArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings
— (map
)The workspace settings for the SageMaker Canvas application.
S3ArtifactPath
— (String
)The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings
— (Array<map>
)The settings for connecting to an external data source with OAuth.
DataSourceName
— (String
)The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Possible values include:"SalesforceGenie"
"Snowflake"
Status
— (String
)Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
SecretArn
— (String
)The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings
— (map
)The model deployment settings for the SageMaker Canvas application.
Status
— (String
)Describes whether model deployment permissions are enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
KendraSettings
— (map
)The settings for document querying.
Status
— (String
)Describes whether the document querying feature is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
GenerativeAiSettings
— (map
)The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn
— (String
)The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes
bedrock.amazonaws.com
as a service principal.
EmrServerlessSettings
— (map
)The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status
— (String
)Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CodeEditorAppSettings
— (map
)The Code Editor application settings.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a Code Editor app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a JupyterLab app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set
LifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings
— (map
)The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
ExecutionRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
SpaceStorageSettings
— (map
)The storage settings for a space.
DefaultEbsStorageSettings
— (map
)The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb
— required — (Integer
)The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb
— required — (Integer
)The maximum size of the EBS storage volume for a space.
DefaultLandingUri
— (String
)The default experience that the user is directed to when accessing the domain. The supported values are:
-
studio::
: Indicates that Studio is the default experience. This value can only be passed ifStudioWebPortal
is set toENABLED
. -
app:JupyterServer:
: Indicates that Studio Classic is the default experience.
-
StudioWebPortal
— (String
)Whether the user can access Studio. If this value is set to
Possible values include:DISABLED
, the user cannot access Studio, even if that is the default experience for the domain."ENABLED"
"DISABLED"
CustomPosixUserConfig
— (map
)Details about the POSIX identity that is used for file system operations.
Uid
— required — (Integer
)The POSIX user ID.
Gid
— required — (Integer
)The POSIX group ID.
CustomFileSystemConfigs
— (Array<map>
)The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystemConfig
— (map
)The settings for a custom Amazon EFS file system.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
FileSystemPath
— (String
)The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings
— (map
)Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools
— (Array<String>
)The machine learning tools that are hidden from the Studio left navigation pane.
HiddenAppTypes
— (Array<String>
)The Applications supported in Studio that are hidden from the Studio left navigation pane.
AutoMountHomeEFS
— (String
)Indicates whether auto-mounting of an EFS volume is supported for the user profile. The
Possible values include:DefaultAsDomain
value is only supported for user profiles. Do not use theDefaultAsDomain
value when setting this parameter for a domain."Enabled"
"Disabled"
"DefaultAsDomain"
-
(AWS.Response)
—
Returns:
describeWorkforce(params = {}, callback) ⇒ AWS.Request
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks.
This operation applies only to private workforces.
Service Reference:
Examples:
Calling the describeWorkforce operation
var params = { WorkforceName: 'STRING_VALUE' /* required */ }; sagemaker.describeWorkforce(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
WorkforceName
— (String
)The name of the private workforce whose access you want to restrict.
WorkforceName
is automatically set todefault
when a workforce is created and cannot be modified.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Workforce
— (map
)A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
WorkforceName
— required — (String
)The name of the private workforce.
WorkforceArn
— required — (String
)The Amazon Resource Name (ARN) of the private workforce.
LastUpdatedDate
— (Date
)The most recent date that UpdateWorkforce was used to successfully add one or more IP address ranges (CIDRs) to a private workforce's allow list.
SourceIpConfig
— (map
)A list of one to ten IP address ranges (CIDRs) to be added to the workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
Cidrs
— required — (Array<String>
)A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note: The following Length Constraints apply to individual CIDR values in the CIDR value list.
SubDomain
— (String
)The subdomain for your OIDC Identity Provider.
CognitoConfig
— (map
)The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
UserPool
— required — (String
)A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId
— required — (String
)The client ID for your Amazon Cognito user pool.
OidcConfig
— (map
)The configuration of an OIDC Identity Provider (IdP) private workforce.
ClientId
— (String
)The OIDC IdP client ID used to configure your private workforce.
Issuer
— (String
)The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint
— (String
)The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint
— (String
)The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint
— (String
)The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint
— (String
)The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri
— (String
)The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
Scope
— (String
)An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.
AuthenticationRequestExtraParams
— (map<String>
)A string to string map of identifiers specific to the custom identity provider (IdP) being used.
CreateDate
— (Date
)The date that the workforce is created.
WorkforceVpcConfig
— (map
)The configuration of a VPC workforce.
VpcId
— required — (String
)The ID of the VPC that the workforce uses for communication.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
Subnets
— required — (Array<String>
)The ID of the subnets in the VPC that you want to connect.
VpcEndpointId
— (String
)The IDs for the VPC service endpoints of your VPC workforce when it is created and updated.
Status
— (String
)The status of your workforce.
Possible values include:"Initializing"
"Updating"
"Deleting"
"Failed"
"Active"
FailureReason
— (String
)The reason your workforce failed.
-
(AWS.Response)
—
Returns:
describeWorkteam(params = {}, callback) ⇒ AWS.Request
Gets information about a specific work team. You can see information such as the creation date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
Service Reference:
Examples:
Calling the describeWorkteam operation
var params = { WorkteamName: 'STRING_VALUE' /* required */ }; sagemaker.describeWorkteam(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
WorkteamName
— (String
)The name of the work team to return a description of.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Workteam
— (map
)A
Workteam
instance that contains information about the work team.WorkteamName
— required — (String
)The name of the work team.
MemberDefinitions
— required — (Array<map>
)A list of
MemberDefinition
objects that contains objects that identify the workers that make up the work team.Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use
CognitoMemberDefinition
. For workforces created using your own OIDC identity provider (IdP) useOidcMemberDefinition
.CognitoMemberDefinition
— (map
)The Amazon Cognito user group that is part of the work team.
UserPool
— required — (String
)An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup
— required — (String
)An identifier for a user group.
ClientId
— required — (String
)An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition
— (map
)A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of
Groups
, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.Groups
— (Array<String>
)A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
WorkteamArn
— required — (String
)The Amazon Resource Name (ARN) that identifies the work team.
WorkforceArn
— (String
)The Amazon Resource Name (ARN) of the workforce.
ProductListingIds
— (Array<String>
)The Amazon Marketplace identifier for a vendor's work team.
Description
— required — (String
)A description of the work team.
SubDomain
— (String
)The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
CreateDate
— (Date
)The date and time that the work team was created (timestamp).
LastUpdatedDate
— (Date
)The date and time that the work team was last updated (timestamp).
NotificationConfiguration
— (map
)Configures SNS notifications of available or expiring work items for work teams.
NotificationTopicArn
— (String
)The ARN for the Amazon SNS topic to which notifications should be published.
WorkerAccessConfiguration
— (map
)Describes any access constraints that have been defined for Amazon S3 resources.
S3Presign
— (map
)Defines any Amazon S3 resource constraints.
IamPolicyConstraints
— (map
)Use this parameter to specify the allowed request source. Possible sources are either
SourceIp
orVpcSourceIp
.SourceIp
— (String
)When
Possible values include:SourceIp
isEnabled
the worker's IP address when a task is rendered in the worker portal is added to the IAM policy as aCondition
used to generate the Amazon S3 presigned URL. This IP address is checked by Amazon S3 and must match in order for the Amazon S3 resource to be rendered in the worker portal."Enabled"
"Disabled"
VpcSourceIp
— (String
)When
Possible values include:VpcSourceIp
isEnabled
the worker's IP address when a task is rendered in private worker portal inside the VPC is added to the IAM policy as aCondition
used to generate the Amazon S3 presigned URL. To render the task successfully Amazon S3 checks that the presigned URL is being accessed over an Amazon S3 VPC Endpoint, and that the worker's IP address matches the IP address in the IAM policy. To learn more about configuring private worker portal, see Use Amazon VPC mode from a private worker portal."Enabled"
"Disabled"
-
(AWS.Response)
—
Returns:
disableSagemakerServicecatalogPortfolio(params = {}, callback) ⇒ AWS.Request
Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
Service Reference:
Examples:
Calling the disableSagemakerServicecatalogPortfolio operation
var params = { }; sagemaker.disableSagemakerServicecatalogPortfolio(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
- params (Object) (defaults to: {})
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
disassociateTrialComponent(params = {}, callback) ⇒ AWS.Request
Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API.
To get a list of the trials a component is associated with, use the Search API. Specify
ExperimentTrialComponent
for theResource
parameter. The list appears in the response underResults.TrialComponent.Parents
.Service Reference:
Examples:
Calling the disassociateTrialComponent operation
var params = { TrialComponentName: 'STRING_VALUE', /* required */ TrialName: 'STRING_VALUE' /* required */ }; sagemaker.disassociateTrialComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialComponentName
— (String
)The name of the component to disassociate from the trial.
TrialName
— (String
)The name of the trial to disassociate from.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialComponentArn
— (String
)The Amazon Resource Name (ARN) of the trial component.
TrialArn
— (String
)The Amazon Resource Name (ARN) of the trial.
-
(AWS.Response)
—
Returns:
enableSagemakerServicecatalogPortfolio(params = {}, callback) ⇒ AWS.Request
Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
Service Reference:
Examples:
Calling the enableSagemakerServicecatalogPortfolio operation
var params = { }; sagemaker.enableSagemakerServicecatalogPortfolio(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
- params (Object) (defaults to: {})
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
getDeviceFleetReport(params = {}, callback) ⇒ AWS.Request
Describes a fleet.
Service Reference:
Examples:
Calling the getDeviceFleetReport operation
var params = { DeviceFleetName: 'STRING_VALUE' /* required */ }; sagemaker.getDeviceFleetReport(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DeviceFleetName
— (String
)The name of the fleet.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DeviceFleetArn
— (String
)The Amazon Resource Name (ARN) of the device.
DeviceFleetName
— (String
)The name of the fleet.
OutputConfig
— (map
)The output configuration for storing sample data collected by the fleet.
S3OutputLocation
— required — (String
)The Amazon Simple Storage (S3) bucker URI.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
PresetDeploymentType
— (String
)The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
Possible values include:"GreengrassV2Component"
PresetDeploymentConfig
— (String
)The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
-
ComponentName
(optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job. -
ComponentDescription
(optional) - Description of the component. -
ComponentVersion
(optional) - The version of the component.Note: Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification. -
PlatformOS
(optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux. -
PlatformArchitecture
(optional) - The processor architecture for the platform.Supported architectures Windows include: Windows32_x86, Windows64_x64.
Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
-
Description
— (String
)Description of the fleet.
ReportGenerated
— (Date
)Timestamp of when the report was generated.
DeviceStats
— (map
)Status of devices.
ConnectedDeviceCount
— required — (Integer
)The number of devices connected with a heartbeat.
RegisteredDeviceCount
— required — (Integer
)The number of registered devices.
AgentVersions
— (Array<map>
)The versions of Edge Manager agent deployed on the fleet.
Version
— required — (String
)Version of the agent.
AgentCount
— required — (Integer
)The number of Edge Manager agents.
ModelStats
— (Array<map>
)Status of model on device.
ModelName
— required — (String
)The name of the model.
ModelVersion
— required — (String
)The model version.
OfflineDeviceCount
— required — (Integer
)The number of devices that have this model version and do not have a heart beat.
ConnectedDeviceCount
— required — (Integer
)The number of devices that have this model version and have a heart beat.
ActiveDeviceCount
— required — (Integer
)The number of devices that have this model version, a heart beat, and are currently running.
SamplingDeviceCount
— required — (Integer
)The number of devices with this model version and are producing sample data.
-
(AWS.Response)
—
Returns:
getLineageGroupPolicy(params = {}, callback) ⇒ AWS.Request
The resource policy for the lineage group.
Service Reference:
Examples:
Calling the getLineageGroupPolicy operation
var params = { LineageGroupName: 'STRING_VALUE' /* required */ }; sagemaker.getLineageGroupPolicy(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
LineageGroupName
— (String
)The name or Amazon Resource Name (ARN) of the lineage group.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:LineageGroupArn
— (String
)The Amazon Resource Name (ARN) of the lineage group.
ResourcePolicy
— (String
)The resource policy that gives access to the lineage group in another account.
-
(AWS.Response)
—
Returns:
getModelPackageGroupPolicy(params = {}, callback) ⇒ AWS.Request
Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
Service Reference:
Examples:
Calling the getModelPackageGroupPolicy operation
var params = { ModelPackageGroupName: 'STRING_VALUE' /* required */ }; sagemaker.getModelPackageGroupPolicy(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageGroupName
— (String
)The name of the model group for which to get the resource policy.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ResourcePolicy
— (String
)The resource policy for the model group.
-
(AWS.Response)
—
Returns:
getSagemakerServicecatalogPortfolioStatus(params = {}, callback) ⇒ AWS.Request
Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
Service Reference:
Examples:
Calling the getSagemakerServicecatalogPortfolioStatus operation
var params = { }; sagemaker.getSagemakerServicecatalogPortfolioStatus(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
- params (Object) (defaults to: {})
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Status
— (String
)Whether Service Catalog is enabled or disabled in SageMaker.
Possible values include:"Enabled"
"Disabled"
-
(AWS.Response)
—
Returns:
getScalingConfigurationRecommendation(params = {}, callback) ⇒ AWS.Request
Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.
Service Reference:
Examples:
Calling the getScalingConfigurationRecommendation operation
var params = { InferenceRecommendationsJobName: 'STRING_VALUE', /* required */ EndpointName: 'STRING_VALUE', RecommendationId: 'STRING_VALUE', ScalingPolicyObjective: { MaxInvocationsPerMinute: 'NUMBER_VALUE', MinInvocationsPerMinute: 'NUMBER_VALUE' }, TargetCpuUtilizationPerCore: 'NUMBER_VALUE' }; sagemaker.getScalingConfigurationRecommendation(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
InferenceRecommendationsJobName
— (String
)The name of a previously completed Inference Recommender job.
RecommendationId
— (String
)The recommendation ID of a previously completed inference recommendation. This ID should come from one of the recommendations returned by the job specified in the
InferenceRecommendationsJobName
field.Specify either this field or the
EndpointName
field.EndpointName
— (String
)The name of an endpoint benchmarked during a previously completed inference recommendation job. This name should come from one of the recommendations returned by the job specified in the
InferenceRecommendationsJobName
field.Specify either this field or the
RecommendationId
field.TargetCpuUtilizationPerCore
— (Integer
)The percentage of how much utilization you want an instance to use before autoscaling. The default value is 50%.
ScalingPolicyObjective
— (map
)An object where you specify the anticipated traffic pattern for an endpoint.
MinInvocationsPerMinute
— (Integer
)The minimum number of expected requests to your endpoint per minute.
MaxInvocationsPerMinute
— (Integer
)The maximum number of expected requests to your endpoint per minute.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceRecommendationsJobName
— (String
)The name of a previously completed Inference Recommender job.
RecommendationId
— (String
)The recommendation ID of a previously completed inference recommendation.
EndpointName
— (String
)The name of an endpoint benchmarked during a previously completed Inference Recommender job.
TargetCpuUtilizationPerCore
— (Integer
)The percentage of how much utilization you want an instance to use before autoscaling, which you specified in the request. The default value is 50%.
ScalingPolicyObjective
— (map
)An object representing the anticipated traffic pattern for an endpoint that you specified in the request.
MinInvocationsPerMinute
— (Integer
)The minimum number of expected requests to your endpoint per minute.
MaxInvocationsPerMinute
— (Integer
)The maximum number of expected requests to your endpoint per minute.
Metric
— (map
)An object with a list of metrics that were benchmarked during the previously completed Inference Recommender job.
InvocationsPerInstance
— (Integer
)The number of invocations sent to a model, normalized by
InstanceCount
in each ProductionVariant.1/numberOfInstances
is sent as the value on each request, wherenumberOfInstances
is the number of active instances for the ProductionVariant behind the endpoint at the time of the request.ModelLatency
— (Integer
)The interval of time taken by a model to respond as viewed from SageMaker. This interval includes the local communication times taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
DynamicScalingConfiguration
— (map
)An object with the recommended values for you to specify when creating an autoscaling policy.
MinCapacity
— (Integer
)The recommended minimum capacity to specify for your autoscaling policy.
MaxCapacity
— (Integer
)The recommended maximum capacity to specify for your autoscaling policy.
ScaleInCooldown
— (Integer
)The recommended scale in cooldown time for your autoscaling policy.
ScaleOutCooldown
— (Integer
)The recommended scale out cooldown time for your autoscaling policy.
ScalingPolicies
— (Array<map>
)An object of the scaling policies for each metric.
TargetTracking
— (map
)A target tracking scaling policy. Includes support for predefined or customized metrics.
MetricSpecification
— (map
)An object containing information about a metric.
Predefined
— (map
)Information about a predefined metric.
PredefinedMetricType
— (String
)The metric type. You can only apply SageMaker metric types to SageMaker endpoints.
Customized
— (map
)Information about a customized metric.
MetricName
— (String
)The name of the customized metric.
Namespace
— (String
)The namespace of the customized metric.
Statistic
— (String
)The statistic of the customized metric.
Possible values include:"Average"
"Minimum"
"Maximum"
"SampleCount"
"Sum"
TargetValue
— (Float
)The recommended target value to specify for the metric when creating a scaling policy.
-
(AWS.Response)
—
Returns:
getSearchSuggestions(params = {}, callback) ⇒ AWS.Request
An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in
Search
queries. Provides suggestions forHyperParameters
,Tags
, andMetrics
.Service Reference:
Examples:
Calling the getSearchSuggestions operation
var params = { Resource: TrainingJob | Experiment | ExperimentTrial | ExperimentTrialComponent | Endpoint | Model | ModelPackage | ModelPackageGroup | Pipeline | PipelineExecution | FeatureGroup | FeatureMetadata | Image | ImageVersion | Project | HyperParameterTuningJob | ModelCard, /* required */ SuggestionQuery: { PropertyNameQuery: { PropertyNameHint: 'STRING_VALUE' /* required */ } } }; sagemaker.getSearchSuggestions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
Resource
— (String
)The name of the SageMaker resource to search for.
Possible values include:"TrainingJob"
"Experiment"
"ExperimentTrial"
"ExperimentTrialComponent"
"Endpoint"
"Model"
"ModelPackage"
"ModelPackageGroup"
"Pipeline"
"PipelineExecution"
"FeatureGroup"
"FeatureMetadata"
"Image"
"ImageVersion"
"Project"
"HyperParameterTuningJob"
"ModelCard"
SuggestionQuery
— (map
)Limits the property names that are included in the response.
PropertyNameQuery
— (map
)Defines a property name hint. Only property names that begin with the specified hint are included in the response.
PropertyNameHint
— required — (String
)Text that begins a property's name.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PropertyNameSuggestions
— (Array<map>
)A list of property names for a
Resource
that match aSuggestionQuery
.PropertyName
— (String
)A suggested property name based on what you entered in the search textbox in the SageMaker console.
-
(AWS.Response)
—
Returns:
importHubContent(params = {}, callback) ⇒ AWS.Request
Import hub content.
Service Reference:
Examples:
Calling the importHubContent operation
var params = { DocumentSchemaVersion: 'STRING_VALUE', /* required */ HubContentDocument: 'STRING_VALUE', /* required */ HubContentName: 'STRING_VALUE', /* required */ HubContentType: Model | Notebook | ModelReference, /* required */ HubName: 'STRING_VALUE', /* required */ HubContentDescription: 'STRING_VALUE', HubContentDisplayName: 'STRING_VALUE', HubContentMarkdown: 'STRING_VALUE', HubContentSearchKeywords: [ 'STRING_VALUE', /* more items */ ], HubContentVersion: 'STRING_VALUE', Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.importHubContent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubContentName
— (String
)The name of the hub content to import.
HubContentVersion
— (String
)The version of the hub content to import.
HubContentType
— (String
)The type of hub content to import.
Possible values include:"Model"
"Notebook"
"ModelReference"
DocumentSchemaVersion
— (String
)The version of the hub content schema to import.
HubName
— (String
)The name of the hub to import content into.
HubContentDisplayName
— (String
)The display name of the hub content to import.
HubContentDescription
— (String
)A description of the hub content to import.
HubContentMarkdown
— (String
)A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.
HubContentDocument
— (String
)The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.
HubContentSearchKeywords
— (Array<String>
)The searchable keywords of the hub content.
Tags
— (Array<map>
)Any tags associated with the hub content.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HubArn
— (String
)The ARN of the hub that the content was imported into.
HubContentArn
— (String
)The ARN of the hub content that was imported.
-
(AWS.Response)
—
Returns:
listActions(params = {}, callback) ⇒ AWS.Request
Lists the actions in your account and their properties.
Service Reference:
Examples:
Calling the listActions operation
var params = { ActionType: 'STRING_VALUE', CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending, SourceUri: 'STRING_VALUE' }; sagemaker.listActions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SourceUri
— (String
)A filter that returns only actions with the specified source URI.
ActionType
— (String
)A filter that returns only actions of the specified type.
CreatedAfter
— (Date
)A filter that returns only actions created on or after the specified time.
CreatedBefore
— (Date
)A filter that returns only actions created on or before the specified time.
SortBy
— (String
)The property used to sort results. The default value is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order. The default value is
Possible values include:Descending
."Ascending"
"Descending"
NextToken
— (String
)If the previous call to
ListActions
didn't return the full set of actions, the call returns a token for getting the next set of actions.MaxResults
— (Integer
)The maximum number of actions to return in the response. The default value is 10.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ActionSummaries
— (Array<map>
)A list of actions and their properties.
ActionArn
— (String
)The Amazon Resource Name (ARN) of the action.
ActionName
— (String
)The name of the action.
Source
— (map
)The source of the action.
SourceUri
— required — (String
)The URI of the source.
SourceType
— (String
)The type of the source.
SourceId
— (String
)The ID of the source.
ActionType
— (String
)The type of the action.
Status
— (String
)The status of the action.
Possible values include:"Unknown"
"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
CreationTime
— (Date
)When the action was created.
LastModifiedTime
— (Date
)When the action was last modified.
NextToken
— (String
)A token for getting the next set of actions, if there are any.
-
(AWS.Response)
—
Returns:
listAlgorithms(params = {}, callback) ⇒ AWS.Request
Lists the machine learning algorithms that have been created.
Service Reference:
Examples:
Calling the listAlgorithms operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listAlgorithms(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only algorithms created after the specified time (timestamp).
CreationTimeBefore
— (Date
)A filter that returns only algorithms created before the specified time (timestamp).
MaxResults
— (Integer
)The maximum number of algorithms to return in the response.
NameContains
— (String
)A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.
NextToken
— (String
)If the response to a previous
ListAlgorithms
request was truncated, the response includes aNextToken
. To retrieve the next set of algorithms, use the token in the next request.SortBy
— (String
)The parameter by which to sort the results. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order for the results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AlgorithmSummaryList
— (Array<map>
)>An array of
AlgorithmSummary
objects, each of which lists an algorithm.AlgorithmName
— required — (String
)The name of the algorithm that is described by the summary.
AlgorithmArn
— required — (String
)The Amazon Resource Name (ARN) of the algorithm.
AlgorithmDescription
— (String
)A brief description of the algorithm.
CreationTime
— required — (Date
)A timestamp that shows when the algorithm was created.
AlgorithmStatus
— required — (String
)The overall status of the algorithm.
Possible values include:"Pending"
"InProgress"
"Completed"
"Failed"
"Deleting"
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listAliases(params = {}, callback) ⇒ AWS.Request
Lists the aliases of a specified image or image version.
Service Reference:
Examples:
Calling the listAliases operation
var params = { ImageName: 'STRING_VALUE', /* required */ Alias: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', Version: 'NUMBER_VALUE' }; sagemaker.listAliases(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ImageName
— (String
)The name of the image.
Alias
— (String
)The alias of the image version.
Version
— (Integer
)The version of the image. If image version is not specified, the aliases of all versions of the image are listed.
MaxResults
— (Integer
)The maximum number of aliases to return.
NextToken
— (String
)If the previous call to
ListAliases
didn't return the full set of aliases, the call returns a token for retrieving the next set of aliases.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:SageMakerImageVersionAliases
— (Array<String>
)A list of SageMaker image version aliases.
NextToken
— (String
)A token for getting the next set of aliases, if more aliases exist.
-
(AWS.Response)
—
Returns:
listAppImageConfigs(params = {}, callback) ⇒ AWS.Request
Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
Service Reference:
Examples:
Calling the listAppImageConfigs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', ModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, ModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime | LastModifiedTime | Name, SortOrder: Ascending | Descending }; sagemaker.listAppImageConfigs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MaxResults
— (Integer
)The total number of items to return in the response. If the total number of items available is more than the value specified, a
NextToken
is provided in the response. To resume pagination, provide theNextToken
value in the as part of a subsequent call. The default value is 10.NextToken
— (String
)If the previous call to
ListImages
didn't return the full set of AppImageConfigs, the call returns a token for getting the next set of AppImageConfigs.NameContains
— (String
)A filter that returns only AppImageConfigs whose name contains the specified string.
CreationTimeBefore
— (Date
)A filter that returns only AppImageConfigs created on or before the specified time.
CreationTimeAfter
— (Date
)A filter that returns only AppImageConfigs created on or after the specified time.
ModifiedTimeBefore
— (Date
)A filter that returns only AppImageConfigs modified on or before the specified time.
ModifiedTimeAfter
— (Date
)A filter that returns only AppImageConfigs modified on or after the specified time.
SortBy
— (String
)The property used to sort results. The default value is
Possible values include:CreationTime
."CreationTime"
"LastModifiedTime"
"Name"
SortOrder
— (String
)The sort order. The default value is
Possible values include:Descending
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NextToken
— (String
)A token for getting the next set of AppImageConfigs, if there are any.
AppImageConfigs
— (Array<map>
)A list of AppImageConfigs and their properties.
AppImageConfigArn
— (String
)The ARN of the AppImageConfig.
AppImageConfigName
— (String
)The name of the AppImageConfig. Must be unique to your account.
CreationTime
— (Date
)When the AppImageConfig was created.
LastModifiedTime
— (Date
)When the AppImageConfig was last modified.
KernelGatewayImageConfig
— (map
)The configuration for the file system and kernels in the SageMaker image.
KernelSpecs
— required — (Array<map>
)The specification of the Jupyter kernels in the image.
Name
— required — (String
)The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName
— (String
)The display name of the kernel.
FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
JupyterLabAppImageConfig
— (map
)The configuration for the file system and the runtime, such as the environment variables and entry point.
FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
ContainerConfig
— (map
)The configuration used to run the application image container.
ContainerArguments
— (Array<String>
)The arguments for the container when you're running the application.
ContainerEntrypoint
— (Array<String>
)The entrypoint used to run the application in the container.
ContainerEnvironmentVariables
— (map<String>
)The environment variables to set in the container
CodeEditorAppImageConfig
— (map
)The configuration for the file system and the runtime, such as the environment variables and entry point.
FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
ContainerConfig
— (map
)The configuration used to run the application image container.
ContainerArguments
— (Array<String>
)The arguments for the container when you're running the application.
ContainerEntrypoint
— (Array<String>
)The entrypoint used to run the application in the container.
ContainerEnvironmentVariables
— (map<String>
)The environment variables to set in the container
-
(AWS.Response)
—
Returns:
listApps(params = {}, callback) ⇒ AWS.Request
Lists apps.
Service Reference:
Examples:
Calling the listApps operation
var params = { DomainIdEquals: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime, SortOrder: Ascending | Descending, SpaceNameEquals: 'STRING_VALUE', UserProfileNameEquals: 'STRING_VALUE' }; sagemaker.listApps(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
MaxResults
— (Integer
)This parameter defines the maximum number of results that can be return in a single response. The
MaxResults
parameter is an upper bound, not a target. If there are more results available than the value specified, aNextToken
is provided in the response. TheNextToken
indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value forMaxResults
is 10.SortOrder
— (String
)The sort order for the results. The default is Ascending.
Possible values include:"Ascending"
"Descending"
SortBy
— (String
)The parameter by which to sort the results. The default is CreationTime.
Possible values include:"CreationTime"
DomainIdEquals
— (String
)A parameter to search for the domain ID.
UserProfileNameEquals
— (String
)A parameter to search by user profile name. If
SpaceNameEquals
is set, then this value cannot be set.SpaceNameEquals
— (String
)A parameter to search by space name. If
UserProfileNameEquals
is set, then this value cannot be set.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Apps
— (Array<map>
)The list of apps.
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The user profile name.
SpaceName
— (String
)The name of the space.
AppType
— (String
)The type of app.
Possible values include:"JupyterServer"
"KernelGateway"
"DetailedProfiler"
"TensorBoard"
"CodeEditor"
"JupyterLab"
"RStudioServerPro"
"RSessionGateway"
"Canvas"
AppName
— (String
)The name of the app.
Status
— (String
)The status.
Possible values include:"Deleted"
"Deleting"
"Failed"
"InService"
"Pending"
CreationTime
— (Date
)The creation time.
ResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
-
(AWS.Response)
—
Returns:
listArtifacts(params = {}, callback) ⇒ AWS.Request
Lists the artifacts in your account and their properties.
Service Reference:
Examples:
Calling the listArtifacts operation
var params = { ArtifactType: 'STRING_VALUE', CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime, SortOrder: Ascending | Descending, SourceUri: 'STRING_VALUE' }; sagemaker.listArtifacts(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SourceUri
— (String
)A filter that returns only artifacts with the specified source URI.
ArtifactType
— (String
)A filter that returns only artifacts of the specified type.
CreatedAfter
— (Date
)A filter that returns only artifacts created on or after the specified time.
CreatedBefore
— (Date
)A filter that returns only artifacts created on or before the specified time.
SortBy
— (String
)The property used to sort results. The default value is
Possible values include:CreationTime
."CreationTime"
SortOrder
— (String
)The sort order. The default value is
Possible values include:Descending
."Ascending"
"Descending"
NextToken
— (String
)If the previous call to
ListArtifacts
didn't return the full set of artifacts, the call returns a token for getting the next set of artifacts.MaxResults
— (Integer
)The maximum number of artifacts to return in the response. The default value is 10.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ArtifactSummaries
— (Array<map>
)A list of artifacts and their properties.
ArtifactArn
— (String
)The Amazon Resource Name (ARN) of the artifact.
ArtifactName
— (String
)The name of the artifact.
Source
— (map
)The source of the artifact.
SourceUri
— required — (String
)The URI of the source.
SourceTypes
— (Array<map>
)A list of source types.
SourceIdType
— required — (String
)The type of ID.
Possible values include:"MD5Hash"
"S3ETag"
"S3Version"
"Custom"
Value
— required — (String
)The ID.
ArtifactType
— (String
)The type of the artifact.
CreationTime
— (Date
)When the artifact was created.
LastModifiedTime
— (Date
)When the artifact was last modified.
NextToken
— (String
)A token for getting the next set of artifacts, if there are any.
-
(AWS.Response)
—
Returns:
listAssociations(params = {}, callback) ⇒ AWS.Request
Lists the associations in your account and their properties.
Service Reference:
Examples:
Calling the listAssociations operation
var params = { AssociationType: ContributedTo | AssociatedWith | DerivedFrom | Produced | SameAs, CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, DestinationArn: 'STRING_VALUE', DestinationType: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: SourceArn | DestinationArn | SourceType | DestinationType | CreationTime, SortOrder: Ascending | Descending, SourceArn: 'STRING_VALUE', SourceType: 'STRING_VALUE' }; sagemaker.listAssociations(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SourceArn
— (String
)A filter that returns only associations with the specified source ARN.
DestinationArn
— (String
)A filter that returns only associations with the specified destination Amazon Resource Name (ARN).
SourceType
— (String
)A filter that returns only associations with the specified source type.
DestinationType
— (String
)A filter that returns only associations with the specified destination type.
AssociationType
— (String
)A filter that returns only associations of the specified type.
Possible values include:"ContributedTo"
"AssociatedWith"
"DerivedFrom"
"Produced"
"SameAs"
CreatedAfter
— (Date
)A filter that returns only associations created on or after the specified time.
CreatedBefore
— (Date
)A filter that returns only associations created on or before the specified time.
SortBy
— (String
)The property used to sort results. The default value is
Possible values include:CreationTime
."SourceArn"
"DestinationArn"
"SourceType"
"DestinationType"
"CreationTime"
SortOrder
— (String
)The sort order. The default value is
Possible values include:Descending
."Ascending"
"Descending"
NextToken
— (String
)If the previous call to
ListAssociations
didn't return the full set of associations, the call returns a token for getting the next set of associations.MaxResults
— (Integer
)The maximum number of associations to return in the response. The default value is 10.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AssociationSummaries
— (Array<map>
)A list of associations and their properties.
SourceArn
— (String
)The ARN of the source.
DestinationArn
— (String
)The Amazon Resource Name (ARN) of the destination.
SourceType
— (String
)The source type.
DestinationType
— (String
)The destination type.
AssociationType
— (String
)The type of the association.
Possible values include:"ContributedTo"
"AssociatedWith"
"DerivedFrom"
"Produced"
"SameAs"
SourceName
— (String
)The name of the source.
DestinationName
— (String
)The name of the destination.
CreationTime
— (Date
)When the association was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
NextToken
— (String
)A token for getting the next set of associations, if there are any.
-
(AWS.Response)
—
Returns:
listAutoMLJobs(params = {}, callback) ⇒ AWS.Request
Request a list of jobs.
Service Reference:
Examples:
Calling the listAutoMLJobs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: Completed | InProgress | Failed | Stopped | Stopping }; sagemaker.listAutoMLJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)Request a list of jobs, using a filter for time.
CreationTimeBefore
— (Date
)Request a list of jobs, using a filter for time.
LastModifiedTimeAfter
— (Date
)Request a list of jobs, using a filter for time.
LastModifiedTimeBefore
— (Date
)Request a list of jobs, using a filter for time.
NameContains
— (String
)Request a list of jobs, using a search filter for name.
StatusEquals
— (String
)Request a list of jobs, using a filter for status.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
SortOrder
— (String
)The sort order for the results. The default is
Possible values include:Descending
."Ascending"
"Descending"
SortBy
— (String
)The parameter by which to sort the results. The default is
Possible values include:Name
."Name"
"CreationTime"
"Status"
MaxResults
— (Integer
)Request a list of jobs up to a specified limit.
NextToken
— (String
)If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AutoMLJobSummaries
— (Array<map>
)Returns a summary list of jobs.
AutoMLJobName
— required — (String
)The name of the AutoML job you are requesting.
AutoMLJobArn
— required — (String
)The ARN of the AutoML job.
AutoMLJobStatus
— required — (String
)The status of the AutoML job.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
AutoMLJobSecondaryStatus
— required — (String
)The secondary status of the AutoML job.
Possible values include:"Starting"
"MaxCandidatesReached"
"Failed"
"Stopped"
"MaxAutoMLJobRuntimeReached"
"Stopping"
"CandidateDefinitionsGenerated"
"Completed"
"ExplainabilityError"
"DeployingModel"
"ModelDeploymentError"
"GeneratingModelInsightsReport"
"ModelInsightsError"
"AnalyzingData"
"FeatureEngineering"
"ModelTuning"
"GeneratingExplainabilityReport"
"TrainingModels"
"PreTraining"
CreationTime
— required — (Date
)When the AutoML job was created.
EndTime
— (Date
)The end time of an AutoML job.
LastModifiedTime
— required — (Date
)When the AutoML job was last modified.
FailureReason
— (String
)The failure reason of an AutoML job.
PartialFailureReasons
— (Array<map>
)The list of reasons for partial failures within an AutoML job.
PartialFailureMessage
— (String
)The message containing the reason for a partial failure of an AutoML job.
NextToken
— (String
)If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
-
(AWS.Response)
—
Returns:
listCandidatesForAutoMLJob(params = {}, callback) ⇒ AWS.Request
List the candidates created for the job.
Service Reference:
Examples:
Calling the listCandidatesForAutoMLJob operation
var params = { AutoMLJobName: 'STRING_VALUE', /* required */ CandidateNameEquals: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime | Status | FinalObjectiveMetricValue, SortOrder: Ascending | Descending, StatusEquals: Completed | InProgress | Failed | Stopped | Stopping }; sagemaker.listCandidatesForAutoMLJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AutoMLJobName
— (String
)List the candidates created for the job by providing the job's name.
StatusEquals
— (String
)List the candidates for the job and filter by status.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
CandidateNameEquals
— (String
)List the candidates for the job and filter by candidate name.
SortOrder
— (String
)The sort order for the results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
SortBy
— (String
)The parameter by which to sort the results. The default is
Possible values include:Descending
."CreationTime"
"Status"
"FinalObjectiveMetricValue"
MaxResults
— (Integer
)List the job's candidates up to a specified limit.
NextToken
— (String
)If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Candidates
— (Array<map>
)Summaries about the
AutoMLCandidates
.CandidateName
— required — (String
)The name of the candidate.
FinalAutoMLJobObjectiveMetric
— (map
)The best candidate result from an AutoML training job.
Type
— (String
)The type of metric with the best result.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.
Possible values include:"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
Value
— required — (Float
)The value of the metric with the best result.
StandardMetricName
— (String
)The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics.
Possible values include:"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
ObjectiveStatus
— required — (String
)The objective's status.
Possible values include:"Succeeded"
"Pending"
"Failed"
CandidateSteps
— required — (Array<map>
)Information about the candidate's steps.
CandidateStepType
— required — (String
)Whether the candidate is at the transform, training, or processing step.
Possible values include:"AWS::SageMaker::TrainingJob"
"AWS::SageMaker::TransformJob"
"AWS::SageMaker::ProcessingJob"
CandidateStepArn
— required — (String
)The ARN for the candidate's step.
CandidateStepName
— required — (String
)The name for the candidate's step.
CandidateStatus
— required — (String
)The candidate's status.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
InferenceContainers
— (Array<map>
)Information about the recommended inference container definitions.
Image
— required — (String
)The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.
ModelDataUrl
— required — (String
)The location of the model artifacts. For more information, see ContainerDefinition.
Environment
— (map<String>
)The environment variables to set in the container. For more information, see ContainerDefinition.
CreationTime
— required — (Date
)The creation time.
EndTime
— (Date
)The end time.
LastModifiedTime
— required — (Date
)The last modified time.
FailureReason
— (String
)The failure reason.
CandidateProperties
— (map
)The properties of an AutoML candidate job.
CandidateArtifactLocations
— (map
)The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
Explainability
— required — (String
)The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
ModelInsights
— (String
)The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
BacktestResults
— (String
)The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.
CandidateMetrics
— (Array<map>
)Information about the candidate metrics for an AutoML job.
MetricName
— (String
)The name of the metric.
Possible values include:"Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"BalancedAccuracy"
"R2"
"Recall"
"RecallMacro"
"Precision"
"PrecisionMacro"
"MAE"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
Value
— (Float
)The value of the metric.
Set
— (String
)The dataset split from which the AutoML job produced the metric.
Possible values include:"Train"
"Validation"
"Test"
StandardMetricName
— (String
)The name of the standard metric.
Note: For definitions of the standard metrics, seePossible values include:Autopilot candidate metrics
."Accuracy"
"MSE"
"F1"
"F1macro"
"AUC"
"RMSE"
"MAE"
"R2"
"BalancedAccuracy"
"Precision"
"PrecisionMacro"
"Recall"
"RecallMacro"
"LogLoss"
"InferenceLatency"
"MAPE"
"MASE"
"WAPE"
"AverageWeightedQuantileLoss"
"Rouge1"
"Rouge2"
"RougeL"
"RougeLSum"
"Perplexity"
"ValidationLoss"
"TrainingLoss"
InferenceContainerDefinitions
— (map<Array<map>>
)The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling
CreateAutoMLJobV2
) related to image or text classification problem types only.Image
— required — (String
)The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.
ModelDataUrl
— required — (String
)The location of the model artifacts. For more information, see ContainerDefinition.
Environment
— (map<String>
)The environment variables to set in the container. For more information, see ContainerDefinition.
NextToken
— (String
)If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
-
(AWS.Response)
—
Returns:
listClusterNodes(params = {}, callback) ⇒ AWS.Request
Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
Service Reference:
Examples:
Calling the listClusterNodes operation
var params = { ClusterName: 'STRING_VALUE', /* required */ CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, InstanceGroupNameContains: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: CREATION_TIME | NAME, SortOrder: Ascending | Descending }; sagemaker.listClusterNodes(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ClusterName
— (String
)The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which you want to retrieve the list of nodes.
CreationTimeAfter
— (Date
)A filter that returns nodes in a SageMaker HyperPod cluster created after the specified time. Timestamps are formatted according to the ISO 8601 standard.
Acceptable formats include:
-
YYYY-MM-DDThh:mm:ss.sssTZD
(UTC), for example,2014-10-01T20:30:00.000Z
-
YYYY-MM-DDThh:mm:ss.sssTZD
(with offset), for example,2014-10-01T12:30:00.000-08:00
-
YYYY-MM-DD
, for example,2014-10-01
-
Unix time in seconds, for example,
1412195400
. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.
For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.
-
CreationTimeBefore
— (Date
)A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for
CreationTimeAfter
. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.InstanceGroupNameContains
— (String
)A filter that returns the instance groups whose name contain a specified string.
MaxResults
— (Integer
)The maximum number of nodes to return in the response.
NextToken
— (String
)If the result of the previous
ListClusterNodes
request was truncated, the response includes aNextToken
. To retrieve the next set of cluster nodes, use the token in the next request.SortBy
— (String
)The field by which to sort results. The default value is
Possible values include:CREATION_TIME
."CREATION_TIME"
"NAME"
SortOrder
— (String
)The sort order for results. The default value is
Possible values include:Ascending
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NextToken
— (String
)The next token specified for listing instances in a SageMaker HyperPod cluster.
ClusterNodeSummaries
— (Array<map>
)The summaries of listed instances in a SageMaker HyperPod cluster
InstanceGroupName
— required — (String
)The name of the instance group in which the instance is.
InstanceId
— required — (String
)The ID of the instance.
InstanceType
— required — (String
)The type of the instance.
Possible values include:"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.c5n.large"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
LaunchTime
— required — (Date
)The time when the instance is launched.
InstanceStatus
— required — (map
)The status of the instance.
Status
— required — (String
)The status of an instance in a SageMaker HyperPod cluster.
Possible values include:"Running"
"Failure"
"Pending"
"ShuttingDown"
"SystemUpdating"
Message
— (String
)The message from an instance in a SageMaker HyperPod cluster.
-
(AWS.Response)
—
Returns:
listClusters(params = {}, callback) ⇒ AWS.Request
Retrieves the list of SageMaker HyperPod clusters.
Service Reference:
Examples:
Calling the listClusters operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: CREATION_TIME | NAME, SortOrder: Ascending | Descending }; sagemaker.listClusters(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)Set a start time for the time range during which you want to list SageMaker HyperPod clusters. Timestamps are formatted according to the ISO 8601 standard.
Acceptable formats include:
-
YYYY-MM-DDThh:mm:ss.sssTZD
(UTC), for example,2014-10-01T20:30:00.000Z
-
YYYY-MM-DDThh:mm:ss.sssTZD
(with offset), for example,2014-10-01T12:30:00.000-08:00
-
YYYY-MM-DD
, for example,2014-10-01
-
Unix time in seconds, for example,
1412195400
. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.
For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.
-
CreationTimeBefore
— (Date
)Set an end time for the time range during which you want to list SageMaker HyperPod clusters. A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for
CreationTimeAfter
. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.MaxResults
— (Integer
)Set the maximum number of SageMaker HyperPod clusters to list.
NameContains
— (String
)Set the maximum number of instances to print in the list.
NextToken
— (String
)Set the next token to retrieve the list of SageMaker HyperPod clusters.
SortBy
— (String
)The field by which to sort results. The default value is
Possible values include:CREATION_TIME
."CREATION_TIME"
"NAME"
SortOrder
— (String
)The sort order for results. The default value is
Possible values include:Ascending
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NextToken
— (String
)If the result of the previous
ListClusters
request was truncated, the response includes aNextToken
. To retrieve the next set of clusters, use the token in the next request.ClusterSummaries
— (Array<map>
)The summaries of listed SageMaker HyperPod clusters.
ClusterArn
— required — (String
)The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.
ClusterName
— required — (String
)The name of the SageMaker HyperPod cluster.
CreationTime
— required — (Date
)The time when the SageMaker HyperPod cluster is created.
ClusterStatus
— required — (String
)The status of the SageMaker HyperPod cluster.
Possible values include:"Creating"
"Deleting"
"Failed"
"InService"
"RollingBack"
"SystemUpdating"
"Updating"
-
(AWS.Response)
—
Returns:
listCodeRepositories(params = {}, callback) ⇒ AWS.Request
Gets a list of the Git repositories in your account.
Service Reference:
Examples:
Calling the listCodeRepositories operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | LastModifiedTime, SortOrder: Ascending | Descending }; sagemaker.listCodeRepositories(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only Git repositories that were created after the specified time.
CreationTimeBefore
— (Date
)A filter that returns only Git repositories that were created before the specified time.
LastModifiedTimeAfter
— (Date
)A filter that returns only Git repositories that were last modified after the specified time.
LastModifiedTimeBefore
— (Date
)A filter that returns only Git repositories that were last modified before the specified time.
MaxResults
— (Integer
)The maximum number of Git repositories to return in the response.
NameContains
— (String
)A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.
NextToken
— (String
)If the result of a
ListCodeRepositoriesOutput
request was truncated, the response includes aNextToken
. To get the next set of Git repositories, use the token in the next request.SortBy
— (String
)The field to sort results by. The default is
Possible values include:Name
."Name"
"CreationTime"
"LastModifiedTime"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CodeRepositorySummaryList
— (Array<map>
)Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository:
-
Name
-
Amazon Resource Name (ARN)
-
Creation time
-
Last modified time
-
Configuration information, including the URL location of the repository and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
CodeRepositoryName
— required — (String
)The name of the Git repository.
CodeRepositoryArn
— required — (String
)The Amazon Resource Name (ARN) of the Git repository.
CreationTime
— required — (Date
)The date and time that the Git repository was created.
LastModifiedTime
— required — (Date
)The date and time that the Git repository was last modified.
GitConfig
— (map
)Configuration details for the Git repository, including the URL where it is located and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
RepositoryUrl
— required — (String
)The URL where the Git repository is located.
Branch
— (String
)The default branch for the Git repository.
SecretArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of
AWSCURRENT
and must be in the following format:{"username": UserName, "password": Password}
-
NextToken
— (String
)If the result of a
ListCodeRepositoriesOutput
request was truncated, the response includes aNextToken
. To get the next set of Git repositories, use the token in the next request.
-
(AWS.Response)
—
Returns:
listCompilationJobs(params = {}, callback) ⇒ AWS.Request
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
Service Reference:
Examples:
Calling the listCompilationJobs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: INPROGRESS | COMPLETED | FAILED | STARTING | STOPPING | STOPPED }; sagemaker.listCompilationJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)If the result of the previous
ListCompilationJobs
request was truncated, the response includes aNextToken
. To retrieve the next set of model compilation jobs, use the token in the next request.MaxResults
— (Integer
)The maximum number of model compilation jobs to return in the response.
CreationTimeAfter
— (Date
)A filter that returns the model compilation jobs that were created after a specified time.
CreationTimeBefore
— (Date
)A filter that returns the model compilation jobs that were created before a specified time.
LastModifiedTimeAfter
— (Date
)A filter that returns the model compilation jobs that were modified after a specified time.
LastModifiedTimeBefore
— (Date
)A filter that returns the model compilation jobs that were modified before a specified time.
NameContains
— (String
)A filter that returns the model compilation jobs whose name contains a specified string.
StatusEquals
— (String
)A filter that retrieves model compilation jobs with a specific
Possible values include:CompilationJobStatus
status."INPROGRESS"
"COMPLETED"
"FAILED"
"STARTING"
"STOPPING"
"STOPPED"
SortBy
— (String
)The field by which to sort results. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
"Status"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CompilationJobSummaries
— (Array<map>
)An array of CompilationJobSummary objects, each describing a model compilation job.
CompilationJobName
— required — (String
)The name of the model compilation job that you want a summary for.
CompilationJobArn
— required — (String
)The Amazon Resource Name (ARN) of the model compilation job.
CreationTime
— required — (Date
)The time when the model compilation job was created.
CompilationStartTime
— (Date
)The time when the model compilation job started.
CompilationEndTime
— (Date
)The time when the model compilation job completed.
CompilationTargetDevice
— (String
)The type of device that the model will run on after the compilation job has completed.
Possible values include:"lambda"
"ml_m4"
"ml_m5"
"ml_m6g"
"ml_c4"
"ml_c5"
"ml_c6g"
"ml_p2"
"ml_p3"
"ml_g4dn"
"ml_inf1"
"ml_inf2"
"ml_trn1"
"ml_eia2"
"jetson_tx1"
"jetson_tx2"
"jetson_nano"
"jetson_xavier"
"rasp3b"
"rasp4b"
"imx8qm"
"deeplens"
"rk3399"
"rk3288"
"aisage"
"sbe_c"
"qcs605"
"qcs603"
"sitara_am57x"
"amba_cv2"
"amba_cv22"
"amba_cv25"
"x86_win32"
"x86_win64"
"coreml"
"jacinto_tda4vm"
"imx8mplus"
CompilationTargetPlatformOs
— (String
)The type of OS that the model will run on after the compilation job has completed.
Possible values include:"ANDROID"
"LINUX"
CompilationTargetPlatformArch
— (String
)The type of architecture that the model will run on after the compilation job has completed.
Possible values include:"X86_64"
"X86"
"ARM64"
"ARM_EABI"
"ARM_EABIHF"
CompilationTargetPlatformAccelerator
— (String
)The type of accelerator that the model will run on after the compilation job has completed.
Possible values include:"INTEL_GRAPHICS"
"MALI"
"NVIDIA"
"NNA"
LastModifiedTime
— (Date
)The time when the model compilation job was last modified.
CompilationJobStatus
— required — (String
)The status of the model compilation job.
Possible values include:"INPROGRESS"
"COMPLETED"
"FAILED"
"STARTING"
"STOPPING"
"STOPPED"
NextToken
— (String
)If the response is truncated, Amazon SageMaker returns this
NextToken
. To retrieve the next set of model compilation jobs, use this token in the next request.
-
(AWS.Response)
—
Returns:
listContexts(params = {}, callback) ⇒ AWS.Request
Lists the contexts in your account and their properties.
Service Reference:
Examples:
Calling the listContexts operation
var params = { ContextType: 'STRING_VALUE', CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending, SourceUri: 'STRING_VALUE' }; sagemaker.listContexts(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SourceUri
— (String
)A filter that returns only contexts with the specified source URI.
ContextType
— (String
)A filter that returns only contexts of the specified type.
CreatedAfter
— (Date
)A filter that returns only contexts created on or after the specified time.
CreatedBefore
— (Date
)A filter that returns only contexts created on or before the specified time.
SortBy
— (String
)The property used to sort results. The default value is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order. The default value is
Possible values include:Descending
."Ascending"
"Descending"
NextToken
— (String
)If the previous call to
ListContexts
didn't return the full set of contexts, the call returns a token for getting the next set of contexts.MaxResults
— (Integer
)The maximum number of contexts to return in the response. The default value is 10.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ContextSummaries
— (Array<map>
)A list of contexts and their properties.
ContextArn
— (String
)The Amazon Resource Name (ARN) of the context.
ContextName
— (String
)The name of the context.
Source
— (map
)The source of the context.
SourceUri
— required — (String
)The URI of the source.
SourceType
— (String
)The type of the source.
SourceId
— (String
)The ID of the source.
ContextType
— (String
)The type of the context.
CreationTime
— (Date
)When the context was created.
LastModifiedTime
— (Date
)When the context was last modified.
NextToken
— (String
)A token for getting the next set of contexts, if there are any.
-
(AWS.Response)
—
Returns:
listDataQualityJobDefinitions(params = {}, callback) ⇒ AWS.Request
Lists the data quality job definitions in your account.
Service Reference:
Examples:
Calling the listDataQualityJobDefinitions operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, EndpointName: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listDataQualityJobDefinitions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (String
)A filter that lists the data quality job definitions associated with the specified endpoint.
SortBy
— (String
)The field to sort results by. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)Whether to sort the results in
Possible values include:Ascending
orDescending
order. The default isDescending
."Ascending"
"Descending"
NextToken
— (String
)If the result of the previous
ListDataQualityJobDefinitions
request was truncated, the response includes aNextToken
. To retrieve the next set of transform jobs, use the token in the next request.>MaxResults
— (Integer
)The maximum number of data quality monitoring job definitions to return in the response.
NameContains
— (String
)A string in the data quality monitoring job definition name. This filter returns only data quality monitoring job definitions whose name contains the specified string.
CreationTimeBefore
— (Date
)A filter that returns only data quality monitoring job definitions created before the specified time.
CreationTimeAfter
— (Date
)A filter that returns only data quality monitoring job definitions created after the specified time.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionSummaries
— (Array<map>
)A list of data quality monitoring job definitions.
MonitoringJobDefinitionName
— required — (String
)The name of the monitoring job.
MonitoringJobDefinitionArn
— required — (String
)The Amazon Resource Name (ARN) of the monitoring job.
CreationTime
— required — (Date
)The time that the monitoring job was created.
EndpointName
— required — (String
)The name of the endpoint that the job monitors.
NextToken
— (String
)If the result of the previous
ListDataQualityJobDefinitions
request was truncated, the response includes aNextToken
. To retrieve the next set of data quality monitoring job definitions, use the token in the next request.
-
(AWS.Response)
—
Returns:
listDeviceFleets(params = {}, callback) ⇒ AWS.Request
Returns a list of devices in the fleet.
Service Reference:
Examples:
Calling the listDeviceFleets operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: NAME | CREATION_TIME | LAST_MODIFIED_TIME, SortOrder: Ascending | Descending }; sagemaker.listDeviceFleets(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)The response from the last list when returning a list large enough to need tokening.
MaxResults
— (Integer
)The maximum number of results to select.
CreationTimeAfter
— (Date
)Filter fleets where packaging job was created after specified time.
CreationTimeBefore
— (Date
)Filter fleets where the edge packaging job was created before specified time.
LastModifiedTimeAfter
— (Date
)Select fleets where the job was updated after X
LastModifiedTimeBefore
— (Date
)Select fleets where the job was updated before X
NameContains
— (String
)Filter for fleets containing this name in their fleet device name.
SortBy
— (String
)The column to sort by.
Possible values include:"NAME"
"CREATION_TIME"
"LAST_MODIFIED_TIME"
SortOrder
— (String
)What direction to sort in.
Possible values include:"Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DeviceFleetSummaries
— (Array<map>
)Summary of the device fleet.
DeviceFleetArn
— required — (String
)Amazon Resource Name (ARN) of the device fleet.
DeviceFleetName
— required — (String
)Name of the device fleet.
CreationTime
— (Date
)Timestamp of when the device fleet was created.
LastModifiedTime
— (Date
)Timestamp of when the device fleet was last updated.
NextToken
— (String
)The response from the last list when returning a list large enough to need tokening.
-
(AWS.Response)
—
Returns:
listDevices(params = {}, callback) ⇒ AWS.Request
A list of devices.
Service Reference:
Examples:
Calling the listDevices operation
var params = { DeviceFleetName: 'STRING_VALUE', LatestHeartbeatAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', ModelName: 'STRING_VALUE', NextToken: 'STRING_VALUE' }; sagemaker.listDevices(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)The response from the last list when returning a list large enough to need tokening.
MaxResults
— (Integer
)Maximum number of results to select.
LatestHeartbeatAfter
— (Date
)Select fleets where the job was updated after X
ModelName
— (String
)A filter that searches devices that contains this name in any of their models.
DeviceFleetName
— (String
)Filter for fleets containing this name in their device fleet name.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DeviceSummaries
— (Array<map>
)Summary of devices.
DeviceName
— required — (String
)The unique identifier of the device.
DeviceArn
— required — (String
)Amazon Resource Name (ARN) of the device.
Description
— (String
)A description of the device.
DeviceFleetName
— (String
)The name of the fleet the device belongs to.
IotThingName
— (String
)The Amazon Web Services Internet of Things (IoT) object thing name associated with the device..
RegistrationTime
— (Date
)The timestamp of the last registration or de-reregistration.
LatestHeartbeat
— (Date
)The last heartbeat received from the device.
Models
— (Array<map>
)Models on the device.
ModelName
— required — (String
)The name of the model.
ModelVersion
— required — (String
)The version model.
AgentVersion
— (String
)Edge Manager agent version.
NextToken
— (String
)The response from the last list when returning a list large enough to need tokening.
-
(AWS.Response)
—
Returns:
listDomains(params = {}, callback) ⇒ AWS.Request
Lists the domains.
Service Reference:
Examples:
Calling the listDomains operation
var params = { MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE' }; sagemaker.listDomains(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
MaxResults
— (Integer
)This parameter defines the maximum number of results that can be return in a single response. The
MaxResults
parameter is an upper bound, not a target. If there are more results available than the value specified, aNextToken
is provided in the response. TheNextToken
indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value forMaxResults
is 10.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Domains
— (Array<map>
)The list of domains.
DomainArn
— (String
)The domain's Amazon Resource Name (ARN).
DomainId
— (String
)The domain ID.
DomainName
— (String
)The domain name.
Status
— (String
)The status.
Possible values include:"Deleting"
"Failed"
"InService"
"Pending"
"Updating"
"Update_Failed"
"Delete_Failed"
CreationTime
— (Date
)The creation time.
LastModifiedTime
— (Date
)The last modified time.
Url
— (String
)The domain's URL.
NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
-
(AWS.Response)
—
Returns:
listEdgeDeploymentPlans(params = {}, callback) ⇒ AWS.Request
Lists all edge deployment plans.
Service Reference:
Examples:
Calling the listEdgeDeploymentPlans operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, DeviceFleetNameContains: 'STRING_VALUE', LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: NAME | DEVICE_FLEET_NAME | CREATION_TIME | LAST_MODIFIED_TIME, SortOrder: Ascending | Descending }; sagemaker.listEdgeDeploymentPlans(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)The response from the last list when returning a list large enough to need tokening.
MaxResults
— (Integer
)The maximum number of results to select (50 by default).
CreationTimeAfter
— (Date
)Selects edge deployment plans created after this time.
CreationTimeBefore
— (Date
)Selects edge deployment plans created before this time.
LastModifiedTimeAfter
— (Date
)Selects edge deployment plans that were last updated after this time.
LastModifiedTimeBefore
— (Date
)Selects edge deployment plans that were last updated before this time.
NameContains
— (String
)Selects edge deployment plans with names containing this name.
DeviceFleetNameContains
— (String
)Selects edge deployment plans with a device fleet name containing this name.
SortBy
— (String
)The column by which to sort the edge deployment plans. Can be one of
Possible values include:NAME
,DEVICEFLEETNAME
,CREATIONTIME
,LASTMODIFIEDTIME
."NAME"
"DEVICE_FLEET_NAME"
"CREATION_TIME"
"LAST_MODIFIED_TIME"
SortOrder
— (String
)The direction of the sorting (ascending or descending).
Possible values include:"Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EdgeDeploymentPlanSummaries
— (Array<map>
)List of summaries of edge deployment plans.
EdgeDeploymentPlanArn
— required — (String
)The ARN of the edge deployment plan.
EdgeDeploymentPlanName
— required — (String
)The name of the edge deployment plan.
DeviceFleetName
— required — (String
)The name of the device fleet used for the deployment.
EdgeDeploymentSuccess
— required — (Integer
)The number of edge devices with the successful deployment.
EdgeDeploymentPending
— required — (Integer
)The number of edge devices yet to pick up the deployment, or in progress.
EdgeDeploymentFailed
— required — (Integer
)The number of edge devices that failed the deployment.
CreationTime
— (Date
)The time when the edge deployment plan was created.
LastModifiedTime
— (Date
)The time when the edge deployment plan was last updated.
NextToken
— (String
)The token to use when calling the next page of results.
-
(AWS.Response)
—
Returns:
listEdgePackagingJobs(params = {}, callback) ⇒ AWS.Request
Returns a list of edge packaging jobs.
Service Reference:
Examples:
Calling the listEdgePackagingJobs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', ModelNameContains: 'STRING_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: NAME | MODEL_NAME | CREATION_TIME | LAST_MODIFIED_TIME | STATUS, SortOrder: Ascending | Descending, StatusEquals: STARTING | INPROGRESS | COMPLETED | FAILED | STOPPING | STOPPED }; sagemaker.listEdgePackagingJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)The response from the last list when returning a list large enough to need tokening.
MaxResults
— (Integer
)Maximum number of results to select.
CreationTimeAfter
— (Date
)Select jobs where the job was created after specified time.
CreationTimeBefore
— (Date
)Select jobs where the job was created before specified time.
LastModifiedTimeAfter
— (Date
)Select jobs where the job was updated after specified time.
LastModifiedTimeBefore
— (Date
)Select jobs where the job was updated before specified time.
NameContains
— (String
)Filter for jobs containing this name in their packaging job name.
ModelNameContains
— (String
)Filter for jobs where the model name contains this string.
StatusEquals
— (String
)The job status to filter for.
Possible values include:"STARTING"
"INPROGRESS"
"COMPLETED"
"FAILED"
"STOPPING"
"STOPPED"
SortBy
— (String
)Use to specify what column to sort by.
Possible values include:"NAME"
"MODEL_NAME"
"CREATION_TIME"
"LAST_MODIFIED_TIME"
"STATUS"
SortOrder
— (String
)What direction to sort by.
Possible values include:"Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EdgePackagingJobSummaries
— (Array<map>
)Summaries of edge packaging jobs.
EdgePackagingJobArn
— required — (String
)The Amazon Resource Name (ARN) of the edge packaging job.
EdgePackagingJobName
— required — (String
)The name of the edge packaging job.
EdgePackagingJobStatus
— required — (String
)The status of the edge packaging job.
Possible values include:"STARTING"
"INPROGRESS"
"COMPLETED"
"FAILED"
"STOPPING"
"STOPPED"
CompilationJobName
— (String
)The name of the SageMaker Neo compilation job.
ModelName
— (String
)The name of the model.
ModelVersion
— (String
)The version of the model.
CreationTime
— (Date
)The timestamp of when the job was created.
LastModifiedTime
— (Date
)The timestamp of when the edge packaging job was last updated.
NextToken
— (String
)Token to use when calling the next page of results.
-
(AWS.Response)
—
Returns:
listEndpointConfigs(params = {}, callback) ⇒ AWS.Request
Lists endpoint configurations.
Service Reference:
Examples:
Calling the listEndpointConfigs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listEndpointConfigs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SortBy
— (String
)The field to sort results by. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Descending
."Ascending"
"Descending"
NextToken
— (String
)If the result of the previous
ListEndpointConfig
request was truncated, the response includes aNextToken
. To retrieve the next set of endpoint configurations, use the token in the next request.MaxResults
— (Integer
)The maximum number of training jobs to return in the response.
NameContains
— (String
)A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.
CreationTimeBefore
— (Date
)A filter that returns only endpoint configurations created before the specified time (timestamp).
CreationTimeAfter
— (Date
)A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointConfigs
— (Array<map>
)An array of endpoint configurations.
EndpointConfigName
— required — (String
)The name of the endpoint configuration.
EndpointConfigArn
— required — (String
)The Amazon Resource Name (ARN) of the endpoint configuration.
CreationTime
— required — (Date
)A timestamp that shows when the endpoint configuration was created.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of endpoint configurations, use it in the subsequent request
-
(AWS.Response)
—
Returns:
listEndpoints(params = {}, callback) ⇒ AWS.Request
Lists endpoints.
Service Reference:
Examples:
Calling the listEndpoints operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: OutOfService | Creating | Updating | SystemUpdating | RollingBack | InService | Deleting | Failed | UpdateRollbackFailed }; sagemaker.listEndpoints(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SortBy
— (String
)Sorts the list of results. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
"Status"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Descending
."Ascending"
"Descending"
NextToken
— (String
)If the result of a
ListEndpoints
request was truncated, the response includes aNextToken
. To retrieve the next set of endpoints, use the token in the next request.MaxResults
— (Integer
)The maximum number of endpoints to return in the response. This value defaults to 10.
NameContains
— (String
)A string in endpoint names. This filter returns only endpoints whose name contains the specified string.
CreationTimeBefore
— (Date
)A filter that returns only endpoints that were created before the specified time (timestamp).
CreationTimeAfter
— (Date
)A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).
LastModifiedTimeBefore
— (Date
)A filter that returns only endpoints that were modified before the specified timestamp.
LastModifiedTimeAfter
— (Date
)A filter that returns only endpoints that were modified after the specified timestamp.
StatusEquals
— (String
)A filter that returns only endpoints with the specified status.
Possible values include:"OutOfService"
"Creating"
"Updating"
"SystemUpdating"
"RollingBack"
"InService"
"Deleting"
"Failed"
"UpdateRollbackFailed"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Endpoints
— (Array<map>
)An array or endpoint objects.
EndpointName
— required — (String
)The name of the endpoint.
EndpointArn
— required — (String
)The Amazon Resource Name (ARN) of the endpoint.
CreationTime
— required — (Date
)A timestamp that shows when the endpoint was created.
LastModifiedTime
— required — (Date
)A timestamp that shows when the endpoint was last modified.
EndpointStatus
— required — (String
)The status of the endpoint.
-
OutOfService
: Endpoint is not available to take incoming requests. -
Creating
: CreateEndpoint is executing. -
Updating
: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing. -
SystemUpdating
: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count. -
RollingBack
: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to anInService
status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly. -
InService
: Endpoint is available to process incoming requests. -
Deleting
: DeleteEndpoint is executing. -
Failed
: Endpoint could not be created, updated, or re-scaled. UseDescribeEndpointOutput$FailureReason
for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.
To get a list of endpoints with a specified status, use the
Possible values include:StatusEquals
filter with a call to ListEndpoints."OutOfService"
"Creating"
"Updating"
"SystemUpdating"
"RollingBack"
"InService"
"Deleting"
"Failed"
"UpdateRollbackFailed"
-
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listExperiments(params = {}, callback) ⇒ AWS.Request
Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
Service Reference:
Examples:
Calling the listExperiments operation
var params = { CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listExperiments(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreatedAfter
— (Date
)A filter that returns only experiments created after the specified time.
CreatedBefore
— (Date
)A filter that returns only experiments created before the specified time.
SortBy
— (String
)The property used to sort results. The default value is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order. The default value is
Possible values include:Descending
."Ascending"
"Descending"
NextToken
— (String
)If the previous call to
ListExperiments
didn't return the full set of experiments, the call returns a token for getting the next set of experiments.MaxResults
— (Integer
)The maximum number of experiments to return in the response. The default value is 10.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ExperimentSummaries
— (Array<map>
)A list of the summaries of your experiments.
ExperimentArn
— (String
)The Amazon Resource Name (ARN) of the experiment.
ExperimentName
— (String
)The name of the experiment.
DisplayName
— (String
)The name of the experiment as displayed. If
DisplayName
isn't specified,ExperimentName
is displayed.ExperimentSource
— (map
)The source of the experiment.
SourceArn
— required — (String
)The Amazon Resource Name (ARN) of the source.
SourceType
— (String
)The source type.
CreationTime
— (Date
)When the experiment was created.
LastModifiedTime
— (Date
)When the experiment was last modified.
NextToken
— (String
)A token for getting the next set of experiments, if there are any.
-
(AWS.Response)
—
Returns:
listFeatureGroups(params = {}, callback) ⇒ AWS.Request
List
FeatureGroup
s based on given filter and order.Service Reference:
Examples:
Calling the listFeatureGroups operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, FeatureGroupStatusEquals: Creating | Created | CreateFailed | Deleting | DeleteFailed, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', OfflineStoreStatusEquals: Active | Blocked | Disabled, SortBy: Name | FeatureGroupStatus | OfflineStoreStatus | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listFeatureGroups(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NameContains
— (String
)A string that partially matches one or more
FeatureGroup
s names. FiltersFeatureGroup
s by name.FeatureGroupStatusEquals
— (String
)A
Possible values include:FeatureGroup
status. Filters byFeatureGroup
status."Creating"
"Created"
"CreateFailed"
"Deleting"
"DeleteFailed"
OfflineStoreStatusEquals
— (String
)An
Possible values include:OfflineStore
status. Filters byOfflineStore
status."Active"
"Blocked"
"Disabled"
CreationTimeAfter
— (Date
)Use this parameter to search for
FeatureGroups
s created after a specific date and time.CreationTimeBefore
— (Date
)Use this parameter to search for
FeatureGroups
s created before a specific date and time.SortOrder
— (String
)The order in which feature groups are listed.
Possible values include:"Ascending"
"Descending"
SortBy
— (String
)The value on which the feature group list is sorted.
Possible values include:"Name"
"FeatureGroupStatus"
"OfflineStoreStatus"
"CreationTime"
MaxResults
— (Integer
)The maximum number of results returned by
ListFeatureGroups
.NextToken
— (String
)A token to resume pagination of
ListFeatureGroups
results.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:FeatureGroupSummaries
— (Array<map>
)A summary of feature groups.
FeatureGroupName
— required — (String
)The name of
FeatureGroup
.FeatureGroupArn
— required — (String
)Unique identifier for the
FeatureGroup
.CreationTime
— required — (Date
)A timestamp indicating the time of creation time of the
FeatureGroup
.FeatureGroupStatus
— (String
)The status of a FeatureGroup. The status can be any of the following:
Possible values include:Creating
,Created
,CreateFail
,Deleting
orDetailFail
."Creating"
"Created"
"CreateFailed"
"Deleting"
"DeleteFailed"
OfflineStoreStatus
— (map
)Notifies you if replicating data into the
OfflineStore
has failed. Returns either:Active
orBlocked
.Status
— required — (String
)An
Possible values include:OfflineStore
status."Active"
"Blocked"
"Disabled"
BlockedReason
— (String
)The justification for why the OfflineStoreStatus is Blocked (if applicable).
NextToken
— (String
)A token to resume pagination of
ListFeatureGroups
results.
-
(AWS.Response)
—
Returns:
listFlowDefinitions(params = {}, callback) ⇒ AWS.Request
Returns information about the flow definitions in your account.
Service Reference:
Examples:
Calling the listFlowDefinitions operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortOrder: Ascending | Descending }; sagemaker.listFlowDefinitions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.
CreationTimeBefore
— (Date
)A filter that returns only flow definitions that were created before the specified timestamp.
SortOrder
— (String
)An optional value that specifies whether you want the results sorted in
Possible values include:Ascending
orDescending
order."Ascending"
"Descending"
NextToken
— (String
)A token to resume pagination.
MaxResults
— (Integer
)The total number of items to return. If the total number of available items is more than the value specified in
MaxResults
, then aNextToken
will be provided in the output that you can use to resume pagination.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:FlowDefinitionSummaries
— (Array<map>
)An array of objects describing the flow definitions.
FlowDefinitionName
— required — (String
)The name of the flow definition.
FlowDefinitionArn
— required — (String
)The Amazon Resource Name (ARN) of the flow definition.
FlowDefinitionStatus
— required — (String
)The status of the flow definition. Valid values:
Possible values include:"Initializing"
"Active"
"Failed"
"Deleting"
CreationTime
— required — (Date
)The timestamp when SageMaker created the flow definition.
FailureReason
— (String
)The reason why the flow definition creation failed. A failure reason is returned only when the flow definition status is
Failed
.
NextToken
— (String
)A token to resume pagination.
-
(AWS.Response)
—
Returns:
listHubContents(params = {}, callback) ⇒ AWS.Request
List the contents of a hub.
Service Reference:
Examples:
Calling the listHubContents operation
var params = { HubContentType: Model | Notebook | ModelReference, /* required */ HubName: 'STRING_VALUE', /* required */ CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', MaxSchemaVersion: 'STRING_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: HubContentName | CreationTime | HubContentStatus, SortOrder: Ascending | Descending }; sagemaker.listHubContents(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubName
— (String
)The name of the hub to list the contents of.
HubContentType
— (String
)The type of hub content to list.
Possible values include:"Model"
"Notebook"
"ModelReference"
NameContains
— (String
)Only list hub content if the name contains the specified string.
MaxSchemaVersion
— (String
)The upper bound of the hub content schema verion.
CreationTimeBefore
— (Date
)Only list hub content that was created before the time specified.
CreationTimeAfter
— (Date
)Only list hub content that was created after the time specified.
SortBy
— (String
)Sort hub content versions by either name or creation time.
Possible values include:"HubContentName"
"CreationTime"
"HubContentStatus"
SortOrder
— (String
)Sort hubs by ascending or descending order.
Possible values include:"Ascending"
"Descending"
MaxResults
— (Integer
)The maximum amount of hub content to list.
NextToken
— (String
)If the response to a previous
ListHubContents
request was truncated, the response includes aNextToken
. To retrieve the next set of hub content, use the token in the next request.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HubContentSummaries
— (Array<map>
)The summaries of the listed hub content.
HubContentName
— required — (String
)The name of the hub content.
HubContentArn
— required — (String
)The Amazon Resource Name (ARN) of the hub content.
SageMakerPublicHubContentArn
— (String
)The ARN of the public hub content.
HubContentVersion
— required — (String
)The version of the hub content.
HubContentType
— required — (String
)The type of hub content.
Possible values include:"Model"
"Notebook"
"ModelReference"
DocumentSchemaVersion
— required — (String
)The version of the hub content document schema.
HubContentDisplayName
— (String
)The display name of the hub content.
HubContentDescription
— (String
)A description of the hub content.
SupportStatus
— (String
)The support status of the hub content.
Possible values include:"Supported"
"Deprecated"
HubContentSearchKeywords
— (Array<String>
)The searchable keywords for the hub content.
HubContentStatus
— required — (String
)The status of the hub content.
Possible values include:"Available"
"Importing"
"Deleting"
"ImportFailed"
"DeleteFailed"
CreationTime
— required — (Date
)The date and time that the hub content was created.
OriginalCreationTime
— (Date
)The date and time when the hub content was originally created, before any updates or revisions.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of hub content, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listHubContentVersions(params = {}, callback) ⇒ AWS.Request
List hub content versions.
Service Reference:
Examples:
Calling the listHubContentVersions operation
var params = { HubContentName: 'STRING_VALUE', /* required */ HubContentType: Model | Notebook | ModelReference, /* required */ HubName: 'STRING_VALUE', /* required */ CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', MaxSchemaVersion: 'STRING_VALUE', MinVersion: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: HubContentName | CreationTime | HubContentStatus, SortOrder: Ascending | Descending }; sagemaker.listHubContentVersions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubName
— (String
)The name of the hub to list the content versions of.
HubContentType
— (String
)The type of hub content to list versions of.
Possible values include:"Model"
"Notebook"
"ModelReference"
HubContentName
— (String
)The name of the hub content.
MinVersion
— (String
)The lower bound of the hub content versions to list.
MaxSchemaVersion
— (String
)The upper bound of the hub content schema version.
CreationTimeBefore
— (Date
)Only list hub content versions that were created before the time specified.
CreationTimeAfter
— (Date
)Only list hub content versions that were created after the time specified.
SortBy
— (String
)Sort hub content versions by either name or creation time.
Possible values include:"HubContentName"
"CreationTime"
"HubContentStatus"
SortOrder
— (String
)Sort hub content versions by ascending or descending order.
Possible values include:"Ascending"
"Descending"
MaxResults
— (Integer
)The maximum number of hub content versions to list.
NextToken
— (String
)If the response to a previous
ListHubContentVersions
request was truncated, the response includes aNextToken
. To retrieve the next set of hub content versions, use the token in the next request.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HubContentSummaries
— (Array<map>
)The summaries of the listed hub content versions.
HubContentName
— required — (String
)The name of the hub content.
HubContentArn
— required — (String
)The Amazon Resource Name (ARN) of the hub content.
SageMakerPublicHubContentArn
— (String
)The ARN of the public hub content.
HubContentVersion
— required — (String
)The version of the hub content.
HubContentType
— required — (String
)The type of hub content.
Possible values include:"Model"
"Notebook"
"ModelReference"
DocumentSchemaVersion
— required — (String
)The version of the hub content document schema.
HubContentDisplayName
— (String
)The display name of the hub content.
HubContentDescription
— (String
)A description of the hub content.
SupportStatus
— (String
)The support status of the hub content.
Possible values include:"Supported"
"Deprecated"
HubContentSearchKeywords
— (Array<String>
)The searchable keywords for the hub content.
HubContentStatus
— required — (String
)The status of the hub content.
Possible values include:"Available"
"Importing"
"Deleting"
"ImportFailed"
"DeleteFailed"
CreationTime
— required — (Date
)The date and time that the hub content was created.
OriginalCreationTime
— (Date
)The date and time when the hub content was originally created, before any updates or revisions.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of hub content versions, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listHubs(params = {}, callback) ⇒ AWS.Request
List all existing hubs.
Service Reference:
Examples:
Calling the listHubs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: HubName | CreationTime | HubStatus | AccountIdOwner, SortOrder: Ascending | Descending }; sagemaker.listHubs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NameContains
— (String
)Only list hubs with names that contain the specified string.
CreationTimeBefore
— (Date
)Only list hubs that were created before the time specified.
CreationTimeAfter
— (Date
)Only list hubs that were created after the time specified.
LastModifiedTimeBefore
— (Date
)Only list hubs that were last modified before the time specified.
LastModifiedTimeAfter
— (Date
)Only list hubs that were last modified after the time specified.
SortBy
— (String
)Sort hubs by either name or creation time.
Possible values include:"HubName"
"CreationTime"
"HubStatus"
"AccountIdOwner"
SortOrder
— (String
)Sort hubs by ascending or descending order.
Possible values include:"Ascending"
"Descending"
MaxResults
— (Integer
)The maximum number of hubs to list.
NextToken
— (String
)If the response to a previous
ListHubs
request was truncated, the response includes aNextToken
. To retrieve the next set of hubs, use the token in the next request.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HubSummaries
— (Array<map>
)The summaries of the listed hubs.
HubName
— required — (String
)The name of the hub.
HubArn
— required — (String
)The Amazon Resource Name (ARN) of the hub.
HubDisplayName
— (String
)The display name of the hub.
HubDescription
— (String
)A description of the hub.
HubSearchKeywords
— (Array<String>
)The searchable keywords for the hub.
HubStatus
— required — (String
)The status of the hub.
Possible values include:"InService"
"Creating"
"Updating"
"Deleting"
"CreateFailed"
"UpdateFailed"
"DeleteFailed"
CreationTime
— required — (Date
)The date and time that the hub was created.
LastModifiedTime
— required — (Date
)The date and time that the hub was last modified.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of hubs, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listHumanTaskUis(params = {}, callback) ⇒ AWS.Request
Returns information about the human task user interfaces in your account.
Service Reference:
Examples:
Calling the listHumanTaskUis operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortOrder: Ascending | Descending }; sagemaker.listHumanTaskUis(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.
CreationTimeBefore
— (Date
)A filter that returns only human task user interfaces that were created before the specified timestamp.
SortOrder
— (String
)An optional value that specifies whether you want the results sorted in
Possible values include:Ascending
orDescending
order."Ascending"
"Descending"
NextToken
— (String
)A token to resume pagination.
MaxResults
— (Integer
)The total number of items to return. If the total number of available items is more than the value specified in
MaxResults
, then aNextToken
will be provided in the output that you can use to resume pagination.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HumanTaskUiSummaries
— (Array<map>
)An array of objects describing the human task user interfaces.
HumanTaskUiName
— required — (String
)The name of the human task user interface.
HumanTaskUiArn
— required — (String
)The Amazon Resource Name (ARN) of the human task user interface.
CreationTime
— required — (Date
)A timestamp when SageMaker created the human task user interface.
NextToken
— (String
)A token to resume pagination.
-
(AWS.Response)
—
Returns:
listHyperParameterTuningJobs(params = {}, callback) ⇒ AWS.Request
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
Service Reference:
Examples:
Calling the listHyperParameterTuningJobs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | Status | CreationTime, SortOrder: Ascending | Descending, StatusEquals: Completed | InProgress | Failed | Stopped | Stopping | Deleting | DeleteFailed }; sagemaker.listHyperParameterTuningJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)If the result of the previous
ListHyperParameterTuningJobs
request was truncated, the response includes aNextToken
. To retrieve the next set of tuning jobs, use the token in the next request.MaxResults
— (Integer
)The maximum number of tuning jobs to return. The default value is 10.
SortBy
— (String
)The field to sort results by. The default is
Possible values include:Name
."Name"
"Status"
"CreationTime"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
NameContains
— (String
)A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.
CreationTimeAfter
— (Date
)A filter that returns only tuning jobs that were created after the specified time.
CreationTimeBefore
— (Date
)A filter that returns only tuning jobs that were created before the specified time.
LastModifiedTimeAfter
— (Date
)A filter that returns only tuning jobs that were modified after the specified time.
LastModifiedTimeBefore
— (Date
)A filter that returns only tuning jobs that were modified before the specified time.
StatusEquals
— (String
)A filter that returns only tuning jobs with the specified status.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
"Deleting"
"DeleteFailed"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HyperParameterTuningJobSummaries
— (Array<map>
)A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the
ListHyperParameterTuningJobs
request returned.HyperParameterTuningJobName
— required — (String
)The name of the tuning job.
HyperParameterTuningJobArn
— required — (String
)The Amazon Resource Name (ARN) of the tuning job.
HyperParameterTuningJobStatus
— required — (String
)The status of the tuning job.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
"Deleting"
"DeleteFailed"
Strategy
— required — (String
)Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to evaluate at each iteration.
Possible values include:"Bayesian"
"Random"
"Hyperband"
"Grid"
CreationTime
— required — (Date
)The date and time that the tuning job was created.
HyperParameterTuningEndTime
— (Date
)The date and time that the tuning job ended.
LastModifiedTime
— (Date
)The date and time that the tuning job was modified.
TrainingJobStatusCounters
— required — (map
)The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.
Completed
— (Integer
)The number of completed training jobs launched by the hyperparameter tuning job.
InProgress
— (Integer
)The number of in-progress training jobs launched by a hyperparameter tuning job.
RetryableError
— (Integer
)The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
NonRetryableError
— (Integer
)The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Stopped
— (Integer
)The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
ObjectiveStatusCounters
— required — (map
)The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.
Succeeded
— (Integer
)The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending
— (Integer
)The number of training jobs that are in progress and pending evaluation of their final objective metric.
Failed
— (Integer
)The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
ResourceLimits
— (map
)The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.
MaxNumberOfTrainingJobs
— (Integer
)The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs
— required — (Integer
)The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds
— (Integer
)The maximum time in seconds that a hyperparameter tuning job can run.
NextToken
— (String
)If the result of this
ListHyperParameterTuningJobs
request was truncated, the response includes aNextToken
. To retrieve the next set of tuning jobs, use the token in the next request.
-
(AWS.Response)
—
Returns:
listImages(params = {}, callback) ⇒ AWS.Request
Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.
Service Reference:
Examples:
Calling the listImages operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: CREATION_TIME | LAST_MODIFIED_TIME | IMAGE_NAME, SortOrder: ASCENDING | DESCENDING }; sagemaker.listImages(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only images created on or after the specified time.
CreationTimeBefore
— (Date
)A filter that returns only images created on or before the specified time.
LastModifiedTimeAfter
— (Date
)A filter that returns only images modified on or after the specified time.
LastModifiedTimeBefore
— (Date
)A filter that returns only images modified on or before the specified time.
MaxResults
— (Integer
)The maximum number of images to return in the response. The default value is 10.
NameContains
— (String
)A filter that returns only images whose name contains the specified string.
NextToken
— (String
)If the previous call to
ListImages
didn't return the full set of images, the call returns a token for getting the next set of images.SortBy
— (String
)The property used to sort results. The default value is
Possible values include:CREATION_TIME
."CREATION_TIME"
"LAST_MODIFIED_TIME"
"IMAGE_NAME"
SortOrder
— (String
)The sort order. The default value is
Possible values include:DESCENDING
."ASCENDING"
"DESCENDING"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Images
— (Array<map>
)A list of images and their properties.
CreationTime
— required — (Date
)When the image was created.
Description
— (String
)The description of the image.
DisplayName
— (String
)The name of the image as displayed.
FailureReason
— (String
)When a create, update, or delete operation fails, the reason for the failure.
ImageArn
— required — (String
)The ARN of the image.
ImageName
— required — (String
)The name of the image.
ImageStatus
— required — (String
)The status of the image.
Possible values include:"CREATING"
"CREATED"
"CREATE_FAILED"
"UPDATING"
"UPDATE_FAILED"
"DELETING"
"DELETE_FAILED"
LastModifiedTime
— required — (Date
)When the image was last modified.
NextToken
— (String
)A token for getting the next set of images, if there are any.
-
(AWS.Response)
—
Returns:
listImageVersions(params = {}, callback) ⇒ AWS.Request
Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
Service Reference:
Examples:
Calling the listImageVersions operation
var params = { ImageName: 'STRING_VALUE', /* required */ CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: CREATION_TIME | LAST_MODIFIED_TIME | VERSION, SortOrder: ASCENDING | DESCENDING }; sagemaker.listImageVersions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only versions created on or after the specified time.
CreationTimeBefore
— (Date
)A filter that returns only versions created on or before the specified time.
ImageName
— (String
)The name of the image to list the versions of.
LastModifiedTimeAfter
— (Date
)A filter that returns only versions modified on or after the specified time.
LastModifiedTimeBefore
— (Date
)A filter that returns only versions modified on or before the specified time.
MaxResults
— (Integer
)The maximum number of versions to return in the response. The default value is 10.
NextToken
— (String
)If the previous call to
ListImageVersions
didn't return the full set of versions, the call returns a token for getting the next set of versions.SortBy
— (String
)The property used to sort results. The default value is
Possible values include:CREATION_TIME
."CREATION_TIME"
"LAST_MODIFIED_TIME"
"VERSION"
SortOrder
— (String
)The sort order. The default value is
Possible values include:DESCENDING
."ASCENDING"
"DESCENDING"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ImageVersions
— (Array<map>
)A list of versions and their properties.
CreationTime
— required — (Date
)When the version was created.
FailureReason
— (String
)When a create or delete operation fails, the reason for the failure.
ImageArn
— required — (String
)The ARN of the image the version is based on.
ImageVersionArn
— required — (String
)The ARN of the version.
ImageVersionStatus
— required — (String
)The status of the version.
Possible values include:"CREATING"
"CREATED"
"CREATE_FAILED"
"DELETING"
"DELETE_FAILED"
LastModifiedTime
— required — (Date
)When the version was last modified.
Version
— required — (Integer
)The version number.
NextToken
— (String
)A token for getting the next set of versions, if there are any.
-
(AWS.Response)
—
Returns:
listInferenceComponents(params = {}, callback) ⇒ AWS.Request
Lists the inference components in your account and their properties.
Service Reference:
Examples:
Calling the listInferenceComponents operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, EndpointNameEquals: 'STRING_VALUE', LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: InService | Creating | Updating | Failed | Deleting, VariantNameEquals: 'STRING_VALUE' }; sagemaker.listInferenceComponents(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SortBy
— (String
)The field by which to sort the inference components in the response. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
"Status"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Descending
."Ascending"
"Descending"
NextToken
— (String
)A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.
MaxResults
— (Integer
)The maximum number of inference components to return in the response. This value defaults to 10.
NameContains
— (String
)Filters the results to only those inference components with a name that contains the specified string.
CreationTimeBefore
— (Date
)Filters the results to only those inference components that were created before the specified time.
CreationTimeAfter
— (Date
)Filters the results to only those inference components that were created after the specified time.
LastModifiedTimeBefore
— (Date
)Filters the results to only those inference components that were updated before the specified time.
LastModifiedTimeAfter
— (Date
)Filters the results to only those inference components that were updated after the specified time.
StatusEquals
— (String
)Filters the results to only those inference components with the specified status.
Possible values include:"InService"
"Creating"
"Updating"
"Failed"
"Deleting"
EndpointNameEquals
— (String
)An endpoint name to filter the listed inference components. The response includes only those inference components that are hosted at the specified endpoint.
VariantNameEquals
— (String
)A production variant name to filter the listed inference components. The response includes only those inference components that are hosted at the specified variant.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceComponents
— (Array<map>
)A list of inference components and their properties that matches any of the filters you specified in the request.
CreationTime
— required — (Date
)The time when the inference component was created.
InferenceComponentArn
— required — (String
)The Amazon Resource Name (ARN) of the inference component.
InferenceComponentName
— required — (String
)The name of the inference component.
EndpointArn
— required — (String
)The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
EndpointName
— required — (String
)The name of the endpoint that hosts the inference component.
VariantName
— required — (String
)The name of the production variant that hosts the inference component.
InferenceComponentStatus
— (String
)The status of the inference component.
Possible values include:"InService"
"Creating"
"Updating"
"Failed"
"Deleting"
LastModifiedTime
— required — (Date
)The time when the inference component was last updated.
NextToken
— (String
)The token to use in a subsequent request to get the next set of results following a truncated response.
-
(AWS.Response)
—
Returns:
listInferenceExperiments(params = {}, callback) ⇒ AWS.Request
Returns the list of all inference experiments.
Service Reference:
Examples:
Calling the listInferenceExperiments operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: Creating | Created | Updating | Running | Starting | Stopping | Completed | Cancelled, Type: ShadowMode }; sagemaker.listInferenceExperiments(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NameContains
— (String
)Selects inference experiments whose names contain this name.
Type
— (String
)Selects inference experiments of this type. For the possible types of inference experiments, see CreateInferenceExperiment.
Possible values include:"ShadowMode"
StatusEquals
— (String
)Selects inference experiments which are in this status. For the possible statuses, see DescribeInferenceExperiment.
Possible values include:"Creating"
"Created"
"Updating"
"Running"
"Starting"
"Stopping"
"Completed"
"Cancelled"
CreationTimeAfter
— (Date
)Selects inference experiments which were created after this timestamp.
CreationTimeBefore
— (Date
)Selects inference experiments which were created before this timestamp.
LastModifiedTimeAfter
— (Date
)Selects inference experiments which were last modified after this timestamp.
LastModifiedTimeBefore
— (Date
)Selects inference experiments which were last modified before this timestamp.
SortBy
— (String
)The column by which to sort the listed inference experiments.
Possible values include:"Name"
"CreationTime"
"Status"
SortOrder
— (String
)The direction of sorting (ascending or descending).
Possible values include:"Ascending"
"Descending"
NextToken
— (String
)The response from the last list when returning a list large enough to need tokening.
MaxResults
— (Integer
)The maximum number of results to select.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceExperiments
— (Array<map>
)List of inference experiments.
Name
— required — (String
)The name of the inference experiment.
Type
— required — (String
)The type of the inference experiment.
Possible values include:"ShadowMode"
Schedule
— (map
)The duration for which the inference experiment ran or will run.
The maximum duration that you can set for an inference experiment is 30 days.
StartTime
— (Date
)The timestamp at which the inference experiment started or will start.
EndTime
— (Date
)The timestamp at which the inference experiment ended or will end.
Status
— required — (String
)The status of the inference experiment.
Possible values include:"Creating"
"Created"
"Updating"
"Running"
"Starting"
"Stopping"
"Completed"
"Cancelled"
StatusReason
— (String
)The error message for the inference experiment status result.
Description
— (String
)The description of the inference experiment.
CreationTime
— required — (Date
)The timestamp at which the inference experiment was created.
CompletionTime
— (Date
)The timestamp at which the inference experiment was completed.
LastModifiedTime
— required — (Date
)The timestamp when you last modified the inference experiment.
RoleArn
— (String
)The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
NextToken
— (String
)The token to use when calling the next page of results.
-
(AWS.Response)
—
Returns:
listInferenceRecommendationsJobs(params = {}, callback) ⇒ AWS.Request
Lists recommendation jobs that satisfy various filters.
Service Reference:
Examples:
Calling the listInferenceRecommendationsJobs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', ModelNameEquals: 'STRING_VALUE', ModelPackageVersionArnEquals: 'STRING_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: PENDING | IN_PROGRESS | COMPLETED | FAILED | STOPPING | STOPPED | DELETING | DELETED }; sagemaker.listInferenceRecommendationsJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only jobs created after the specified time (timestamp).
CreationTimeBefore
— (Date
)A filter that returns only jobs created before the specified time (timestamp).
LastModifiedTimeAfter
— (Date
)A filter that returns only jobs that were last modified after the specified time (timestamp).
LastModifiedTimeBefore
— (Date
)A filter that returns only jobs that were last modified before the specified time (timestamp).
NameContains
— (String
)A string in the job name. This filter returns only recommendations whose name contains the specified string.
StatusEquals
— (String
)A filter that retrieves only inference recommendations jobs with a specific status.
Possible values include:"PENDING"
"IN_PROGRESS"
"COMPLETED"
"FAILED"
"STOPPING"
"STOPPED"
"DELETING"
"DELETED"
SortBy
— (String
)The parameter by which to sort the results.
Possible values include:"Name"
"CreationTime"
"Status"
SortOrder
— (String
)The sort order for the results.
Possible values include:"Ascending"
"Descending"
NextToken
— (String
)If the response to a previous
ListInferenceRecommendationsJobsRequest
request was truncated, the response includes aNextToken
. To retrieve the next set of recommendations, use the token in the next request.MaxResults
— (Integer
)The maximum number of recommendations to return in the response.
ModelNameEquals
— (String
)A filter that returns only jobs that were created for this model.
ModelPackageVersionArnEquals
— (String
)A filter that returns only jobs that were created for this versioned model package.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceRecommendationsJobs
— (Array<map>
)The recommendations created from the Amazon SageMaker Inference Recommender job.
JobName
— required — (String
)The name of the job.
JobDescription
— required — (String
)The job description.
JobType
— required — (String
)The recommendation job type.
Possible values include:"Default"
"Advanced"
JobArn
— required — (String
)The Amazon Resource Name (ARN) of the recommendation job.
Status
— required — (String
)The status of the job.
Possible values include:"PENDING"
"IN_PROGRESS"
"COMPLETED"
"FAILED"
"STOPPING"
"STOPPED"
"DELETING"
"DELETED"
CreationTime
— required — (Date
)A timestamp that shows when the job was created.
CompletionTime
— (Date
)A timestamp that shows when the job completed.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
LastModifiedTime
— required — (Date
)A timestamp that shows when the job was last modified.
FailureReason
— (String
)If the job fails, provides information why the job failed.
ModelName
— (String
)The name of the created model.
SamplePayloadUrl
— (String
)The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
ModelPackageVersionArn
— (String
)The Amazon Resource Name (ARN) of a versioned model package.
NextToken
— (String
)A token for getting the next set of recommendations, if there are any.
-
(AWS.Response)
—
Returns:
listInferenceRecommendationsJobSteps(params = {}, callback) ⇒ AWS.Request
Returns a list of the subtasks for an Inference Recommender job.
The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
Service Reference:
Examples:
Calling the listInferenceRecommendationsJobSteps operation
var params = { JobName: 'STRING_VALUE', /* required */ MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', Status: PENDING | IN_PROGRESS | COMPLETED | FAILED | STOPPING | STOPPED | DELETING | DELETED, StepType: BENCHMARK }; sagemaker.listInferenceRecommendationsJobSteps(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobName
— (String
)The name for the Inference Recommender job.
Status
— (String
)A filter to return benchmarks of a specified status. If this field is left empty, then all benchmarks are returned.
Possible values include:"PENDING"
"IN_PROGRESS"
"COMPLETED"
"FAILED"
"STOPPING"
"STOPPED"
"DELETING"
"DELETED"
StepType
— (String
)A filter to return details about the specified type of subtask.
BENCHMARK
: Evaluate the performance of your model on different instance types."BENCHMARK"
MaxResults
— (Integer
)The maximum number of results to return.
NextToken
— (String
)A token that you can specify to return more results from the list. Specify this field if you have a token that was returned from a previous request.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Steps
— (Array<map>
)A list of all subtask details in Inference Recommender.
StepType
— required — (String
)The type of the subtask.
BENCHMARK
: Evaluate the performance of your model on different instance types."BENCHMARK"
JobName
— required — (String
)The name of the Inference Recommender job.
Status
— required — (String
)The current status of the benchmark.
Possible values include:"PENDING"
"IN_PROGRESS"
"COMPLETED"
"FAILED"
"STOPPING"
"STOPPED"
"DELETING"
"DELETED"
InferenceBenchmark
— (map
)The details for a specific benchmark.
Metrics
— (map
)The metrics of recommendations.
CostPerHour
— (Float
)Defines the cost per hour for the instance.
CostPerInference
— (Float
)Defines the cost per inference for the instance .
MaxInvocations
— (Integer
)The expected maximum number of requests per minute for the instance.
ModelLatency
— (Integer
)The expected model latency at maximum invocation per minute for the instance.
CpuUtilization
— (Float
)The expected CPU utilization at maximum invocations per minute for the instance.
NaN
indicates that the value is not available.MemoryUtilization
— (Float
)The expected memory utilization at maximum invocations per minute for the instance.
NaN
indicates that the value is not available.ModelSetupTime
— (Integer
)The time it takes to launch new compute resources for a serverless endpoint. The time can vary depending on the model size, how long it takes to download the model, and the start-up time of the container.
NaN
indicates that the value is not available.
EndpointMetrics
— (map
)The metrics for an existing endpoint compared in an Inference Recommender job.
MaxInvocations
— required — (Integer
)The expected maximum number of requests per minute for the instance.
ModelLatency
— required — (Integer
)The expected model latency at maximum invocations per minute for the instance.
EndpointConfiguration
— (map
)The endpoint configuration made by Inference Recommender during a recommendation job.
EndpointName
— required — (String
)The name of the endpoint made during a recommendation job.
VariantName
— required — (String
)The name of the production variant (deployed model) made during a recommendation job.
InstanceType
— (String
)The instance type recommended by Amazon SageMaker Inference Recommender.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
InitialInstanceCount
— (Integer
)The number of instances recommended to launch initially.
ServerlessConfig
— (map
)Specifies the serverless configuration for an endpoint variant.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ModelConfiguration
— required — (map
)Defines the model configuration. Includes the specification name and environment parameters.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
EnvironmentParameters
— (Array<map>
)Defines the environment parameters that includes key, value types, and values.
Key
— required — (String
)The environment key suggested by the Amazon SageMaker Inference Recommender.
ValueType
— required — (String
)The value type suggested by the Amazon SageMaker Inference Recommender.
Value
— required — (String
)The value suggested by the Amazon SageMaker Inference Recommender.
CompilationJobName
— (String
)The name of the compilation job used to create the recommended model artifacts.
FailureReason
— (String
)The reason why a benchmark failed.
InvocationEndTime
— (Date
)A timestamp that shows when the benchmark completed.
InvocationStartTime
— (Date
)A timestamp that shows when the benchmark started.
NextToken
— (String
)A token that you can specify in your next request to return more results from the list.
-
(AWS.Response)
—
Returns:
listLabelingJobs(params = {}, callback) ⇒ AWS.Request
Gets a list of labeling jobs.
Service Reference:
Examples:
Calling the listLabelingJobs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: Initializing | InProgress | Completed | Failed | Stopping | Stopped }; sagemaker.listLabelingJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only labeling jobs created after the specified time (timestamp).
CreationTimeBefore
— (Date
)A filter that returns only labeling jobs created before the specified time (timestamp).
LastModifiedTimeAfter
— (Date
)A filter that returns only labeling jobs modified after the specified time (timestamp).
LastModifiedTimeBefore
— (Date
)A filter that returns only labeling jobs modified before the specified time (timestamp).
MaxResults
— (Integer
)The maximum number of labeling jobs to return in each page of the response.
NextToken
— (String
)If the result of the previous
ListLabelingJobs
request was truncated, the response includes aNextToken
. To retrieve the next set of labeling jobs, use the token in the next request.NameContains
— (String
)A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.
SortBy
— (String
)The field to sort results by. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
"Status"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
StatusEquals
— (String
)A filter that retrieves only labeling jobs with a specific status.
Possible values include:"Initializing"
"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:LabelingJobSummaryList
— (Array<map>
)An array of
LabelingJobSummary
objects, each describing a labeling job.LabelingJobName
— required — (String
)The name of the labeling job.
LabelingJobArn
— required — (String
)The Amazon Resource Name (ARN) assigned to the labeling job when it was created.
CreationTime
— required — (Date
)The date and time that the job was created (timestamp).
LastModifiedTime
— required — (Date
)The date and time that the job was last modified (timestamp).
LabelingJobStatus
— required — (String
)The current status of the labeling job.
Possible values include:"Initializing"
"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
LabelCounters
— required — (map
)Counts showing the progress of the labeling job.
TotalLabeled
— (Integer
)The total number of objects labeled.
HumanLabeled
— (Integer
)The total number of objects labeled by a human worker.
MachineLabeled
— (Integer
)The total number of objects labeled by automated data labeling.
FailedNonRetryableError
— (Integer
)The total number of objects that could not be labeled due to an error.
Unlabeled
— (Integer
)The total number of objects not yet labeled.
WorkteamArn
— required — (String
)The Amazon Resource Name (ARN) of the work team assigned to the job.
PreHumanTaskLambdaArn
— required — (String
)The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.
AnnotationConsolidationLambdaArn
— (String
)The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation.
FailureReason
— (String
)If the
LabelingJobStatus
field isFailed
, this field contains a description of the error.LabelingJobOutput
— (map
)The location of the output produced by the labeling job.
OutputDatasetS3Uri
— required — (String
)The Amazon S3 bucket location of the manifest file for labeled data.
FinalActiveLearningModelArn
— (String
)The Amazon Resource Name (ARN) for the most recent SageMaker model trained as part of automated data labeling.
InputConfig
— (map
)Input configuration for the labeling job.
DataSource
— required — (map
)The location of the input data.
S3DataSource
— (map
)The Amazon S3 location of the input data objects.
ManifestS3Uri
— required — (String
)The Amazon S3 location of the manifest file that describes the input data objects.
The input manifest file referenced in
ManifestS3Uri
must contain one of the following keys:source-ref
orsource
. The value of the keys are interpreted as follows:-
source-ref
: The source of the object is the Amazon S3 object specified in the value. Use this value when the object is a binary object, such as an image. -
source
: The source of the object is the value. Use this value when the object is a text value.
If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
-
SnsDataSource
— (map
)An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job.
SnsTopicArn
— required — (String
)The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.
DataAttributes
— (map
)Attributes of the data specified by the customer.
ContentClassifiers
— (Array<String>
)Declares that your content is free of personally identifiable information or adult content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listLabelingJobsForWorkteam(params = {}, callback) ⇒ AWS.Request
Gets a list of labeling jobs assigned to a specified work team.
Service Reference:
Examples:
Calling the listLabelingJobsForWorkteam operation
var params = { WorkteamArn: 'STRING_VALUE', /* required */ CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, JobReferenceCodeContains: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime, SortOrder: Ascending | Descending }; sagemaker.listLabelingJobsForWorkteam(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
WorkteamArn
— (String
)The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.
MaxResults
— (Integer
)The maximum number of labeling jobs to return in each page of the response.
NextToken
— (String
)If the result of the previous
ListLabelingJobsForWorkteam
request was truncated, the response includes aNextToken
. To retrieve the next set of labeling jobs, use the token in the next request.CreationTimeAfter
— (Date
)A filter that returns only labeling jobs created after the specified time (timestamp).
CreationTimeBefore
— (Date
)A filter that returns only labeling jobs created before the specified time (timestamp).
JobReferenceCodeContains
— (String
)A filter the limits jobs to only the ones whose job reference code contains the specified string.
SortBy
— (String
)The field to sort results by. The default is
Possible values include:CreationTime
."CreationTime"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:LabelingJobSummaryList
— (Array<map>
)An array of
LabelingJobSummary
objects, each describing a labeling job.LabelingJobName
— (String
)The name of the labeling job that the work team is assigned to.
JobReferenceCode
— required — (String
)A unique identifier for a labeling job. You can use this to refer to a specific labeling job.
WorkRequesterAccountId
— required — (String
)The Amazon Web Services account ID of the account used to start the labeling job.
CreationTime
— required — (Date
)The date and time that the labeling job was created.
LabelCounters
— (map
)Provides information about the progress of a labeling job.
HumanLabeled
— (Integer
)The total number of data objects labeled by a human worker.
PendingHuman
— (Integer
)The total number of data objects that need to be labeled by a human worker.
Total
— (Integer
)The total number of tasks in the labeling job.
NumberOfHumanWorkersPerDataObject
— (Integer
)The configured number of workers per data object.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listLineageGroups(params = {}, callback) ⇒ AWS.Request
A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
Service Reference:
Examples:
Calling the listLineageGroups operation
var params = { CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listLineageGroups(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreatedAfter
— (Date
)A timestamp to filter against lineage groups created after a certain point in time.
CreatedBefore
— (Date
)A timestamp to filter against lineage groups created before a certain point in time.
SortBy
— (String
)The parameter by which to sort the results. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order for the results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
MaxResults
— (Integer
)The maximum number of endpoints to return in the response. This value defaults to 10.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:LineageGroupSummaries
— (Array<map>
)A list of lineage groups and their properties.
LineageGroupArn
— (String
)The Amazon Resource Name (ARN) of the lineage group resource.
LineageGroupName
— (String
)The name or Amazon Resource Name (ARN) of the lineage group.
DisplayName
— (String
)The display name of the lineage group summary.
CreationTime
— (Date
)The creation time of the lineage group summary.
LastModifiedTime
— (Date
)The last modified time of the lineage group summary.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listMlflowTrackingServers(params = {}, callback) ⇒ AWS.Request
Lists all MLflow Tracking Servers.
Service Reference:
Examples:
Calling the listMlflowTrackingServers operation
var params = { CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', MlflowVersion: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, TrackingServerStatus: Creating | Created | CreateFailed | Updating | Updated | UpdateFailed | Deleting | DeleteFailed | Stopping | Stopped | StopFailed | Starting | Started | StartFailed | MaintenanceInProgress | MaintenanceComplete | MaintenanceFailed }; sagemaker.listMlflowTrackingServers(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreatedAfter
— (Date
)Use the
CreatedAfter
filter to only list tracking servers created after a specific date and time. Listed tracking servers are shown with a date and time such as"2024-03-16T01:46:56+00:00"
. TheCreatedAfter
parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.CreatedBefore
— (Date
)Use the
CreatedBefore
filter to only list tracking servers created before a specific date and time. Listed tracking servers are shown with a date and time such as"2024-03-16T01:46:56+00:00"
. TheCreatedBefore
parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.TrackingServerStatus
— (String
)Filter for tracking servers with a specified creation status.
Possible values include:"Creating"
"Created"
"CreateFailed"
"Updating"
"Updated"
"UpdateFailed"
"Deleting"
"DeleteFailed"
"Stopping"
"Stopped"
"StopFailed"
"Starting"
"Started"
"StartFailed"
"MaintenanceInProgress"
"MaintenanceComplete"
"MaintenanceFailed"
MlflowVersion
— (String
)Filter for tracking servers using the specified MLflow version.
SortBy
— (String
)Filter for trackings servers sorting by name, creation time, or creation status.
Possible values include:"Name"
"CreationTime"
"Status"
SortOrder
— (String
)Change the order of the listed tracking servers. By default, tracking servers are listed in
Possible values include:Descending
order by creation time. To change the list order, you can specifySortOrder
to beAscending
."Ascending"
"Descending"
NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
MaxResults
— (Integer
)The maximum number of tracking servers to list.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrackingServerSummaries
— (Array<map>
)A list of tracking servers according to chosen filters.
TrackingServerArn
— (String
)The ARN of a listed tracking server.
TrackingServerName
— (String
)The name of a listed tracking server.
CreationTime
— (Date
)The creation time of a listed tracking server.
LastModifiedTime
— (Date
)The last modified time of a listed tracking server.
TrackingServerStatus
— (String
)The creation status of a listed tracking server.
Possible values include:"Creating"
"Created"
"CreateFailed"
"Updating"
"Updated"
"UpdateFailed"
"Deleting"
"DeleteFailed"
"Stopping"
"Stopped"
"StopFailed"
"Starting"
"Started"
"StartFailed"
"MaintenanceInProgress"
"MaintenanceComplete"
"MaintenanceFailed"
IsActive
— (String
)The activity status of a listed tracking server.
Possible values include:"Active"
"Inactive"
MlflowVersion
— (String
)The MLflow version used for a listed tracking server.
NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
-
(AWS.Response)
—
Returns:
listModelBiasJobDefinitions(params = {}, callback) ⇒ AWS.Request
Lists model bias jobs definitions that satisfy various filters.
Service Reference:
Examples:
Calling the listModelBiasJobDefinitions operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, EndpointName: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listModelBiasJobDefinitions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (String
)Name of the endpoint to monitor for model bias.
SortBy
— (String
)Whether to sort results by the
Possible values include:Name
orCreationTime
field. The default isCreationTime
."Name"
"CreationTime"
SortOrder
— (String
)Whether to sort the results in
Possible values include:Ascending
orDescending
order. The default isDescending
."Ascending"
"Descending"
NextToken
— (String
)The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
MaxResults
— (Integer
)The maximum number of model bias jobs to return in the response. The default value is 10.
NameContains
— (String
)Filter for model bias jobs whose name contains a specified string.
CreationTimeBefore
— (Date
)A filter that returns only model bias jobs created before a specified time.
CreationTimeAfter
— (Date
)A filter that returns only model bias jobs created after a specified time.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionSummaries
— (Array<map>
)A JSON array in which each element is a summary for a model bias jobs.
MonitoringJobDefinitionName
— required — (String
)The name of the monitoring job.
MonitoringJobDefinitionArn
— required — (String
)The Amazon Resource Name (ARN) of the monitoring job.
CreationTime
— required — (Date
)The time that the monitoring job was created.
EndpointName
— required — (String
)The name of the endpoint that the job monitors.
NextToken
— (String
)The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
-
(AWS.Response)
—
Returns:
listModelCardExportJobs(params = {}, callback) ⇒ AWS.Request
List the export jobs for the Amazon SageMaker Model Card.
Service Reference:
Examples:
Calling the listModelCardExportJobs operation
var params = { ModelCardName: 'STRING_VALUE', /* required */ CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', ModelCardExportJobNameContains: 'STRING_VALUE', ModelCardVersion: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: InProgress | Completed | Failed }; sagemaker.listModelCardExportJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelCardName
— (String
)List export jobs for the model card with the specified name.
ModelCardVersion
— (Integer
)List export jobs for the model card with the specified version.
CreationTimeAfter
— (Date
)Only list model card export jobs that were created after the time specified.
CreationTimeBefore
— (Date
)Only list model card export jobs that were created before the time specified.
ModelCardExportJobNameContains
— (String
)Only list model card export jobs with names that contain the specified string.
StatusEquals
— (String
)Only list model card export jobs with the specified status.
Possible values include:"InProgress"
"Completed"
"Failed"
SortBy
— (String
)Sort model card export jobs by either name or creation time. Sorts by creation time by default.
Possible values include:"Name"
"CreationTime"
"Status"
SortOrder
— (String
)Sort model card export jobs by ascending or descending order.
Possible values include:"Ascending"
"Descending"
NextToken
— (String
)If the response to a previous
ListModelCardExportJobs
request was truncated, the response includes aNextToken
. To retrieve the next set of model card export jobs, use the token in the next request.MaxResults
— (Integer
)The maximum number of model card export jobs to list.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelCardExportJobSummaries
— (Array<map>
)The summaries of the listed model card export jobs.
ModelCardExportJobName
— required — (String
)The name of the model card export job.
ModelCardExportJobArn
— required — (String
)The Amazon Resource Name (ARN) of the model card export job.
Status
— required — (String
)The completion status of the model card export job.
Possible values include:"InProgress"
"Completed"
"Failed"
ModelCardName
— required — (String
)The name of the model card that the export job exports.
ModelCardVersion
— required — (Integer
)The version of the model card that the export job exports.
CreatedAt
— required — (Date
)The date and time that the model card export job was created.
LastModifiedAt
— required — (Date
)The date and time that the model card export job was last modified..
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of model card export jobs, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listModelCards(params = {}, callback) ⇒ AWS.Request
List existing model cards.
Service Reference:
Examples:
Calling the listModelCards operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', ModelCardStatus: Draft | PendingReview | Approved | Archived, NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listModelCards(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)Only list model cards that were created after the time specified.
CreationTimeBefore
— (Date
)Only list model cards that were created before the time specified.
MaxResults
— (Integer
)The maximum number of model cards to list.
NameContains
— (String
)Only list model cards with names that contain the specified string.
ModelCardStatus
— (String
)Only list model cards with the specified approval status.
Possible values include:"Draft"
"PendingReview"
"Approved"
"Archived"
NextToken
— (String
)If the response to a previous
ListModelCards
request was truncated, the response includes aNextToken
. To retrieve the next set of model cards, use the token in the next request.SortBy
— (String
)Sort model cards by either name or creation time. Sorts by creation time by default.
Possible values include:"Name"
"CreationTime"
SortOrder
— (String
)Sort model cards by ascending or descending order.
Possible values include:"Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelCardSummaries
— (Array<map>
)The summaries of the listed model cards.
ModelCardName
— required — (String
)The name of the model card.
ModelCardArn
— required — (String
)The Amazon Resource Name (ARN) of the model card.
ModelCardStatus
— required — (String
)The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft
: The model card is a work in progress. -
PendingReview
: The model card is pending review. -
Approved
: The model card is approved. -
Archived
: The model card is archived. No more updates should be made to the model card, but it can still be exported.
"Draft"
"PendingReview"
"Approved"
"Archived"
-
CreationTime
— required — (Date
)The date and time that the model card was created.
LastModifiedTime
— (Date
)The date and time that the model card was last modified.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of model cards, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listModelCardVersions(params = {}, callback) ⇒ AWS.Request
List existing versions of an Amazon SageMaker Model Card.
Service Reference:
Examples:
Calling the listModelCardVersions operation
var params = { ModelCardName: 'STRING_VALUE', /* required */ CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', ModelCardStatus: Draft | PendingReview | Approved | Archived, NextToken: 'STRING_VALUE', SortBy: Version, SortOrder: Ascending | Descending }; sagemaker.listModelCardVersions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)Only list model card versions that were created after the time specified.
CreationTimeBefore
— (Date
)Only list model card versions that were created before the time specified.
MaxResults
— (Integer
)The maximum number of model card versions to list.
ModelCardName
— (String
)List model card versions for the model card with the specified name or Amazon Resource Name (ARN).
ModelCardStatus
— (String
)Only list model card versions with the specified approval status.
Possible values include:"Draft"
"PendingReview"
"Approved"
"Archived"
NextToken
— (String
)If the response to a previous
ListModelCardVersions
request was truncated, the response includes aNextToken
. To retrieve the next set of model card versions, use the token in the next request.SortBy
— (String
)Sort listed model card versions by version. Sorts by version by default.
Possible values include:"Version"
SortOrder
— (String
)Sort model card versions by ascending or descending order.
Possible values include:"Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelCardVersionSummaryList
— (Array<map>
)The summaries of the listed versions of the model card.
ModelCardName
— required — (String
)The name of the model card.
ModelCardArn
— required — (String
)The Amazon Resource Name (ARN) of the model card.
ModelCardStatus
— required — (String
)The approval status of the model card version within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft
: The model card is a work in progress. -
PendingReview
: The model card is pending review. -
Approved
: The model card is approved. -
Archived
: The model card is archived. No more updates should be made to the model card, but it can still be exported.
"Draft"
"PendingReview"
"Approved"
"Archived"
-
ModelCardVersion
— required — (Integer
)A version of the model card.
CreationTime
— required — (Date
)The date and time that the model card version was created.
LastModifiedTime
— (Date
)The time date and time that the model card version was last modified.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of model card versions, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listModelExplainabilityJobDefinitions(params = {}, callback) ⇒ AWS.Request
Lists model explainability job definitions that satisfy various filters.
Service Reference:
Examples:
Calling the listModelExplainabilityJobDefinitions operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, EndpointName: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listModelExplainabilityJobDefinitions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (String
)Name of the endpoint to monitor for model explainability.
SortBy
— (String
)Whether to sort results by the
Possible values include:Name
orCreationTime
field. The default isCreationTime
."Name"
"CreationTime"
SortOrder
— (String
)Whether to sort the results in
Possible values include:Ascending
orDescending
order. The default isDescending
."Ascending"
"Descending"
NextToken
— (String
)The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
MaxResults
— (Integer
)The maximum number of jobs to return in the response. The default value is 10.
NameContains
— (String
)Filter for model explainability jobs whose name contains a specified string.
CreationTimeBefore
— (Date
)A filter that returns only model explainability jobs created before a specified time.
CreationTimeAfter
— (Date
)A filter that returns only model explainability jobs created after a specified time.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionSummaries
— (Array<map>
)A JSON array in which each element is a summary for a explainability bias jobs.
MonitoringJobDefinitionName
— required — (String
)The name of the monitoring job.
MonitoringJobDefinitionArn
— required — (String
)The Amazon Resource Name (ARN) of the monitoring job.
CreationTime
— required — (Date
)The time that the monitoring job was created.
EndpointName
— required — (String
)The name of the endpoint that the job monitors.
NextToken
— (String
)The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
-
(AWS.Response)
—
Returns:
listModelMetadata(params = {}, callback) ⇒ AWS.Request
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
Service Reference:
Examples:
Calling the listModelMetadata operation
var params = { MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SearchExpression: { Filters: [ { Name: Domain | Framework | Task | FrameworkVersion, /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] } }; sagemaker.listModelMetadata(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SearchExpression
— (map
)One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. Specify the Framework, FrameworkVersion, Domain or Task to filter supported. Filter names and values are case-sensitive.
Filters
— (Array<map>
)A list of filter objects.
Name
— required — (String
)The name of the of the model to filter by.
Possible values include:"Domain"
"Framework"
"Task"
"FrameworkVersion"
Value
— required — (String
)The value to filter the model metadata.
NextToken
— (String
)If the response to a previous
ListModelMetadataResponse
request was truncated, the response includes a NextToken. To retrieve the next set of model metadata, use the token in the next request.MaxResults
— (Integer
)The maximum number of models to return in the response.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelMetadataSummaries
— (Array<map>
)A structure that holds model metadata.
Domain
— required — (String
)The machine learning domain of the model.
Framework
— required — (String
)The machine learning framework of the model.
Task
— required — (String
)The machine learning task of the model.
Model
— required — (String
)The name of the model.
FrameworkVersion
— required — (String
)The framework version of the model.
NextToken
— (String
)A token for getting the next set of recommendations, if there are any.
-
(AWS.Response)
—
Returns:
listModelPackageGroups(params = {}, callback) ⇒ AWS.Request
Gets a list of the model groups in your Amazon Web Services account.
Service Reference:
Examples:
Calling the listModelPackageGroups operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CrossAccountFilterOption: SameAccount | CrossAccount, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listModelPackageGroups(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only model groups created after the specified time.
CreationTimeBefore
— (Date
)A filter that returns only model groups created before the specified time.
MaxResults
— (Integer
)The maximum number of results to return in the response.
NameContains
— (String
)A string in the model group name. This filter returns only model groups whose name contains the specified string.
NextToken
— (String
)If the result of the previous
ListModelPackageGroups
request was truncated, the response includes aNextToken
. To retrieve the next set of model groups, use the token in the next request.SortBy
— (String
)The field to sort results by. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
CrossAccountFilterOption
— (String
)A filter that returns either model groups shared with you or model groups in your own account. When the value is
Possible values include:CrossAccount
, the results show the resources made discoverable to you from other accounts. When the value isSameAccount
ornull
, the results show resources from your account. The default isSameAccount
."SameAccount"
"CrossAccount"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageGroupSummaryList
— (Array<map>
)A list of summaries of the model groups in your Amazon Web Services account.
ModelPackageGroupName
— required — (String
)The name of the model group.
ModelPackageGroupArn
— required — (String
)The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription
— (String
)A description of the model group.
CreationTime
— required — (Date
)The time that the model group was created.
ModelPackageGroupStatus
— required — (String
)The status of the model group.
Possible values include:"Pending"
"InProgress"
"Completed"
"Failed"
"Deleting"
"DeleteFailed"
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of model groups, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listModelPackages(params = {}, callback) ⇒ AWS.Request
Lists the model packages that have been created.
Service Reference:
Examples:
Calling the listModelPackages operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', ModelApprovalStatus: Approved | Rejected | PendingManualApproval, ModelPackageGroupName: 'STRING_VALUE', ModelPackageType: Versioned | Unversioned | Both, NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listModelPackages(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only model packages created after the specified time (timestamp).
CreationTimeBefore
— (Date
)A filter that returns only model packages created before the specified time (timestamp).
MaxResults
— (Integer
)The maximum number of model packages to return in the response.
NameContains
— (String
)A string in the model package name. This filter returns only model packages whose name contains the specified string.
ModelApprovalStatus
— (String
)A filter that returns only the model packages with the specified approval status.
Possible values include:"Approved"
"Rejected"
"PendingManualApproval"
ModelPackageGroupName
— (String
)A filter that returns only model versions that belong to the specified model group.
ModelPackageType
— (String
)A filter that returns only the model packages of the specified type. This can be one of the following values.
-
UNVERSIONED
- List only unversioined models. This is the default value if noModelPackageType
is specified. -
VERSIONED
- List only versioned models. -
BOTH
- List both versioned and unversioned models.
"Versioned"
"Unversioned"
"Both"
-
NextToken
— (String
)If the response to a previous
ListModelPackages
request was truncated, the response includes aNextToken
. To retrieve the next set of model packages, use the token in the next request.SortBy
— (String
)The parameter by which to sort the results. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order for the results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageSummaryList
— (Array<map>
)An array of
ModelPackageSummary
objects, each of which lists a model package.ModelPackageName
— (String
)The name of the model package.
ModelPackageGroupName
— (String
)If the model package is a versioned model, the model group that the versioned model belongs to.
ModelPackageVersion
— (Integer
)If the model package is a versioned model, the version of the model.
ModelPackageArn
— required — (String
)The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription
— (String
)A brief description of the model package.
CreationTime
— required — (Date
)A timestamp that shows when the model package was created.
ModelPackageStatus
— required — (String
)The overall status of the model package.
Possible values include:"Pending"
"InProgress"
"Completed"
"Failed"
"Deleting"
ModelApprovalStatus
— (String
)The approval status of the model. This can be one of the following values.
-
APPROVED
- The model is approved -
REJECTED
- The model is rejected. -
PENDING_MANUAL_APPROVAL
- The model is waiting for manual approval.
"Approved"
"Rejected"
"PendingManualApproval"
-
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listModelQualityJobDefinitions(params = {}, callback) ⇒ AWS.Request
Gets a list of model quality monitoring job definitions in your account.
Service Reference:
Examples:
Calling the listModelQualityJobDefinitions operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, EndpointName: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listModelQualityJobDefinitions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (String
)A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.
SortBy
— (String
)The field to sort results by. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)Whether to sort the results in
Possible values include:Ascending
orDescending
order. The default isDescending
."Ascending"
"Descending"
NextToken
— (String
)If the result of the previous
ListModelQualityJobDefinitions
request was truncated, the response includes aNextToken
. To retrieve the next set of model quality monitoring job definitions, use the token in the next request.MaxResults
— (Integer
)The maximum number of results to return in a call to
ListModelQualityJobDefinitions
.NameContains
— (String
)A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.
CreationTimeBefore
— (Date
)A filter that returns only model quality monitoring job definitions created before the specified time.
CreationTimeAfter
— (Date
)A filter that returns only model quality monitoring job definitions created after the specified time.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:JobDefinitionSummaries
— (Array<map>
)A list of summaries of model quality monitoring job definitions.
MonitoringJobDefinitionName
— required — (String
)The name of the monitoring job.
MonitoringJobDefinitionArn
— required — (String
)The Amazon Resource Name (ARN) of the monitoring job.
CreationTime
— required — (Date
)The time that the monitoring job was created.
EndpointName
— required — (String
)The name of the endpoint that the job monitors.
NextToken
— (String
)If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model quality monitoring job definitions, use it in the next request.
-
(AWS.Response)
—
Returns:
listModels(params = {}, callback) ⇒ AWS.Request
Lists models created with the
CreateModel
API.Service Reference:
Examples:
Calling the listModels operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listModels(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SortBy
— (String
)Sorts the list of results. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Descending
."Ascending"
"Descending"
NextToken
— (String
)If the response to a previous
ListModels
request was truncated, the response includes aNextToken
. To retrieve the next set of models, use the token in the next request.MaxResults
— (Integer
)The maximum number of models to return in the response.
NameContains
— (String
)A string in the model name. This filter returns only models whose name contains the specified string.
CreationTimeBefore
— (Date
)A filter that returns only models created before the specified time (timestamp).
CreationTimeAfter
— (Date
)A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Models
— (Array<map>
)An array of
ModelSummary
objects, each of which lists a model.ModelName
— required — (String
)The name of the model that you want a summary for.
ModelArn
— required — (String
)The Amazon Resource Name (ARN) of the model.
CreationTime
— required — (Date
)A timestamp that indicates when the model was created.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of models, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listMonitoringAlertHistory(params = {}, callback) ⇒ AWS.Request
Gets a list of past alerts in a model monitoring schedule.
Service Reference:
Examples:
Calling the listMonitoringAlertHistory operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', MonitoringAlertName: 'STRING_VALUE', MonitoringScheduleName: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: InAlert | OK }; sagemaker.listMonitoringAlertHistory(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)The name of a monitoring schedule.
MonitoringAlertName
— (String
)The name of a monitoring alert.
SortBy
— (String
)The field used to sort results. The default is
Possible values include:CreationTime
."CreationTime"
"Status"
SortOrder
— (String
)The sort order, whether
Possible values include:Ascending
orDescending
, of the alert history. The default isDescending
."Ascending"
"Descending"
NextToken
— (String
)If the result of the previous
ListMonitoringAlertHistory
request was truncated, the response includes aNextToken
. To retrieve the next set of alerts in the history, use the token in the next request.MaxResults
— (Integer
)The maximum number of results to display. The default is 100.
CreationTimeBefore
— (Date
)A filter that returns only alerts created on or before the specified time.
CreationTimeAfter
— (Date
)A filter that returns only alerts created on or after the specified time.
StatusEquals
— (String
)A filter that retrieves only alerts with a specific status.
Possible values include:"InAlert"
"OK"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:MonitoringAlertHistory
— (Array<map>
)An alert history for a model monitoring schedule.
MonitoringScheduleName
— required — (String
)The name of a monitoring schedule.
MonitoringAlertName
— required — (String
)The name of a monitoring alert.
CreationTime
— required — (Date
)A timestamp that indicates when the first alert transition occurred in an alert history. An alert transition can be from status
InAlert
toOK
, or fromOK
toInAlert
.AlertStatus
— required — (String
)The current alert status of an alert.
Possible values include:"InAlert"
"OK"
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of alerts, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listMonitoringAlerts(params = {}, callback) ⇒ AWS.Request
Gets the alerts for a single monitoring schedule.
Service Reference:
Examples:
Calling the listMonitoringAlerts operation
var params = { MonitoringScheduleName: 'STRING_VALUE', /* required */ MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE' }; sagemaker.listMonitoringAlerts(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)The name of a monitoring schedule.
NextToken
— (String
)If the result of the previous
ListMonitoringAlerts
request was truncated, the response includes aNextToken
. To retrieve the next set of alerts in the history, use the token in the next request.MaxResults
— (Integer
)The maximum number of results to display. The default is 100.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:MonitoringAlertSummaries
— (Array<map>
)A JSON array where each element is a summary for a monitoring alert.
MonitoringAlertName
— required — (String
)The name of a monitoring alert.
CreationTime
— required — (Date
)A timestamp that indicates when a monitor alert was created.
LastModifiedTime
— required — (Date
)A timestamp that indicates when a monitor alert was last updated.
AlertStatus
— required — (String
)The current status of an alert.
Possible values include:"InAlert"
"OK"
DatapointsToAlert
— required — (Integer
)Within
EvaluationPeriod
, how many execution failures will raise an alert.EvaluationPeriod
— required — (Integer
)The number of most recent monitoring executions to consider when evaluating alert status.
Actions
— required — (map
)A list of alert actions taken in response to an alert going into
InAlert
status.ModelDashboardIndicator
— (map
)An alert action taken to light up an icon on the Model Dashboard when an alert goes into
InAlert
status.Enabled
— (Boolean
)Indicates whether the alert action is turned on.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of alerts, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listMonitoringExecutions(params = {}, callback) ⇒ AWS.Request
Returns list of all monitoring job executions.
Service Reference:
Examples:
Calling the listMonitoringExecutions operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, EndpointName: 'STRING_VALUE', LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', MonitoringJobDefinitionName: 'STRING_VALUE', MonitoringScheduleName: 'STRING_VALUE', MonitoringTypeEquals: DataQuality | ModelQuality | ModelBias | ModelExplainability, NextToken: 'STRING_VALUE', ScheduledTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, ScheduledTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, SortBy: CreationTime | ScheduledTime | Status, SortOrder: Ascending | Descending, StatusEquals: Pending | Completed | CompletedWithViolations | InProgress | Failed | Stopping | Stopped }; sagemaker.listMonitoringExecutions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)Name of a specific schedule to fetch jobs for.
EndpointName
— (String
)Name of a specific endpoint to fetch jobs for.
SortBy
— (String
)Whether to sort the results by the
Possible values include:Status
,CreationTime
, orScheduledTime
field. The default isCreationTime
."CreationTime"
"ScheduledTime"
"Status"
SortOrder
— (String
)Whether to sort the results in
Possible values include:Ascending
orDescending
order. The default isDescending
."Ascending"
"Descending"
NextToken
— (String
)The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
MaxResults
— (Integer
)The maximum number of jobs to return in the response. The default value is 10.
ScheduledTimeBefore
— (Date
)Filter for jobs scheduled before a specified time.
ScheduledTimeAfter
— (Date
)Filter for jobs scheduled after a specified time.
CreationTimeBefore
— (Date
)A filter that returns only jobs created before a specified time.
CreationTimeAfter
— (Date
)A filter that returns only jobs created after a specified time.
LastModifiedTimeBefore
— (Date
)A filter that returns only jobs modified after a specified time.
LastModifiedTimeAfter
— (Date
)A filter that returns only jobs modified before a specified time.
StatusEquals
— (String
)A filter that retrieves only jobs with a specific status.
Possible values include:"Pending"
"Completed"
"CompletedWithViolations"
"InProgress"
"Failed"
"Stopping"
"Stopped"
MonitoringJobDefinitionName
— (String
)Gets a list of the monitoring job runs of the specified monitoring job definitions.
MonitoringTypeEquals
— (String
)A filter that returns only the monitoring job runs of the specified monitoring type.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:MonitoringExecutionSummaries
— (Array<map>
)A JSON array in which each element is a summary for a monitoring execution.
MonitoringScheduleName
— required — (String
)The name of the monitoring schedule.
ScheduledTime
— required — (Date
)The time the monitoring job was scheduled.
CreationTime
— required — (Date
)The time at which the monitoring job was created.
LastModifiedTime
— required — (Date
)A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus
— required — (String
)The status of the monitoring job.
Possible values include:"Pending"
"Completed"
"CompletedWithViolations"
"InProgress"
"Failed"
"Stopping"
"Stopped"
ProcessingJobArn
— (String
)The Amazon Resource Name (ARN) of the monitoring job.
EndpointName
— (String
)The name of the endpoint used to run the monitoring job.
FailureReason
— (String
)Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job.
MonitoringType
— (String
)The type of the monitoring job.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
NextToken
— (String
)The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
-
(AWS.Response)
—
Returns:
listMonitoringSchedules(params = {}, callback) ⇒ AWS.Request
Returns list of all monitoring schedules.
Service Reference:
Examples:
Calling the listMonitoringSchedules operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, EndpointName: 'STRING_VALUE', LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', MonitoringJobDefinitionName: 'STRING_VALUE', MonitoringTypeEquals: DataQuality | ModelQuality | ModelBias | ModelExplainability, NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: Pending | Failed | Scheduled | Stopped }; sagemaker.listMonitoringSchedules(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (String
)Name of a specific endpoint to fetch schedules for.
SortBy
— (String
)Whether to sort the results by the
Possible values include:Status
,CreationTime
, orScheduledTime
field. The default isCreationTime
."Name"
"CreationTime"
"Status"
SortOrder
— (String
)Whether to sort the results in
Possible values include:Ascending
orDescending
order. The default isDescending
."Ascending"
"Descending"
NextToken
— (String
)The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
MaxResults
— (Integer
)The maximum number of jobs to return in the response. The default value is 10.
NameContains
— (String
)Filter for monitoring schedules whose name contains a specified string.
CreationTimeBefore
— (Date
)A filter that returns only monitoring schedules created before a specified time.
CreationTimeAfter
— (Date
)A filter that returns only monitoring schedules created after a specified time.
LastModifiedTimeBefore
— (Date
)A filter that returns only monitoring schedules modified before a specified time.
LastModifiedTimeAfter
— (Date
)A filter that returns only monitoring schedules modified after a specified time.
StatusEquals
— (String
)A filter that returns only monitoring schedules modified before a specified time.
Possible values include:"Pending"
"Failed"
"Scheduled"
"Stopped"
MonitoringJobDefinitionName
— (String
)Gets a list of the monitoring schedules for the specified monitoring job definition.
MonitoringTypeEquals
— (String
)A filter that returns only the monitoring schedules for the specified monitoring type.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:MonitoringScheduleSummaries
— (Array<map>
)A JSON array in which each element is a summary for a monitoring schedule.
MonitoringScheduleName
— required — (String
)The name of the monitoring schedule.
MonitoringScheduleArn
— required — (String
)The Amazon Resource Name (ARN) of the monitoring schedule.
CreationTime
— required — (Date
)The creation time of the monitoring schedule.
LastModifiedTime
— required — (Date
)The last time the monitoring schedule was modified.
MonitoringScheduleStatus
— required — (String
)The status of the monitoring schedule.
Possible values include:"Pending"
"Failed"
"Scheduled"
"Stopped"
EndpointName
— (String
)The name of the endpoint using the monitoring schedule.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job definition that the schedule is for.
MonitoringType
— (String
)The type of the monitoring job definition that the schedule is for.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
NextToken
— (String
)The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
-
(AWS.Response)
—
Returns:
listNotebookInstanceLifecycleConfigs(params = {}, callback) ⇒ AWS.Request
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
Service Reference:
Examples:
Calling the listNotebookInstanceLifecycleConfigs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | LastModifiedTime, SortOrder: Ascending | Descending }; sagemaker.listNotebookInstanceLifecycleConfigs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)If the result of a
ListNotebookInstanceLifecycleConfigs
request was truncated, the response includes aNextToken
. To get the next set of lifecycle configurations, use the token in the next request.MaxResults
— (Integer
)The maximum number of lifecycle configurations to return in the response.
SortBy
— (String
)Sorts the list of results. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
"LastModifiedTime"
SortOrder
— (String
)The sort order for results.
Possible values include:"Ascending"
"Descending"
NameContains
— (String
)A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.
CreationTimeBefore
— (Date
)A filter that returns only lifecycle configurations that were created before the specified time (timestamp).
CreationTimeAfter
— (Date
)A filter that returns only lifecycle configurations that were created after the specified time (timestamp).
LastModifiedTimeBefore
— (Date
)A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).
LastModifiedTimeAfter
— (Date
)A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NextToken
— (String
)If the response is truncated, SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request.
NotebookInstanceLifecycleConfigs
— (Array<map>
)An array of
NotebookInstanceLifecycleConfiguration
objects, each listing a lifecycle configuration.NotebookInstanceLifecycleConfigName
— required — (String
)The name of the lifecycle configuration.
NotebookInstanceLifecycleConfigArn
— required — (String
)The Amazon Resource Name (ARN) of the lifecycle configuration.
CreationTime
— (Date
)A timestamp that tells when the lifecycle configuration was created.
LastModifiedTime
— (Date
)A timestamp that tells when the lifecycle configuration was last modified.
-
(AWS.Response)
—
Returns:
listNotebookInstances(params = {}, callback) ⇒ AWS.Request
Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
Service Reference:
Examples:
Calling the listNotebookInstances operation
var params = { AdditionalCodeRepositoryEquals: 'STRING_VALUE', CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, DefaultCodeRepositoryContains: 'STRING_VALUE', LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', NotebookInstanceLifecycleConfigNameContains: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: Pending | InService | Stopping | Stopped | Failed | Deleting | Updating }; sagemaker.listNotebookInstances(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)If the previous call to the
ListNotebookInstances
is truncated, the response includes aNextToken
. You can use this token in your subsequentListNotebookInstances
request to fetch the next set of notebook instances.Note: You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.MaxResults
— (Integer
)The maximum number of notebook instances to return.
SortBy
— (String
)The field to sort results by. The default is
Possible values include:Name
."Name"
"CreationTime"
"Status"
SortOrder
— (String
)The sort order for results.
Possible values include:"Ascending"
"Descending"
NameContains
— (String
)A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string.
CreationTimeBefore
— (Date
)A filter that returns only notebook instances that were created before the specified time (timestamp).
CreationTimeAfter
— (Date
)A filter that returns only notebook instances that were created after the specified time (timestamp).
LastModifiedTimeBefore
— (Date
)A filter that returns only notebook instances that were modified before the specified time (timestamp).
LastModifiedTimeAfter
— (Date
)A filter that returns only notebook instances that were modified after the specified time (timestamp).
StatusEquals
— (String
)A filter that returns only notebook instances with the specified status.
Possible values include:"Pending"
"InService"
"Stopping"
"Stopped"
"Failed"
"Deleting"
"Updating"
NotebookInstanceLifecycleConfigNameContains
— (String
)A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.
DefaultCodeRepositoryContains
— (String
)A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.
AdditionalCodeRepositoryEquals
— (String
)A filter that returns only notebook instances with associated with the specified git repository.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NextToken
— (String
)If the response to the previous
ListNotebookInstances
request was truncated, SageMaker returns this token. To retrieve the next set of notebook instances, use the token in the next request.NotebookInstances
— (Array<map>
)An array of
NotebookInstanceSummary
objects, one for each notebook instance.NotebookInstanceName
— required — (String
)The name of the notebook instance that you want a summary for.
NotebookInstanceArn
— required — (String
)The Amazon Resource Name (ARN) of the notebook instance.
NotebookInstanceStatus
— (String
)The status of the notebook instance.
Possible values include:"Pending"
"InService"
"Stopping"
"Stopped"
"Failed"
"Deleting"
"Updating"
Url
— (String
)The URL that you use to connect to the Jupyter notebook running in your notebook instance.
InstanceType
— (String
)The type of ML compute instance that the notebook instance is running on.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
CreationTime
— (Date
)A timestamp that shows when the notebook instance was created.
LastModifiedTime
— (Date
)A timestamp that shows when the notebook instance was last modified.
NotebookInstanceLifecycleConfigName
— (String
)The name of a notebook instance lifecycle configuration associated with this notebook instance.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
DefaultCodeRepository
— (String
)The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
AdditionalCodeRepositories
— (Array<String>
)An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
-
(AWS.Response)
—
Returns:
listOptimizationJobs(params = {}, callback) ⇒ AWS.Request
Lists the optimization jobs in your account and their properties.
Service Reference:
Examples:
Calling the listOptimizationJobs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', OptimizationContains: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: INPROGRESS | COMPLETED | FAILED | STARTING | STOPPING | STOPPED }; sagemaker.listOptimizationJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.
MaxResults
— (Integer
)The maximum number of optimization jobs to return in the response. The default is 50.
CreationTimeAfter
— (Date
)Filters the results to only those optimization jobs that were created after the specified time.
CreationTimeBefore
— (Date
)Filters the results to only those optimization jobs that were created before the specified time.
LastModifiedTimeAfter
— (Date
)Filters the results to only those optimization jobs that were updated after the specified time.
LastModifiedTimeBefore
— (Date
)Filters the results to only those optimization jobs that were updated before the specified time.
OptimizationContains
— (String
)Filters the results to only those optimization jobs that apply the specified optimization techniques. You can specify either
Quantization
orCompilation
.NameContains
— (String
)Filters the results to only those optimization jobs with a name that contains the specified string.
StatusEquals
— (String
)Filters the results to only those optimization jobs with the specified status.
Possible values include:"INPROGRESS"
"COMPLETED"
"FAILED"
"STARTING"
"STOPPING"
"STOPPED"
SortBy
— (String
)The field by which to sort the optimization jobs in the response. The default is
Possible values include:CreationTime
"Name"
"CreationTime"
"Status"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
"Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:OptimizationJobSummaries
— (Array<map>
)A list of optimization jobs and their properties that matches any of the filters you specified in the request.
OptimizationJobName
— required — (String
)The name that you assigned to the optimization job.
OptimizationJobArn
— required — (String
)The Amazon Resource Name (ARN) of the optimization job.
CreationTime
— required — (Date
)The time when you created the optimization job.
OptimizationJobStatus
— required — (String
)The current status of the optimization job.
Possible values include:"INPROGRESS"
"COMPLETED"
"FAILED"
"STARTING"
"STOPPING"
"STOPPED"
OptimizationStartTime
— (Date
)The time when the optimization job started.
OptimizationEndTime
— (Date
)The time when the optimization job finished processing.
LastModifiedTime
— (Date
)The time when the optimization job was last updated.
DeploymentInstanceType
— required — (String
)The type of instance that hosts the optimized model that you create with the optimization job.
Possible values include:"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
OptimizationTypes
— required — (Array<String>
)The optimization techniques that are applied by the optimization job.
NextToken
— (String
)The token to use in a subsequent request to get the next set of results following a truncated response.
-
(AWS.Response)
—
Returns:
listPipelineExecutions(params = {}, callback) ⇒ AWS.Request
Gets a list of the pipeline executions.
Service Reference:
Examples:
Calling the listPipelineExecutions operation
var params = { PipelineName: 'STRING_VALUE', /* required */ CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime | PipelineExecutionArn, SortOrder: Ascending | Descending }; sagemaker.listPipelineExecutions(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineName
— (String
)The name or Amazon Resource Name (ARN) of the pipeline.
CreatedAfter
— (Date
)A filter that returns the pipeline executions that were created after a specified time.
CreatedBefore
— (Date
)A filter that returns the pipeline executions that were created before a specified time.
SortBy
— (String
)The field by which to sort results. The default is
Possible values include:CreatedTime
."CreationTime"
"PipelineExecutionArn"
SortOrder
— (String
)The sort order for results.
Possible values include:"Ascending"
"Descending"
NextToken
— (String
)If the result of the previous
ListPipelineExecutions
request was truncated, the response includes aNextToken
. To retrieve the next set of pipeline executions, use the token in the next request.MaxResults
— (Integer
)The maximum number of pipeline executions to return in the response.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineExecutionSummaries
— (Array<map>
)Contains a sorted list of pipeline execution summary objects matching the specified filters. Each run summary includes the Amazon Resource Name (ARN) of the pipeline execution, the run date, and the status. This list can be empty.
PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
StartTime
— (Date
)The start time of the pipeline execution.
PipelineExecutionStatus
— (String
)The status of the pipeline execution.
Possible values include:"Executing"
"Stopping"
"Stopped"
"Failed"
"Succeeded"
PipelineExecutionDescription
— (String
)The description of the pipeline execution.
PipelineExecutionDisplayName
— (String
)The display name of the pipeline execution.
PipelineExecutionFailureReason
— (String
)A message generated by SageMaker Pipelines describing why the pipeline execution failed.
NextToken
— (String
)If the result of the previous
ListPipelineExecutions
request was truncated, the response includes aNextToken
. To retrieve the next set of pipeline executions, use the token in the next request.
-
(AWS.Response)
—
Returns:
listPipelineExecutionSteps(params = {}, callback) ⇒ AWS.Request
Gets a list of
PipeLineExecutionStep
objects.Service Reference:
Examples:
Calling the listPipelineExecutionSteps operation
var params = { MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', PipelineExecutionArn: 'STRING_VALUE', SortOrder: Ascending | Descending }; sagemaker.listPipelineExecutionSteps(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
NextToken
— (String
)If the result of the previous
ListPipelineExecutionSteps
request was truncated, the response includes aNextToken
. To retrieve the next set of pipeline execution steps, use the token in the next request.MaxResults
— (Integer
)The maximum number of pipeline execution steps to return in the response.
SortOrder
— (String
)The field by which to sort results. The default is
Possible values include:CreatedTime
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineExecutionSteps
— (Array<map>
)A list of
PipeLineExecutionStep
objects. EachPipeLineExecutionStep
consists of StepName, StartTime, EndTime, StepStatus, and Metadata. Metadata is an object with properties for each job that contains relevant information about the job created by the step.StepName
— (String
)The name of the step that is executed.
StepDisplayName
— (String
)The display name of the step.
StepDescription
— (String
)The description of the step.
StartTime
— (Date
)The time that the step started executing.
EndTime
— (Date
)The time that the step stopped executing.
StepStatus
— (String
)The status of the step execution.
Possible values include:"Starting"
"Executing"
"Stopping"
"Stopped"
"Failed"
"Succeeded"
CacheHitResult
— (map
)If this pipeline execution step was cached, details on the cache hit.
SourcePipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
FailureReason
— (String
)The reason why the step failed execution. This is only returned if the step failed its execution.
Metadata
— (map
)Metadata to run the pipeline step.
TrainingJob
— (map
)The Amazon Resource Name (ARN) of the training job that was run by this step execution.
Arn
— (String
)The Amazon Resource Name (ARN) of the training job that was run by this step execution.
ProcessingJob
— (map
)The Amazon Resource Name (ARN) of the processing job that was run by this step execution.
Arn
— (String
)The Amazon Resource Name (ARN) of the processing job.
TransformJob
— (map
)The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
Arn
— (String
)The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
TuningJob
— (map
)The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
Arn
— (String
)The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
Model
— (map
)The Amazon Resource Name (ARN) of the model that was created by this step execution.
Arn
— (String
)The Amazon Resource Name (ARN) of the created model.
RegisterModel
— (map
)The Amazon Resource Name (ARN) of the model package that the model was registered to by this step execution.
Arn
— (String
)The Amazon Resource Name (ARN) of the model package.
Condition
— (map
)The outcome of the condition evaluation that was run by this step execution.
Outcome
— (String
)The outcome of the Condition step evaluation.
Possible values include:"True"
"False"
Callback
— (map
)The URL of the Amazon SQS queue used by this step execution, the pipeline generated token, and a list of output parameters.
CallbackToken
— (String
)The pipeline generated token from the Amazon SQS queue.
SqsQueueUrl
— (String
)The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the callback step.
OutputParameters
— (Array<map>
)A list of the output parameters of the callback step.
Name
— required — (String
)The name of the output parameter.
Value
— required — (String
)The value of the output parameter.
Lambda
— (map
)The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution and a list of output parameters.
Arn
— (String
)The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution.
OutputParameters
— (Array<map>
)A list of the output parameters of the Lambda step.
Name
— required — (String
)The name of the output parameter.
Value
— required — (String
)The value of the output parameter.
EMR
— (map
)The configurations and outcomes of an Amazon EMR step execution.
ClusterId
— (String
)The identifier of the EMR cluster.
StepId
— (String
)The identifier of the EMR cluster step.
StepName
— (String
)The name of the EMR cluster step.
LogFilePath
— (String
)The path to the log file where the cluster step's failure root cause is recorded.
QualityCheck
— (map
)The configurations and outcomes of the check step execution. This includes:
-
The type of the check conducted.
-
The Amazon S3 URIs of baseline constraints and statistics files to be used for the drift check.
-
The Amazon S3 URIs of newly calculated baseline constraints and statistics.
-
The model package group name provided.
-
The Amazon S3 URI of the violation report if violations detected.
-
The Amazon Resource Name (ARN) of check processing job initiated by the step execution.
-
The Boolean flags indicating if the drift check is skipped.
-
If step property
BaselineUsedForDriftCheck
is set the same asCalculatedBaseline
.
CheckType
— (String
)The type of the Quality check step.
BaselineUsedForDriftCheckStatistics
— (String
)The Amazon S3 URI of the baseline statistics file used for the drift check.
BaselineUsedForDriftCheckConstraints
— (String
)The Amazon S3 URI of the baseline constraints file used for the drift check.
CalculatedBaselineStatistics
— (String
)The Amazon S3 URI of the newly calculated baseline statistics file.
CalculatedBaselineConstraints
— (String
)The Amazon S3 URI of the newly calculated baseline constraints file.
ModelPackageGroupName
— (String
)The model package group name.
ViolationReport
— (String
)The Amazon S3 URI of violation report if violations are detected.
CheckJobArn
— (String
)The Amazon Resource Name (ARN) of the Quality check processing job that was run by this step execution.
SkipCheck
— (Boolean
)This flag indicates if the drift check against the previous baseline will be skipped or not. If it is set to
False
, the previous baseline of the configured check type must be available.RegisterNewBaseline
— (Boolean
)This flag indicates if a newly calculated baseline can be accessed through step properties
BaselineUsedForDriftCheckConstraints
andBaselineUsedForDriftCheckStatistics
. If it is set toFalse
, the previous baseline of the configured check type must also be available. These can be accessed through theBaselineUsedForDriftCheckConstraints
andBaselineUsedForDriftCheckStatistics
properties.
-
ClarifyCheck
— (map
)Container for the metadata for a Clarify check step. The configurations and outcomes of the check step execution. This includes:
-
The type of the check conducted,
-
The Amazon S3 URIs of baseline constraints and statistics files to be used for the drift check.
-
The Amazon S3 URIs of newly calculated baseline constraints and statistics.
-
The model package group name provided.
-
The Amazon S3 URI of the violation report if violations detected.
-
The Amazon Resource Name (ARN) of check processing job initiated by the step execution.
-
The boolean flags indicating if the drift check is skipped.
-
If step property
BaselineUsedForDriftCheck
is set the same asCalculatedBaseline
.
CheckType
— (String
)The type of the Clarify Check step
BaselineUsedForDriftCheckConstraints
— (String
)The Amazon S3 URI of baseline constraints file to be used for the drift check.
CalculatedBaselineConstraints
— (String
)The Amazon S3 URI of the newly calculated baseline constraints file.
ModelPackageGroupName
— (String
)The model package group name.
ViolationReport
— (String
)The Amazon S3 URI of the violation report if violations are detected.
CheckJobArn
— (String
)The Amazon Resource Name (ARN) of the check processing job that was run by this step's execution.
SkipCheck
— (Boolean
)This flag indicates if the drift check against the previous baseline will be skipped or not. If it is set to
False
, the previous baseline of the configured check type must be available.RegisterNewBaseline
— (Boolean
)This flag indicates if a newly calculated baseline can be accessed through step properties
BaselineUsedForDriftCheckConstraints
andBaselineUsedForDriftCheckStatistics
. If it is set toFalse
, the previous baseline of the configured check type must also be available. These can be accessed through theBaselineUsedForDriftCheckConstraints
property.
-
Fail
— (map
)The configurations and outcomes of a Fail step execution.
ErrorMessage
— (String
)A message that you define and then is processed and rendered by the Fail step when the error occurs.
AutoMLJob
— (map
)The Amazon Resource Name (ARN) of the AutoML job that was run by this step.
Arn
— (String
)The Amazon Resource Name (ARN) of the AutoML job.
Endpoint
— (map
)The endpoint that was invoked during this step execution.
Arn
— (String
)The Amazon Resource Name (ARN) of the endpoint in the step.
EndpointConfig
— (map
)The endpoint configuration used to create an endpoint during this step execution.
Arn
— (String
)The Amazon Resource Name (ARN) of the endpoint configuration used in the step.
AttemptCount
— (Integer
)The current attempt of the execution step. For more information, see Retry Policy for SageMaker Pipelines steps.
SelectiveExecutionResult
— (map
)The ARN from an execution of the current pipeline from which results are reused for this step.
SourcePipelineExecutionArn
— (String
)The ARN from an execution of the current pipeline.
NextToken
— (String
)If the result of the previous
ListPipelineExecutionSteps
request was truncated, the response includes aNextToken
. To retrieve the next set of pipeline execution steps, use the token in the next request.
-
(AWS.Response)
—
Returns:
listPipelineParametersForExecution(params = {}, callback) ⇒ AWS.Request
Gets a list of parameters for a pipeline execution.
Service Reference:
Examples:
Calling the listPipelineParametersForExecution operation
var params = { PipelineExecutionArn: 'STRING_VALUE', /* required */ MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE' }; sagemaker.listPipelineParametersForExecution(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
NextToken
— (String
)If the result of the previous
ListPipelineParametersForExecution
request was truncated, the response includes aNextToken
. To retrieve the next set of parameters, use the token in the next request.MaxResults
— (Integer
)The maximum number of parameters to return in the response.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineParameters
— (Array<map>
)Contains a list of pipeline parameters. This list can be empty.
Name
— required — (String
)The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value
— required — (String
)The literal value for the parameter.
NextToken
— (String
)If the result of the previous
ListPipelineParametersForExecution
request was truncated, the response includes aNextToken
. To retrieve the next set of parameters, use the token in the next request.
-
(AWS.Response)
—
Returns:
listPipelines(params = {}, callback) ⇒ AWS.Request
Gets a list of pipelines.
Service Reference:
Examples:
Calling the listPipelines operation
var params = { CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', PipelineNamePrefix: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listPipelines(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineNamePrefix
— (String
)The prefix of the pipeline name.
CreatedAfter
— (Date
)A filter that returns the pipelines that were created after a specified time.
CreatedBefore
— (Date
)A filter that returns the pipelines that were created before a specified time.
SortBy
— (String
)The field by which to sort results. The default is
Possible values include:CreatedTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order for results.
Possible values include:"Ascending"
"Descending"
NextToken
— (String
)If the result of the previous
ListPipelines
request was truncated, the response includes aNextToken
. To retrieve the next set of pipelines, use the token in the next request.MaxResults
— (Integer
)The maximum number of pipelines to return in the response.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineSummaries
— (Array<map>
)Contains a sorted list of
PipelineSummary
objects matching the specified filters. EachPipelineSummary
consists of PipelineArn, PipelineName, ExperimentName, PipelineDescription, CreationTime, LastModifiedTime, LastRunTime, and RoleArn. This list can be empty.PipelineArn
— (String
)The Amazon Resource Name (ARN) of the pipeline.
PipelineName
— (String
)The name of the pipeline.
PipelineDisplayName
— (String
)The display name of the pipeline.
PipelineDescription
— (String
)The description of the pipeline.
RoleArn
— (String
)The Amazon Resource Name (ARN) that the pipeline used to execute.
CreationTime
— (Date
)The creation time of the pipeline.
LastModifiedTime
— (Date
)The time that the pipeline was last modified.
LastExecutionTime
— (Date
)The last time that a pipeline execution began.
NextToken
— (String
)If the result of the previous
ListPipelines
request was truncated, the response includes aNextToken
. To retrieve the next set of pipelines, use the token in the next request.
-
(AWS.Response)
—
Returns:
listProcessingJobs(params = {}, callback) ⇒ AWS.Request
Lists processing jobs that satisfy various filters.
Service Reference:
Examples:
Calling the listProcessingJobs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: InProgress | Completed | Failed | Stopping | Stopped }; sagemaker.listProcessingJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only processing jobs created after the specified time.
CreationTimeBefore
— (Date
)A filter that returns only processing jobs created after the specified time.
LastModifiedTimeAfter
— (Date
)A filter that returns only processing jobs modified after the specified time.
LastModifiedTimeBefore
— (Date
)A filter that returns only processing jobs modified before the specified time.
NameContains
— (String
)A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.
StatusEquals
— (String
)A filter that retrieves only processing jobs with a specific status.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
SortBy
— (String
)The field to sort results by. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
"Status"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
NextToken
— (String
)If the result of the previous
ListProcessingJobs
request was truncated, the response includes aNextToken
. To retrieve the next set of processing jobs, use the token in the next request.MaxResults
— (Integer
)The maximum number of processing jobs to return in the response.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ProcessingJobSummaries
— (Array<map>
)An array of
ProcessingJobSummary
objects, each listing a processing job.ProcessingJobName
— required — (String
)The name of the processing job.
ProcessingJobArn
— required — (String
)The Amazon Resource Name (ARN) of the processing job..
CreationTime
— required — (Date
)The time at which the processing job was created.
ProcessingEndTime
— (Date
)The time at which the processing job completed.
LastModifiedTime
— (Date
)A timestamp that indicates the last time the processing job was modified.
ProcessingJobStatus
— required — (String
)The status of the processing job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
FailureReason
— (String
)A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ExitMessage
— (String
)An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
NextToken
— (String
)If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of processing jobs, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listProjects(params = {}, callback) ⇒ AWS.Request
Gets a list of the projects in an Amazon Web Services account.
Service Reference:
Examples:
Calling the listProjects operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending }; sagemaker.listProjects(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns the projects that were created after a specified time.
CreationTimeBefore
— (Date
)A filter that returns the projects that were created before a specified time.
MaxResults
— (Integer
)The maximum number of projects to return in the response.
NameContains
— (String
)A filter that returns the projects whose name contains a specified string.
NextToken
— (String
)If the result of the previous
ListProjects
request was truncated, the response includes aNextToken
. To retrieve the next set of projects, use the token in the next request.SortBy
— (String
)The field by which to sort results. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ProjectSummaryList
— (Array<map>
)A list of summaries of projects.
ProjectName
— required — (String
)The name of the project.
ProjectDescription
— (String
)The description of the project.
ProjectArn
— required — (String
)The Amazon Resource Name (ARN) of the project.
ProjectId
— required — (String
)The ID of the project.
CreationTime
— required — (Date
)The time that the project was created.
ProjectStatus
— required — (String
)The status of the project.
Possible values include:"Pending"
"CreateInProgress"
"CreateCompleted"
"CreateFailed"
"DeleteInProgress"
"DeleteFailed"
"DeleteCompleted"
"UpdateInProgress"
"UpdateCompleted"
"UpdateFailed"
NextToken
— (String
)If the result of the previous
ListCompilationJobs
request was truncated, the response includes aNextToken
. To retrieve the next set of model compilation jobs, use the token in the next request.
-
(AWS.Response)
—
Returns:
listResourceCatalogs(params = {}, callback) ⇒ AWS.Request
Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of
ResourceCatalog
s viewable is 1000.Service Reference:
Examples:
Calling the listResourceCatalogs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime, SortOrder: Ascending | Descending }; sagemaker.listResourceCatalogs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NameContains
— (String
)A string that partially matches one or more
ResourceCatalog
s names. FiltersResourceCatalog
by name.CreationTimeAfter
— (Date
)Use this parameter to search for
ResourceCatalog
s created after a specific date and time.CreationTimeBefore
— (Date
)Use this parameter to search for
ResourceCatalog
s created before a specific date and time.SortOrder
— (String
)The order in which the resource catalogs are listed.
Possible values include:"Ascending"
"Descending"
SortBy
— (String
)The value on which the resource catalog list is sorted.
Possible values include:"CreationTime"
MaxResults
— (Integer
)The maximum number of results returned by
ListResourceCatalogs
.NextToken
— (String
)A token to resume pagination of
ListResourceCatalogs
results.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ResourceCatalogs
— (Array<map>
)A list of the requested
ResourceCatalog
s.ResourceCatalogArn
— required — (String
)The Amazon Resource Name (ARN) of the
ResourceCatalog
.ResourceCatalogName
— required — (String
)The name of the
ResourceCatalog
.Description
— required — (String
)A free form description of the
ResourceCatalog
.CreationTime
— required — (Date
)The time the
ResourceCatalog
was created.
NextToken
— (String
)A token to resume pagination of
ListResourceCatalogs
results.
-
(AWS.Response)
—
Returns:
listSpaces(params = {}, callback) ⇒ AWS.Request
Lists spaces.
Service Reference:
Examples:
Calling the listSpaces operation
var params = { DomainIdEquals: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime | LastModifiedTime, SortOrder: Ascending | Descending, SpaceNameContains: 'STRING_VALUE' }; sagemaker.listSpaces(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
MaxResults
— (Integer
)This parameter defines the maximum number of results that can be return in a single response. The
MaxResults
parameter is an upper bound, not a target. If there are more results available than the value specified, aNextToken
is provided in the response. TheNextToken
indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value forMaxResults
is 10.SortOrder
— (String
)The sort order for the results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
SortBy
— (String
)The parameter by which to sort the results. The default is
Possible values include:CreationTime
."CreationTime"
"LastModifiedTime"
DomainIdEquals
— (String
)A parameter to search for the domain ID.
SpaceNameContains
— (String
)A parameter by which to filter the results.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Spaces
— (Array<map>
)The list of spaces.
DomainId
— (String
)The ID of the associated domain.
SpaceName
— (String
)The name of the space.
Status
— (String
)The status.
Possible values include:"Deleting"
"Failed"
"InService"
"Pending"
"Updating"
"Update_Failed"
"Delete_Failed"
CreationTime
— (Date
)The creation time.
LastModifiedTime
— (Date
)The last modified time.
SpaceSettingsSummary
— (map
)Specifies summary information about the space settings.
AppType
— (String
)The type of app created within the space.
Possible values include:"JupyterServer"
"KernelGateway"
"DetailedProfiler"
"TensorBoard"
"CodeEditor"
"JupyterLab"
"RStudioServerPro"
"RSessionGateway"
"Canvas"
SpaceStorageSettings
— (map
)The storage settings for a space.
EbsStorageSettings
— (map
)A collection of EBS storage settings for a space.
EbsVolumeSizeInGb
— required — (Integer
)The size of an EBS storage volume for a space.
SpaceSharingSettingsSummary
— (map
)Specifies summary information about the space sharing settings.
SharingType
— (String
)Specifies the sharing type of the space.
Possible values include:"Private"
"Shared"
OwnershipSettingsSummary
— (map
)Specifies summary information about the ownership settings.
OwnerUserProfileName
— (String
)The user profile who is the owner of the space.
SpaceDisplayName
— (String
)The name of the space that appears in the Studio UI.
NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
-
(AWS.Response)
—
Returns:
listStageDevices(params = {}, callback) ⇒ AWS.Request
Lists devices allocated to the stage, containing detailed device information and deployment status.
Service Reference:
Examples:
Calling the listStageDevices operation
var params = { EdgeDeploymentPlanName: 'STRING_VALUE', /* required */ StageName: 'STRING_VALUE', /* required */ ExcludeDevicesDeployedInOtherStage: true || false, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE' }; sagemaker.listStageDevices(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)The response from the last list when returning a list large enough to neeed tokening.
MaxResults
— (Integer
)The maximum number of requests to select.
EdgeDeploymentPlanName
— (String
)The name of the edge deployment plan.
ExcludeDevicesDeployedInOtherStage
— (Boolean
)Toggle for excluding devices deployed in other stages.
StageName
— (String
)The name of the stage in the deployment.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DeviceDeploymentSummaries
— (Array<map>
)List of summaries of devices allocated to the stage.
EdgeDeploymentPlanArn
— required — (String
)The ARN of the edge deployment plan.
EdgeDeploymentPlanName
— required — (String
)The name of the edge deployment plan.
StageName
— required — (String
)The name of the stage in the edge deployment plan.
DeployedStageName
— (String
)The name of the deployed stage.
DeviceFleetName
— (String
)The name of the fleet to which the device belongs to.
DeviceName
— required — (String
)The name of the device.
DeviceArn
— required — (String
)The ARN of the device.
DeviceDeploymentStatus
— (String
)The deployment status of the device.
Possible values include:"READYTODEPLOY"
"INPROGRESS"
"DEPLOYED"
"FAILED"
"STOPPING"
"STOPPED"
DeviceDeploymentStatusMessage
— (String
)The detailed error message for the deployoment status result.
Description
— (String
)The description of the device.
DeploymentStartTime
— (Date
)The time when the deployment on the device started.
NextToken
— (String
)The token to use when calling the next page of results.
-
(AWS.Response)
—
Returns:
listStudioLifecycleConfigs(params = {}, callback) ⇒ AWS.Request
Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.
Service Reference:
Examples:
Calling the listStudioLifecycleConfigs operation
var params = { AppTypeEquals: JupyterServer | KernelGateway | CodeEditor | JupyterLab, CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', ModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, ModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime | LastModifiedTime | Name, SortOrder: Ascending | Descending }; sagemaker.listStudioLifecycleConfigs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MaxResults
— (Integer
)The total number of items to return in the response. If the total number of items available is more than the value specified, a
NextToken
is provided in the response. To resume pagination, provide theNextToken
value in the as part of a subsequent call. The default value is 10.NextToken
— (String
)If the previous call to ListStudioLifecycleConfigs didn't return the full set of Lifecycle Configurations, the call returns a token for getting the next set of Lifecycle Configurations.
NameContains
— (String
)A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.
AppTypeEquals
— (String
)A parameter to search for the App Type to which the Lifecycle Configuration is attached.
Possible values include:"JupyterServer"
"KernelGateway"
"CodeEditor"
"JupyterLab"
CreationTimeBefore
— (Date
)A filter that returns only Lifecycle Configurations created on or before the specified time.
CreationTimeAfter
— (Date
)A filter that returns only Lifecycle Configurations created on or after the specified time.
ModifiedTimeBefore
— (Date
)A filter that returns only Lifecycle Configurations modified before the specified time.
ModifiedTimeAfter
— (Date
)A filter that returns only Lifecycle Configurations modified after the specified time.
SortBy
— (String
)The property used to sort results. The default value is CreationTime.
Possible values include:"CreationTime"
"LastModifiedTime"
"Name"
SortOrder
— (String
)The sort order. The default value is Descending.
Possible values include:"Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
StudioLifecycleConfigs
— (Array<map>
)A list of Lifecycle Configurations and their properties.
StudioLifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration.
StudioLifecycleConfigName
— (String
)The name of the Amazon SageMaker Studio Lifecycle Configuration.
CreationTime
— (Date
)The creation time of the Amazon SageMaker Studio Lifecycle Configuration.
LastModifiedTime
— (Date
)This value is equivalent to CreationTime because Amazon SageMaker Studio Lifecycle Configurations are immutable.
StudioLifecycleConfigAppType
— (String
)The App type to which the Lifecycle Configuration is attached.
Possible values include:"JupyterServer"
"KernelGateway"
"CodeEditor"
"JupyterLab"
-
(AWS.Response)
—
Returns:
listSubscribedWorkteams(params = {}, callback) ⇒ AWS.Request
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the
NameContains
parameter.Service Reference:
Examples:
Calling the listSubscribedWorkteams operation
var params = { MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE' }; sagemaker.listSubscribedWorkteams(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NameContains
— (String
)A string in the work team name. This filter returns only work teams whose name contains the specified string.
NextToken
— (String
)If the result of the previous
ListSubscribedWorkteams
request was truncated, the response includes aNextToken
. To retrieve the next set of labeling jobs, use the token in the next request.MaxResults
— (Integer
)The maximum number of work teams to return in each page of the response.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:SubscribedWorkteams
— (Array<map>
)An array of
Workteam
objects, each describing a work team.WorkteamArn
— required — (String
)The Amazon Resource Name (ARN) of the vendor that you have subscribed.
MarketplaceTitle
— (String
)The title of the service provided by the vendor in the Amazon Marketplace.
SellerName
— (String
)The name of the vendor in the Amazon Marketplace.
MarketplaceDescription
— (String
)The description of the vendor from the Amazon Marketplace.
ListingId
— (String
)Marketplace product listing ID.
NextToken
— (String
)If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listTags(params = {}, callback) ⇒ AWS.Request
Returns the tags for the specified SageMaker resource.
Service Reference:
Examples:
Calling the listTags operation
var params = { ResourceArn: 'STRING_VALUE', /* required */ MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE' }; sagemaker.listTags(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ResourceArn
— (String
)The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.
NextToken
— (String
)If the response to the previous
ListTags
request is truncated, SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request.MaxResults
— (Integer
)Maximum number of tags to return.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Tags
— (Array<map>
)An array of
Tag
objects, each with a tag key and a value.Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
NextToken
— (String
)If response is truncated, SageMaker includes a token in the response. You can use this token in your subsequent request to fetch next set of tokens.
-
(AWS.Response)
—
Returns:
listTrainingJobs(params = {}, callback) ⇒ AWS.Request
Lists training jobs.
Note: WhenStatusEquals
andMaxResults
are set at the same time, theMaxResults
number of training jobs are first retrieved ignoring theStatusEquals
parameter and then they are filtered by theStatusEquals
parameter, which is returned as a response. For example, ifListTrainingJobs
is invoked with the following parameters:{ ... MaxResults: 100, StatusEquals: InProgress ... }
First, 100 trainings jobs with any status, including those other thanInProgress
, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status ofInProgress
are returned. You can quickly test the API using the following Amazon Web Services CLI code.aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress
Service Reference:
Examples:
Calling the listTrainingJobs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: InProgress | Completed | Failed | Stopping | Stopped, WarmPoolStatusEquals: Available | Terminated | Reused | InUse }; sagemaker.listTrainingJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)If the result of the previous
ListTrainingJobs
request was truncated, the response includes aNextToken
. To retrieve the next set of training jobs, use the token in the next request.MaxResults
— (Integer
)The maximum number of training jobs to return in the response.
CreationTimeAfter
— (Date
)A filter that returns only training jobs created after the specified time (timestamp).
CreationTimeBefore
— (Date
)A filter that returns only training jobs created before the specified time (timestamp).
LastModifiedTimeAfter
— (Date
)A filter that returns only training jobs modified after the specified time (timestamp).
LastModifiedTimeBefore
— (Date
)A filter that returns only training jobs modified before the specified time (timestamp).
NameContains
— (String
)A string in the training job name. This filter returns only training jobs whose name contains the specified string.
StatusEquals
— (String
)A filter that retrieves only training jobs with a specific status.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
SortBy
— (String
)The field to sort results by. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
"Status"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
WarmPoolStatusEquals
— (String
)A filter that retrieves only training jobs with a specific warm pool status.
Possible values include:"Available"
"Terminated"
"Reused"
"InUse"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrainingJobSummaries
— (Array<map>
)An array of
TrainingJobSummary
objects, each listing a training job.TrainingJobName
— required — (String
)The name of the training job that you want a summary for.
TrainingJobArn
— required — (String
)The Amazon Resource Name (ARN) of the training job.
CreationTime
— required — (Date
)A timestamp that shows when the training job was created.
TrainingEndTime
— (Date
)A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (
Completed
,Failed
, orStopped
).LastModifiedTime
— (Date
)Timestamp when the training job was last modified.
TrainingJobStatus
— required — (String
)The status of the training job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
WarmPoolStatus
— (map
)The status of the warm pool associated with the training job.
Status
— required — (String
)The status of the warm pool.
-
InUse
: The warm pool is in use for the training job. -
Available
: The warm pool is available to reuse for a matching training job. -
Reused
: The warm pool moved to a matching training job for reuse. -
Terminated
: The warm pool is no longer available. Warm pools are unavailable if they are terminated by a user, terminated for a patch update, or terminated for exceeding the specifiedKeepAlivePeriodInSeconds
.
"Available"
"Terminated"
"Reused"
"InUse"
-
ResourceRetainedBillableTimeInSeconds
— (Integer
)The billable time in seconds used by the warm pool. Billable time refers to the absolute wall-clock time.
Multiply
ResourceRetainedBillableTimeInSeconds
by the number of instances (InstanceCount
) in your training cluster to get the total compute time SageMaker bills you if you run warm pool training. The formula is as follows:ResourceRetainedBillableTimeInSeconds * InstanceCount
.ReusedByJob
— (String
)The name of the matching training job that reused the warm pool.
NextToken
— (String
)If the response is truncated, SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
listTrainingJobsForHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
Service Reference:
Examples:
Calling the listTrainingJobsForHyperParameterTuningJob operation
var params = { HyperParameterTuningJobName: 'STRING_VALUE', /* required */ MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status | FinalObjectiveMetricValue, SortOrder: Ascending | Descending, StatusEquals: InProgress | Completed | Failed | Stopping | Stopped }; sagemaker.listTrainingJobsForHyperParameterTuningJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HyperParameterTuningJobName
— (String
)The name of the tuning job whose training jobs you want to list.
NextToken
— (String
)If the result of the previous
ListTrainingJobsForHyperParameterTuningJob
request was truncated, the response includes aNextToken
. To retrieve the next set of training jobs, use the token in the next request.MaxResults
— (Integer
)The maximum number of training jobs to return. The default value is 10.
StatusEquals
— (String
)A filter that returns only training jobs with the specified status.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
SortBy
— (String
)The field to sort results by. The default is
Name
.If the value of this field is
Possible values include:FinalObjectiveMetricValue
, any training jobs that did not return an objective metric are not listed."Name"
"CreationTime"
"Status"
"FinalObjectiveMetricValue"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrainingJobSummaries
— (Array<map>
)A list of TrainingJobSummary objects that describe the training jobs that the
ListTrainingJobsForHyperParameterTuningJob
request returned.TrainingJobDefinitionName
— (String
)The training job definition name.
TrainingJobName
— required — (String
)The name of the training job.
TrainingJobArn
— required — (String
)The Amazon Resource Name (ARN) of the training job.
TuningJobName
— (String
)The HyperParameter tuning job that launched the training job.
CreationTime
— required — (Date
)The date and time that the training job was created.
TrainingStartTime
— (Date
)The date and time that the training job started.
TrainingEndTime
— (Date
)Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of
TrainingStartTime
and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.TrainingJobStatus
— required — (String
)The status of the training job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
TunedHyperParameters
— required — (map<String>
)A list of the hyperparameters for which you specified ranges to search.
FailureReason
— (String
)The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric
— (map
)The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type
— (String
)Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.
Value
— required — (Float
)The value of the objective metric.
ObjectiveStatus
— (String
)The status of the objective metric for the training job:
-
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
-
Pending: The training job is in progress and evaluation of its final objective metric is pending.
-
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
"Succeeded"
"Pending"
"Failed"
-
NextToken
— (String
)If the result of this
ListTrainingJobsForHyperParameterTuningJob
request was truncated, the response includes aNextToken
. To retrieve the next set of training jobs, use the token in the next request.
-
(AWS.Response)
—
Returns:
listTransformJobs(params = {}, callback) ⇒ AWS.Request
Lists transform jobs.
Service Reference:
Examples:
Calling the listTransformJobs operation
var params = { CreationTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreationTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LastModifiedTimeBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime | Status, SortOrder: Ascending | Descending, StatusEquals: InProgress | Completed | Failed | Stopping | Stopped }; sagemaker.listTransformJobs(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CreationTimeAfter
— (Date
)A filter that returns only transform jobs created after the specified time.
CreationTimeBefore
— (Date
)A filter that returns only transform jobs created before the specified time.
LastModifiedTimeAfter
— (Date
)A filter that returns only transform jobs modified after the specified time.
LastModifiedTimeBefore
— (Date
)A filter that returns only transform jobs modified before the specified time.
NameContains
— (String
)A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.
StatusEquals
— (String
)A filter that retrieves only transform jobs with a specific status.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
SortBy
— (String
)The field to sort results by. The default is
Possible values include:CreationTime
."Name"
"CreationTime"
"Status"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Descending
."Ascending"
"Descending"
NextToken
— (String
)If the result of the previous
ListTransformJobs
request was truncated, the response includes aNextToken
. To retrieve the next set of transform jobs, use the token in the next request.MaxResults
— (Integer
)The maximum number of transform jobs to return in the response. The default value is
10
.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TransformJobSummaries
— (Array<map>
)An array of
TransformJobSummary
objects.TransformJobName
— required — (String
)The name of the transform job.
TransformJobArn
— required — (String
)The Amazon Resource Name (ARN) of the transform job.
CreationTime
— required — (Date
)A timestamp that shows when the transform Job was created.
TransformEndTime
— (Date
)Indicates when the transform job ends on compute instances. For successful jobs and stopped jobs, this is the exact time recorded after the results are uploaded. For failed jobs, this is when Amazon SageMaker detected that the job failed.
LastModifiedTime
— (Date
)Indicates when the transform job was last modified.
TransformJobStatus
— required — (String
)The status of the transform job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
FailureReason
— (String
)If the transform job failed, the reason it failed.
NextToken
— (String
)If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of transform jobs, use it in the next request.
-
(AWS.Response)
—
Returns:
listTrialComponents(params = {}, callback) ⇒ AWS.Request
Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:
-
ExperimentName
-
SourceArn
-
TrialName
Service Reference:
Examples:
Calling the listTrialComponents operation
var params = { CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, ExperimentName: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending, SourceArn: 'STRING_VALUE', TrialName: 'STRING_VALUE' }; sagemaker.listTrialComponents(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ExperimentName
— (String
)A filter that returns only components that are part of the specified experiment. If you specify
ExperimentName
, you can't filter bySourceArn
orTrialName
.TrialName
— (String
)A filter that returns only components that are part of the specified trial. If you specify
TrialName
, you can't filter byExperimentName
orSourceArn
.SourceArn
— (String
)A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify
SourceArn
, you can't filter byExperimentName
orTrialName
.CreatedAfter
— (Date
)A filter that returns only components created after the specified time.
CreatedBefore
— (Date
)A filter that returns only components created before the specified time.
SortBy
— (String
)The property used to sort results. The default value is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order. The default value is
Possible values include:Descending
."Ascending"
"Descending"
MaxResults
— (Integer
)The maximum number of components to return in the response. The default value is 10.
NextToken
— (String
)If the previous call to
ListTrialComponents
didn't return the full set of components, the call returns a token for getting the next set of components.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialComponentSummaries
— (Array<map>
)A list of the summaries of your trial components.
TrialComponentName
— (String
)The name of the trial component.
TrialComponentArn
— (String
)The Amazon Resource Name (ARN) of the trial component.
DisplayName
— (String
)The name of the component as displayed. If
DisplayName
isn't specified,TrialComponentName
is displayed.TrialComponentSource
— (map
)The Amazon Resource Name (ARN) and job type of the source of a trial component.
SourceArn
— required — (String
)The source Amazon Resource Name (ARN).
SourceType
— (String
)The source job type.
Status
— (map
)The status of the component. States include:
-
InProgress
-
Completed
-
Failed
PrimaryStatus
— (String
)The status of the trial component.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
Message
— (String
)If the component failed, a message describing why.
-
StartTime
— (Date
)When the component started.
EndTime
— (Date
)When the component ended.
CreationTime
— (Date
)When the component was created.
CreatedBy
— (map
)Who created the trial component.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)When the component was last modified.
LastModifiedBy
— (map
)Who last modified the component.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
NextToken
— (String
)A token for getting the next set of components, if there are any.
-
(AWS.Response)
—
Returns:
listTrials(params = {}, callback) ⇒ AWS.Request
Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
Service Reference:
Examples:
Calling the listTrials operation
var params = { CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, ExperimentName: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreationTime, SortOrder: Ascending | Descending, TrialComponentName: 'STRING_VALUE' }; sagemaker.listTrials(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ExperimentName
— (String
)A filter that returns only trials that are part of the specified experiment.
TrialComponentName
— (String
)A filter that returns only trials that are associated with the specified trial component.
CreatedAfter
— (Date
)A filter that returns only trials created after the specified time.
CreatedBefore
— (Date
)A filter that returns only trials created before the specified time.
SortBy
— (String
)The property used to sort results. The default value is
Possible values include:CreationTime
."Name"
"CreationTime"
SortOrder
— (String
)The sort order. The default value is
Possible values include:Descending
."Ascending"
"Descending"
MaxResults
— (Integer
)The maximum number of trials to return in the response. The default value is 10.
NextToken
— (String
)If the previous call to
ListTrials
didn't return the full set of trials, the call returns a token for getting the next set of trials.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialSummaries
— (Array<map>
)A list of the summaries of your trials.
TrialArn
— (String
)The Amazon Resource Name (ARN) of the trial.
TrialName
— (String
)The name of the trial.
DisplayName
— (String
)The name of the trial as displayed. If
DisplayName
isn't specified,TrialName
is displayed.TrialSource
— (map
)The source of the trial.
SourceArn
— required — (String
)The Amazon Resource Name (ARN) of the source.
SourceType
— (String
)The source job type.
CreationTime
— (Date
)When the trial was created.
LastModifiedTime
— (Date
)When the trial was last modified.
NextToken
— (String
)A token for getting the next set of trials, if there are any.
-
(AWS.Response)
—
Returns:
listUserProfiles(params = {}, callback) ⇒ AWS.Request
Lists user profiles.
Service Reference:
Examples:
Calling the listUserProfiles operation
var params = { DomainIdEquals: 'STRING_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SortBy: CreationTime | LastModifiedTime, SortOrder: Ascending | Descending, UserProfileNameContains: 'STRING_VALUE' }; sagemaker.listUserProfiles(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
MaxResults
— (Integer
)This parameter defines the maximum number of results that can be return in a single response. The
MaxResults
parameter is an upper bound, not a target. If there are more results available than the value specified, aNextToken
is provided in the response. TheNextToken
indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value forMaxResults
is 10.SortOrder
— (String
)The sort order for the results. The default is Ascending.
Possible values include:"Ascending"
"Descending"
SortBy
— (String
)The parameter by which to sort the results. The default is CreationTime.
Possible values include:"CreationTime"
"LastModifiedTime"
DomainIdEquals
— (String
)A parameter by which to filter the results.
UserProfileNameContains
— (String
)A parameter by which to filter the results.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:UserProfiles
— (Array<map>
)The list of user profiles.
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The user profile name.
Status
— (String
)The status.
Possible values include:"Deleting"
"Failed"
"InService"
"Pending"
"Updating"
"Update_Failed"
"Delete_Failed"
CreationTime
— (Date
)The creation time.
LastModifiedTime
— (Date
)The last modified time.
NextToken
— (String
)If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
-
(AWS.Response)
—
Returns:
listWorkforces(params = {}, callback) ⇒ AWS.Request
Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.
Service Reference:
Examples:
Calling the listWorkforces operation
var params = { MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreateDate, SortOrder: Ascending | Descending }; sagemaker.listWorkforces(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SortBy
— (String
)Sort workforces using the workforce name or creation date.
Possible values include:"Name"
"CreateDate"
SortOrder
— (String
)Sort workforces in ascending or descending order.
Possible values include:"Ascending"
"Descending"
NameContains
— (String
)A filter you can use to search for workforces using part of the workforce name.
NextToken
— (String
)A token to resume pagination.
MaxResults
— (Integer
)The maximum number of workforces returned in the response.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Workforces
— (Array<map>
)A list containing information about your workforce.
WorkforceName
— required — (String
)The name of the private workforce.
WorkforceArn
— required — (String
)The Amazon Resource Name (ARN) of the private workforce.
LastUpdatedDate
— (Date
)The most recent date that UpdateWorkforce was used to successfully add one or more IP address ranges (CIDRs) to a private workforce's allow list.
SourceIpConfig
— (map
)A list of one to ten IP address ranges (CIDRs) to be added to the workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
Cidrs
— required — (Array<String>
)A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note: The following Length Constraints apply to individual CIDR values in the CIDR value list.
SubDomain
— (String
)The subdomain for your OIDC Identity Provider.
CognitoConfig
— (map
)The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
UserPool
— required — (String
)A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId
— required — (String
)The client ID for your Amazon Cognito user pool.
OidcConfig
— (map
)The configuration of an OIDC Identity Provider (IdP) private workforce.
ClientId
— (String
)The OIDC IdP client ID used to configure your private workforce.
Issuer
— (String
)The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint
— (String
)The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint
— (String
)The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint
— (String
)The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint
— (String
)The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri
— (String
)The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
Scope
— (String
)An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.
AuthenticationRequestExtraParams
— (map<String>
)A string to string map of identifiers specific to the custom identity provider (IdP) being used.
CreateDate
— (Date
)The date that the workforce is created.
WorkforceVpcConfig
— (map
)The configuration of a VPC workforce.
VpcId
— required — (String
)The ID of the VPC that the workforce uses for communication.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
Subnets
— required — (Array<String>
)The ID of the subnets in the VPC that you want to connect.
VpcEndpointId
— (String
)The IDs for the VPC service endpoints of your VPC workforce when it is created and updated.
Status
— (String
)The status of your workforce.
Possible values include:"Initializing"
"Updating"
"Deleting"
"Failed"
"Active"
FailureReason
— (String
)The reason your workforce failed.
NextToken
— (String
)A token to resume pagination.
-
(AWS.Response)
—
Returns:
listWorkteams(params = {}, callback) ⇒ AWS.Request
Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the
NameContains
parameter.Service Reference:
Examples:
Calling the listWorkteams operation
var params = { MaxResults: 'NUMBER_VALUE', NameContains: 'STRING_VALUE', NextToken: 'STRING_VALUE', SortBy: Name | CreateDate, SortOrder: Ascending | Descending }; sagemaker.listWorkteams(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
SortBy
— (String
)The field to sort results by. The default is
Possible values include:CreationTime
."Name"
"CreateDate"
SortOrder
— (String
)The sort order for results. The default is
Possible values include:Ascending
."Ascending"
"Descending"
NameContains
— (String
)A string in the work team's name. This filter returns only work teams whose name contains the specified string.
NextToken
— (String
)If the result of the previous
ListWorkteams
request was truncated, the response includes aNextToken
. To retrieve the next set of labeling jobs, use the token in the next request.MaxResults
— (Integer
)The maximum number of work teams to return in each page of the response.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Workteams
— (Array<map>
)An array of
Workteam
objects, each describing a work team.WorkteamName
— required — (String
)The name of the work team.
MemberDefinitions
— required — (Array<map>
)A list of
MemberDefinition
objects that contains objects that identify the workers that make up the work team.Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use
CognitoMemberDefinition
. For workforces created using your own OIDC identity provider (IdP) useOidcMemberDefinition
.CognitoMemberDefinition
— (map
)The Amazon Cognito user group that is part of the work team.
UserPool
— required — (String
)An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup
— required — (String
)An identifier for a user group.
ClientId
— required — (String
)An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition
— (map
)A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of
Groups
, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.Groups
— (Array<String>
)A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
WorkteamArn
— required — (String
)The Amazon Resource Name (ARN) that identifies the work team.
WorkforceArn
— (String
)The Amazon Resource Name (ARN) of the workforce.
ProductListingIds
— (Array<String>
)The Amazon Marketplace identifier for a vendor's work team.
Description
— required — (String
)A description of the work team.
SubDomain
— (String
)The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
CreateDate
— (Date
)The date and time that the work team was created (timestamp).
LastUpdatedDate
— (Date
)The date and time that the work team was last updated (timestamp).
NotificationConfiguration
— (map
)Configures SNS notifications of available or expiring work items for work teams.
NotificationTopicArn
— (String
)The ARN for the Amazon SNS topic to which notifications should be published.
WorkerAccessConfiguration
— (map
)Describes any access constraints that have been defined for Amazon S3 resources.
S3Presign
— (map
)Defines any Amazon S3 resource constraints.
IamPolicyConstraints
— (map
)Use this parameter to specify the allowed request source. Possible sources are either
SourceIp
orVpcSourceIp
.SourceIp
— (String
)When
Possible values include:SourceIp
isEnabled
the worker's IP address when a task is rendered in the worker portal is added to the IAM policy as aCondition
used to generate the Amazon S3 presigned URL. This IP address is checked by Amazon S3 and must match in order for the Amazon S3 resource to be rendered in the worker portal."Enabled"
"Disabled"
VpcSourceIp
— (String
)When
Possible values include:VpcSourceIp
isEnabled
the worker's IP address when a task is rendered in private worker portal inside the VPC is added to the IAM policy as aCondition
used to generate the Amazon S3 presigned URL. To render the task successfully Amazon S3 checks that the presigned URL is being accessed over an Amazon S3 VPC Endpoint, and that the worker's IP address matches the IP address in the IAM policy. To learn more about configuring private worker portal, see Use Amazon VPC mode from a private worker portal."Enabled"
"Disabled"
NextToken
— (String
)If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.
-
(AWS.Response)
—
Returns:
putModelPackageGroupPolicy(params = {}, callback) ⇒ AWS.Request
Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
Service Reference:
Examples:
Calling the putModelPackageGroupPolicy operation
var params = { ModelPackageGroupName: 'STRING_VALUE', /* required */ ResourcePolicy: 'STRING_VALUE' /* required */ }; sagemaker.putModelPackageGroupPolicy(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageGroupName
— (String
)The name of the model group to add a resource policy to.
ResourcePolicy
— (String
)The resource policy for the model group.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageGroupArn
— (String
)The Amazon Resource Name (ARN) of the model package group.
-
(AWS.Response)
—
Returns:
queryLineage(params = {}, callback) ⇒ AWS.Request
Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.
Service Reference:
Examples:
Calling the queryLineage operation
var params = { Direction: Both | Ascendants | Descendants, Filters: { CreatedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, CreatedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, LineageTypes: [ TrialComponent | Artifact | Context | Action, /* more items */ ], ModifiedAfter: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, ModifiedBefore: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, Properties: { '<String256>': 'STRING_VALUE', /* '<String256>': ... */ }, Types: [ 'STRING_VALUE', /* more items */ ] }, IncludeEdges: true || false, MaxDepth: 'NUMBER_VALUE', MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', StartArns: [ 'STRING_VALUE', /* more items */ ] }; sagemaker.queryLineage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
StartArns
— (Array<String>
)A list of resource Amazon Resource Name (ARN) that represent the starting point for your lineage query.
Direction
— (String
)Associations between lineage entities have a direction. This parameter determines the direction from the StartArn(s) that the query traverses.
Possible values include:"Both"
"Ascendants"
"Descendants"
IncludeEdges
— (Boolean
)Setting this value to
True
retrieves not only the entities of interest but also the Associations and lineage entities on the path. Set toFalse
to only return lineage entities that match your query.Filters
— (map
)A set of filtering parameters that allow you to specify which entities should be returned.
-
Properties - Key-value pairs to match on the lineage entities' properties.
-
LineageTypes - A set of lineage entity types to match on. For example:
TrialComponent
,Artifact
, orContext
. -
CreatedBefore - Filter entities created before this date.
-
ModifiedBefore - Filter entities modified before this date.
-
ModifiedAfter - Filter entities modified after this date.
Types
— (Array<String>
)Filter the lineage entities connected to the
StartArn
by type. For example:DataSet
,Model
,Endpoint
, orModelDeployment
.LineageTypes
— (Array<String>
)Filter the lineage entities connected to the
StartArn
(s) by the type of the lineage entity.CreatedBefore
— (Date
)Filter the lineage entities connected to the
StartArn
(s) by created date.CreatedAfter
— (Date
)Filter the lineage entities connected to the
StartArn
(s) after the create date.ModifiedBefore
— (Date
)Filter the lineage entities connected to the
StartArn
(s) before the last modified date.ModifiedAfter
— (Date
)Filter the lineage entities connected to the
StartArn
(s) after the last modified date.Properties
— (map<String>
)Filter the lineage entities connected to the
StartArn
(s) by a set if property key value pairs. If multiple pairs are provided, an entity is included in the results if it matches any of the provided pairs.
-
MaxDepth
— (Integer
)The maximum depth in lineage relationships from the
StartArns
that are traversed. Depth is a measure of the number ofAssociations
from theStartArn
entity to the matched results.MaxResults
— (Integer
)Limits the number of vertices in the results. Use the
NextToken
in a response to to retrieve the next page of results.NextToken
— (String
)Limits the number of vertices in the request. Use the
NextToken
in a response to to retrieve the next page of results.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Vertices
— (Array<map>
)A list of vertices connected to the start entity(ies) in the lineage graph.
Arn
— (String
)The Amazon Resource Name (ARN) of the lineage entity resource.
Type
— (String
)The type of the lineage entity resource. For example:
DataSet
,Model
,Endpoint
, etc...LineageType
— (String
)The type of resource of the lineage entity.
Possible values include:"TrialComponent"
"Artifact"
"Context"
"Action"
Edges
— (Array<map>
)A list of edges that connect vertices in the response.
SourceArn
— (String
)The Amazon Resource Name (ARN) of the source lineage entity of the directed edge.
DestinationArn
— (String
)The Amazon Resource Name (ARN) of the destination lineage entity of the directed edge.
AssociationType
— (String
)The type of the Association(Edge) between the source and destination. For example
Possible values include:ContributedTo
,Produced
, orDerivedFrom
."ContributedTo"
"AssociatedWith"
"DerivedFrom"
"Produced"
"SameAs"
NextToken
— (String
)Limits the number of vertices in the response. Use the
NextToken
in a response to to retrieve the next page of results.
-
(AWS.Response)
—
Returns:
registerDevices(params = {}, callback) ⇒ AWS.Request
Register devices.
Service Reference:
Examples:
Calling the registerDevices operation
var params = { DeviceFleetName: 'STRING_VALUE', /* required */ Devices: [ /* required */ { DeviceName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', IotThingName: 'STRING_VALUE' }, /* more items */ ], Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.registerDevices(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DeviceFleetName
— (String
)The name of the fleet.
Devices
— (Array<map>
)A list of devices to register with SageMaker Edge Manager.
DeviceName
— required — (String
)The name of the device.
Description
— (String
)Description of the device.
IotThingName
— (String
)Amazon Web Services Internet of Things (IoT) object name.
Tags
— (Array<map>
)The tags associated with devices.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
renderUiTemplate(params = {}, callback) ⇒ AWS.Request
Renders the UI template so that you can preview the worker's experience.
Service Reference:
Examples:
Calling the renderUiTemplate operation
var params = { RoleArn: 'STRING_VALUE', /* required */ Task: { /* required */ Input: 'STRING_VALUE' /* required */ }, HumanTaskUiArn: 'STRING_VALUE', UiTemplate: { Content: 'STRING_VALUE' /* required */ } }; sagemaker.renderUiTemplate(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
UiTemplate
— (map
)A
Template
object containing the worker UI template to render.Content
— required — (String
)The content of the Liquid template for the worker user interface.
Task
— (map
)A
RenderableTask
object containing a representative task to render.Input
— required — (String
)A JSON object that contains values for the variables defined in the template. It is made available to the template under the substitution variable
task.input
. For example, if you define a variabletask.input.text
in your template, you can supply the variable in the JSON object as"text": "sample text"
.
RoleArn
— (String
)The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.
HumanTaskUiArn
— (String
)The
HumanTaskUiArn
of the worker UI that you want to render. Do not provide aHumanTaskUiArn
if you use theUiTemplate
parameter.See a list of available Human Ui Amazon Resource Names (ARNs) in UiConfig.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:RenderedContent
— (String
)A Liquid template that renders the HTML for the worker UI.
Errors
— (Array<map>
)A list of one or more
RenderingError
objects if any were encountered while rendering the template. If there were no errors, the list is empty.Code
— required — (String
)A unique identifier for a specific class of errors.
Message
— required — (String
)A human-readable message describing the error.
-
(AWS.Response)
—
Returns:
retryPipelineExecution(params = {}, callback) ⇒ AWS.Request
Retry the execution of the pipeline.
Service Reference:
Examples:
Calling the retryPipelineExecution operation
var params = { ClientRequestToken: 'STRING_VALUE', /* required */ PipelineExecutionArn: 'STRING_VALUE', /* required */ ParallelismConfiguration: { MaxParallelExecutionSteps: 'NUMBER_VALUE' /* required */ } }; sagemaker.retryPipelineExecution(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
ClientRequestToken
— (String
)A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
If a token is not provided, the SDK will use a version 4 UUID.ParallelismConfiguration
— (map
)This configuration, if specified, overrides the parallelism configuration of the parent pipeline.
MaxParallelExecutionSteps
— required — (Integer
)The max number of steps that can be executed in parallel.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
-
(AWS.Response)
—
Returns:
search(params = {}, callback) ⇒ AWS.Request
Finds SageMaker resources that match a search query. Matching resources are returned as a list of
SearchRecord
objects in the response. You can sort the search results by any resource property in a ascending or descending order.You can query against the following value types: numeric, text, Boolean, and timestamp.
Note: The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.Service Reference:
Examples:
Calling the search operation
var params = { Resource: TrainingJob | Experiment | ExperimentTrial | ExperimentTrialComponent | Endpoint | Model | ModelPackage | ModelPackageGroup | Pipeline | PipelineExecution | FeatureGroup | FeatureMetadata | Image | ImageVersion | Project | HyperParameterTuningJob | ModelCard, /* required */ CrossAccountFilterOption: SameAccount | CrossAccount, MaxResults: 'NUMBER_VALUE', NextToken: 'STRING_VALUE', SearchExpression: { /* SearchExpression */ Filters: [ { Name: 'STRING_VALUE', /* required */ Operator: Equals | NotEquals | GreaterThan | GreaterThanOrEqualTo | LessThan | LessThanOrEqualTo | Contains | Exists | NotExists | In, Value: 'STRING_VALUE' }, /* more items */ ], NestedFilters: [ { Filters: [ /* required */ { Name: 'STRING_VALUE', /* required */ Operator: Equals | NotEquals | GreaterThan | GreaterThanOrEqualTo | LessThan | LessThanOrEqualTo | Contains | Exists | NotExists | In, Value: 'STRING_VALUE' }, /* more items */ ], NestedPropertyName: 'STRING_VALUE' /* required */ }, /* more items */ ], Operator: And | Or, SubExpressions: [ /* recursive SearchExpression */, /* more items */ ] }, SortBy: 'STRING_VALUE', SortOrder: Ascending | Descending, VisibilityConditions: [ { Key: 'STRING_VALUE', Value: 'STRING_VALUE' }, /* more items */ ] }; sagemaker.search(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
Resource
— (String
)The name of the SageMaker resource to search for.
Possible values include:"TrainingJob"
"Experiment"
"ExperimentTrial"
"ExperimentTrialComponent"
"Endpoint"
"Model"
"ModelPackage"
"ModelPackageGroup"
"Pipeline"
"PipelineExecution"
"FeatureGroup"
"FeatureMetadata"
"Image"
"ImageVersion"
"Project"
"HyperParameterTuningJob"
"ModelCard"
SearchExpression
— (map
)A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive
SubExpressions
,NestedFilters
, andFilters
that can be included in aSearchExpression
object is 50.Filters
— (Array<map>
)A list of filter objects.
Name
— required — (String
)A resource property name. For example,
TrainingJobName
. For valid property names, see SearchRecord. You must specify a valid property for the resource.Operator
— (String
)A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
- Equals
-
The value of
Name
equalsValue
. - NotEquals
-
The value of
Name
doesn't equalValue
. - Exists
-
The
Name
property exists. - NotExists
-
The
Name
property does not exist. - GreaterThan
-
The value of
Name
is greater thanValue
. Not supported for text properties. - GreaterThanOrEqualTo
-
The value of
Name
is greater than or equal toValue
. Not supported for text properties. - LessThan
-
The value of
Name
is less thanValue
. Not supported for text properties. - LessThanOrEqualTo
-
The value of
Name
is less than or equal toValue
. Not supported for text properties. - In
-
The value of
Name
is one of the comma delimited strings inValue
. Only supported for text properties. - Contains
-
The value of
Name
contains the stringValue
. Only supported for text properties.A
SearchExpression
can include theContains
operator multiple times when the value ofName
is one of the following:-
Experiment.DisplayName
-
Experiment.ExperimentName
-
Experiment.Tags
-
Trial.DisplayName
-
Trial.TrialName
-
Trial.Tags
-
TrialComponent.DisplayName
-
TrialComponent.TrialComponentName
-
TrialComponent.Tags
-
TrialComponent.InputArtifacts
-
TrialComponent.OutputArtifacts
A
SearchExpression
can include only oneContains
operator for all other values ofName
. In these cases, if you include multipleContains
operators in theSearchExpression
, the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded.
" -
"Equals"
"NotEquals"
"GreaterThan"
"GreaterThanOrEqualTo"
"LessThan"
"LessThanOrEqualTo"
"Contains"
"Exists"
"NotExists"
"In"
Value
— (String
)A value used with
Name
andOperator
to determine which resources satisfy the filter's condition. For numerical properties,Value
must be an integer or floating-point decimal. For timestamp properties,Value
must be an ISO 8601 date-time string of the following format:YYYY-mm-dd'T'HH:MM:SS
.
NestedFilters
— (Array<map>
)A list of nested filter objects.
NestedPropertyName
— required — (String
)The name of the property to use in the nested filters. The value must match a listed property name, such as
InputDataConfig
.Filters
— required — (Array<map>
)A list of filters. Each filter acts on a property. Filters must contain at least one
Filters
value. For example, aNestedFilters
call might include a filter on thePropertyName
parameter of theInputDataConfig
property:InputDataConfig.DataSource.S3DataSource.S3Uri
.Name
— required — (String
)A resource property name. For example,
TrainingJobName
. For valid property names, see SearchRecord. You must specify a valid property for the resource.Operator
— (String
)A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
- Equals
-
The value of
Name
equalsValue
. - NotEquals
-
The value of
Name
doesn't equalValue
. - Exists
-
The
Name
property exists. - NotExists
-
The
Name
property does not exist. - GreaterThan
-
The value of
Name
is greater thanValue
. Not supported for text properties. - GreaterThanOrEqualTo
-
The value of
Name
is greater than or equal toValue
. Not supported for text properties. - LessThan
-
The value of
Name
is less thanValue
. Not supported for text properties. - LessThanOrEqualTo
-
The value of
Name
is less than or equal toValue
. Not supported for text properties. - In
-
The value of
Name
is one of the comma delimited strings inValue
. Only supported for text properties. - Contains
-
The value of
Name
contains the stringValue
. Only supported for text properties.A
SearchExpression
can include theContains
operator multiple times when the value ofName
is one of the following:-
Experiment.DisplayName
-
Experiment.ExperimentName
-
Experiment.Tags
-
Trial.DisplayName
-
Trial.TrialName
-
Trial.Tags
-
TrialComponent.DisplayName
-
TrialComponent.TrialComponentName
-
TrialComponent.Tags
-
TrialComponent.InputArtifacts
-
TrialComponent.OutputArtifacts
A
SearchExpression
can include only oneContains
operator for all other values ofName
. In these cases, if you include multipleContains
operators in theSearchExpression
, the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded.
" -
"Equals"
"NotEquals"
"GreaterThan"
"GreaterThanOrEqualTo"
"LessThan"
"LessThanOrEqualTo"
"Contains"
"Exists"
"NotExists"
"In"
Value
— (String
)A value used with
Name
andOperator
to determine which resources satisfy the filter's condition. For numerical properties,Value
must be an integer or floating-point decimal. For timestamp properties,Value
must be an ISO 8601 date-time string of the following format:YYYY-mm-dd'T'HH:MM:SS
.
SubExpressions
— (Array<map>
)A list of search expression objects.
Filters
— (Array<map>
)A list of filter objects.
Name
— required — (String
)A resource property name. For example,
TrainingJobName
. For valid property names, see SearchRecord. You must specify a valid property for the resource.Operator
— (String
)A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
- Equals
-
The value of
Name
equalsValue
. - NotEquals
-
The value of
Name
doesn't equalValue
. - Exists
-
The
Name
property exists. - NotExists
-
The
Name
property does not exist. - GreaterThan
-
The value of
Name
is greater thanValue
. Not supported for text properties. - GreaterThanOrEqualTo
-
The value of
Name
is greater than or equal toValue
. Not supported for text properties. - LessThan
-
The value of
Name
is less thanValue
. Not supported for text properties. - LessThanOrEqualTo
-
The value of
Name
is less than or equal toValue
. Not supported for text properties. - In
-
The value of
Name
is one of the comma delimited strings inValue
. Only supported for text properties. - Contains
-
The value of
Name
contains the stringValue
. Only supported for text properties.A
SearchExpression
can include theContains
operator multiple times when the value ofName
is one of the following:-
Experiment.DisplayName
-
Experiment.ExperimentName
-
Experiment.Tags
-
Trial.DisplayName
-
Trial.TrialName
-
Trial.Tags
-
TrialComponent.DisplayName
-
TrialComponent.TrialComponentName
-
TrialComponent.Tags
-
TrialComponent.InputArtifacts
-
TrialComponent.OutputArtifacts
A
SearchExpression
can include only oneContains
operator for all other values ofName
. In these cases, if you include multipleContains
operators in theSearchExpression
, the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded.
" -
"Equals"
"NotEquals"
"GreaterThan"
"GreaterThanOrEqualTo"
"LessThan"
"LessThanOrEqualTo"
"Contains"
"Exists"
"NotExists"
"In"
Value
— (String
)A value used with
Name
andOperator
to determine which resources satisfy the filter's condition. For numerical properties,Value
must be an integer or floating-point decimal. For timestamp properties,Value
must be an ISO 8601 date-time string of the following format:YYYY-mm-dd'T'HH:MM:SS
.
NestedFilters
— (Array<map>
)A list of nested filter objects.
NestedPropertyName
— required — (String
)The name of the property to use in the nested filters. The value must match a listed property name, such as
InputDataConfig
.Filters
— required — (Array<map>
)A list of filters. Each filter acts on a property. Filters must contain at least one
Filters
value. For example, aNestedFilters
call might include a filter on thePropertyName
parameter of theInputDataConfig
property:InputDataConfig.DataSource.S3DataSource.S3Uri
.Name
— required — (String
)A resource property name. For example,
TrainingJobName
. For valid property names, see SearchRecord. You must specify a valid property for the resource.Operator
— (String
)A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
- Equals
-
The value of
Name
equalsValue
. - NotEquals
-
The value of
Name
doesn't equalValue
. - Exists
-
The
Name
property exists. - NotExists
-
The
Name
property does not exist. - GreaterThan
-
The value of
Name
is greater thanValue
. Not supported for text properties. - GreaterThanOrEqualTo
-
The value of
Name
is greater than or equal toValue
. Not supported for text properties. - LessThan
-
The value of
Name
is less thanValue
. Not supported for text properties. - LessThanOrEqualTo
-
The value of
Name
is less than or equal toValue
. Not supported for text properties. - In
-
The value of
Name
is one of the comma delimited strings inValue
. Only supported for text properties. - Contains
-
The value of
Name
contains the stringValue
. Only supported for text properties.A
SearchExpression
can include theContains
operator multiple times when the value ofName
is one of the following:-
Experiment.DisplayName
-
Experiment.ExperimentName
-
Experiment.Tags
-
Trial.DisplayName
-
Trial.TrialName
-
Trial.Tags
-
TrialComponent.DisplayName
-
TrialComponent.TrialComponentName
-
TrialComponent.Tags
-
TrialComponent.InputArtifacts
-
TrialComponent.OutputArtifacts
A
SearchExpression
can include only oneContains
operator for all other values ofName
. In these cases, if you include multipleContains
operators in theSearchExpression
, the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded.
" -
"Equals"
"NotEquals"
"GreaterThan"
"GreaterThanOrEqualTo"
"LessThan"
"LessThanOrEqualTo"
"Contains"
"Exists"
"NotExists"
"In"
Value
— (String
)A value used with
Name
andOperator
to determine which resources satisfy the filter's condition. For numerical properties,Value
must be an integer or floating-point decimal. For timestamp properties,Value
must be an ISO 8601 date-time string of the following format:YYYY-mm-dd'T'HH:MM:SS
.
Operator
— (String
)A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify
Possible values include:And
. If only a single conditional statement needs to be true for the entire search expression to be true, specifyOr
. The default value isAnd
."And"
"Or"
Operator
— (String
)A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify
Possible values include:And
. If only a single conditional statement needs to be true for the entire search expression to be true, specifyOr
. The default value isAnd
."And"
"Or"
SortBy
— (String
)The name of the resource property used to sort the
SearchResults
. The default isLastModifiedTime
.SortOrder
— (String
)How
Possible values include:SearchResults
are ordered. Valid values areAscending
orDescending
. The default isDescending
."Ascending"
"Descending"
NextToken
— (String
)If more than
MaxResults
resources match the specifiedSearchExpression
, the response includes aNextToken
. TheNextToken
can be passed to the nextSearchRequest
to continue retrieving results.MaxResults
— (Integer
)The maximum number of results to return.
CrossAccountFilterOption
— (String
)A cross account filter option. When the value is
Possible values include:"CrossAccount"
the search results will only include resources made discoverable to you from other accounts. When the value is"SameAccount"
ornull
the search results will only include resources from your account. Default isnull
. For more information on searching for resources made discoverable to your account, see Search discoverable resources in the SageMaker Developer Guide. The maximum number ofResourceCatalog
s viewable is 1000."SameAccount"
"CrossAccount"
VisibilityConditions
— (Array<map>
)Limits the results of your search request to the resources that you can access.
Key
— (String
)The key that specifies the tag that you're using to filter the search results. It must be in the following format:
Tags.<key>
.Value
— (String
)The value for the tag that you're using to filter the search results.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Results
— (Array<map>
)A list of
SearchRecord
objects.TrainingJob
— (map
)The properties of a training job.
TrainingJobName
— (String
)The name of the training job.
TrainingJobArn
— (String
)The Amazon Resource Name (ARN) of the training job.
TuningJobArn
— (String
)The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn
— (String
)The Amazon Resource Name (ARN) of the job.
ModelArtifacts
— (map
)Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts
— required — (String
)The path of the S3 object that contains the model artifacts. For example,
s3://bucket-name/keynameprefix/model.tar.gz
.
TrainingJobStatus
— (String
)The status of the training job.
Training job statuses are:
-
InProgress
- The training is in progress. -
Completed
- The training job has completed. -
Failed
- The training job has failed. To see the reason for the failure, see theFailureReason
field in the response to aDescribeTrainingJobResponse
call. -
Stopping
- The training job is stopping. -
Stopped
- The training job has stopped.
For more detailed information, see
Possible values include:SecondaryStatus
."InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
-
SecondaryStatus
— (String
)Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see
StatusMessage
under SecondaryStatusTransition.SageMaker provides primary statuses and secondary statuses that apply to each of them:
- InProgress
-
-
Starting
- Starting the training job. -
Downloading
- An optional stage for algorithms that supportFile
training input mode. It indicates that data is being downloaded to the ML storage volumes. -
Training
- Training is in progress. -
Uploading
- Training is complete and the model artifacts are being uploaded to the S3 location.
-
- Completed
-
-
Completed
- The training job has completed.
-
- Failed
-
-
Failed
- The training job has failed. The reason for the failure is returned in theFailureReason
field ofDescribeTrainingJobResponse
.
-
- Stopped
-
-
MaxRuntimeExceeded
- The job stopped because it exceeded the maximum allowed runtime. -
Stopped
- The training job has stopped.
-
- Stopping
-
-
Stopping
- Stopping the training job.
-
Valid values for
SecondaryStatus
are subject to change.We no longer support the following secondary statuses:
-
LaunchingMLInstances
-
PreparingTrainingStack
-
DownloadingTrainingImage
"Starting"
"LaunchingMLInstances"
"PreparingTrainingStack"
"Downloading"
"DownloadingTrainingImage"
"Training"
"Uploading"
"Stopping"
"Stopped"
"MaxRuntimeExceeded"
"Completed"
"Failed"
"Interrupted"
"MaxWaitTimeExceeded"
"Updating"
"Restarting"
"Pending"
FailureReason
— (String
)If the training job failed, the reason it failed.
HyperParameters
— (map<String>
)Algorithm-specific parameters.
AlgorithmSpecification
— (map
)Information about the algorithm used for training, and algorithm metadata.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. For more information, see the note in theAlgorithmName
parameter description.AlgorithmName
— (String
)The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. Note that theAlgorithmName
parameter is mutually exclusive with theTrainingImage
parameter. If you specify a value for theAlgorithmName
parameter, you can't specify a value forTrainingImage
, and vice versa. If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise anull
error.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
MetricDefinitions
— (Array<map>
)A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
EnableSageMakerMetricsTimeSeries
— (Boolean
)To generate and save time-series metrics during training, set to
true
. The default isfalse
and time-series metrics aren't generated except in the following cases:-
You use one of the SageMaker built-in algorithms
-
You use one of the following Prebuilt SageMaker Docker Images:
-
Tensorflow (version >= 1.15)
-
MXNet (version >= 1.6)
-
PyTorch (version >= 1.3)
-
-
You specify at least one MetricDefinition
-
ContainerEntrypoint
— (Array<String>
)The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
ContainerArguments
— (Array<String>
)The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
TrainingImageConfig
— (map
)The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode
— required — (String
)The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to
Possible values include:Vpc
."Platform"
"Vpc"
TrainingRepositoryAuthConfig
— (map
)An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn
— (String
)The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig
— (Array<map>
)An array of
Channel
objects that describes each data input channel.Your input must be in the same Amazon Web Services region as your training job.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
OutputDataConfig
— (map
)The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
VpcConfig
— (map
)A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
StoppingCondition
— (map
)Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the
SIGTERM
signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
CreationTime
— (Date
)A timestamp that indicates when the training job was created.
TrainingStartTime
— (Date
)Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of
TrainingEndTime
. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.TrainingEndTime
— (Date
)Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of
TrainingStartTime
and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.LastModifiedTime
— (Date
)A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions
— (Array<map>
)A history of all of the secondary statuses that the training job has transitioned through.
Status
— required — (String
)Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
- InProgress
-
-
Starting
- Starting the training job. -
Downloading
- An optional stage for algorithms that supportFile
training input mode. It indicates that data is being downloaded to the ML storage volumes. -
Training
- Training is in progress. -
Uploading
- Training is complete and the model artifacts are being uploaded to the S3 location.
-
- Completed
-
-
Completed
- The training job has completed.
-
- Failed
-
-
Failed
- The training job has failed. The reason for the failure is returned in theFailureReason
field ofDescribeTrainingJobResponse
.
-
- Stopped
-
-
MaxRuntimeExceeded
- The job stopped because it exceeded the maximum allowed runtime. -
Stopped
- The training job has stopped.
-
- Stopping
-
-
Stopping
- Stopping the training job.
-
We no longer support the following secondary statuses:
-
LaunchingMLInstances
-
PreparingTrainingStack
-
DownloadingTrainingImage
"Starting"
"LaunchingMLInstances"
"PreparingTrainingStack"
"Downloading"
"DownloadingTrainingImage"
"Training"
"Uploading"
"Stopping"
"Stopped"
"MaxRuntimeExceeded"
"Completed"
"Failed"
"Interrupted"
"MaxWaitTimeExceeded"
"Updating"
"Restarting"
"Pending"
StartTime
— required — (Date
)A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime
— (Date
)A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage
— (String
)A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
- Starting
-
-
Starting the training job.
-
Launching requested ML instances.
-
Insufficient capacity error from EC2 while launching instances, retrying!
-
Launched instance was unhealthy, replacing it!
-
Preparing the instances for training.
-
- Training
-
-
Training image download completed. Training in progress.
-
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view
TrainingJobStatus
andSecondaryStatus
in DescribeTrainingJob, andStatusMessage
together. For example, at the start of a training job, you might see the following:-
TrainingJobStatus
- InProgress -
SecondaryStatus
- Training -
StatusMessage
- Downloading the training image
FinalMetricDataList
— (Array<map>
)A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
MetricName
— (String
)The name of the metric.
Value
— (Float
)The value of the metric.
Timestamp
— (Date
)The date and time that the algorithm emitted the metric.
EnableNetworkIsolation
— (Boolean
)If the
TrainingJob
was created with network isolation, the value is set totrue
. If network isolation is enabled, nodes can't communicate beyond the VPC they run in.EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.EnableManagedSpotTraining
— (Boolean
)When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training.
CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
TrainingTimeInSeconds
— (Integer
)The training time in seconds.
BillableTimeInSeconds
— (Integer
)The billable time in seconds.
DebugHookConfig
— (map
)Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the
DebugHookConfig
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.LocalPath
— (String
)Path to local storage location for metrics and tensors. Defaults to
/opt/ml/output/tensors/
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for metrics and tensors.
HookParameters
— (map<String>
)Configuration information for the Amazon SageMaker Debugger hook parameters.
CollectionConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the
CollectionConfiguration
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.CollectionName
— (String
)The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters
— (map<String>
)Parameter values for the tensor collection. The allowed parameters are
"name"
,"include_regex"
,"reduction_config"
,"save_config"
,"tensor_names"
, and"save_histogram"
.
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
DebugRuleConfigurations
— (Array<map>
)Information about the debug rule configuration.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a custom rule for debugging a training job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
TensorBoardOutputConfig
— (map
)Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath
— (String
)Path to local storage location for tensorBoard output. Defaults to
/opt/ml/output/tensorboard
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses
— (Array<map>
)Information about the evaluation status of the rules for the training job.
RuleConfigurationName
— (String
)The name of the rule configuration.
RuleEvaluationJobArn
— (String
)The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus
— (String
)Status of the rule evaluation.
Possible values include:"InProgress"
"NoIssuesFound"
"IssuesFound"
"Error"
"Stopping"
"Stopped"
StatusDetails
— (String
)Details from the rule evaluation.
LastModifiedTime
— (Date
)Timestamp when the rule evaluation status was last modified.
ProfilerConfig
— (map
)Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath
— (String
)Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds
— (Integer
)A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters
— (map<String>
)Configuration information for capturing framework metrics. Available key strings for different profiling options are
DetailedProfilingConfig
,PythonProfilingConfig
, andDataLoaderProfilingConfig
. The following codes are configuration structures for theProfilingParameters
parameter. To learn more about how to configure theProfilingParameters
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.DisableProfiler
— (Boolean
)Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to
True
.
Environment
— (map<String>
)The environment variables to set in the Docker container.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Experiment
— (map
)The properties of an experiment.
ExperimentName
— (String
)The name of the experiment.
ExperimentArn
— (String
)The Amazon Resource Name (ARN) of the experiment.
DisplayName
— (String
)The name of the experiment as displayed. If
DisplayName
isn't specified,ExperimentName
is displayed.Source
— (map
)The source of the experiment.
SourceArn
— required — (String
)The Amazon Resource Name (ARN) of the source.
SourceType
— (String
)The source type.
Description
— (String
)The description of the experiment.
CreationTime
— (Date
)When the experiment was created.
CreatedBy
— (map
)Who created the experiment.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)When the experiment was last modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
Tags
— (Array<map>
)The list of tags that are associated with the experiment. You can use Search API to search on the tags.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Trial
— (map
)The properties of a trial.
TrialName
— (String
)The name of the trial.
TrialArn
— (String
)The Amazon Resource Name (ARN) of the trial.
DisplayName
— (String
)The name of the trial as displayed. If
DisplayName
isn't specified,TrialName
is displayed.ExperimentName
— (String
)The name of the experiment the trial is part of.
Source
— (map
)The source of the trial.
SourceArn
— required — (String
)The Amazon Resource Name (ARN) of the source.
SourceType
— (String
)The source job type.
CreationTime
— (Date
)When the trial was created.
CreatedBy
— (map
)Who created the trial.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)Who last modified the trial.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
Tags
— (Array<map>
)The list of tags that are associated with the trial. You can use Search API to search on the tags.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
TrialComponentSummaries
— (Array<map>
)A list of the components associated with the trial. For each component, a summary of the component's properties is included.
TrialComponentName
— (String
)The name of the trial component.
TrialComponentArn
— (String
)The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource
— (map
)The Amazon Resource Name (ARN) and job type of the source of a trial component.
SourceArn
— required — (String
)The source Amazon Resource Name (ARN).
SourceType
— (String
)The source job type.
CreationTime
— (Date
)When the component was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
TrialComponent
— (map
)The properties of a trial component.
TrialComponentName
— (String
)The name of the trial component.
DisplayName
— (String
)The name of the component as displayed. If
DisplayName
isn't specified,TrialComponentName
is displayed.TrialComponentArn
— (String
)The Amazon Resource Name (ARN) of the trial component.
Source
— (map
)The Amazon Resource Name (ARN) and job type of the source of the component.
SourceArn
— required — (String
)The source Amazon Resource Name (ARN).
SourceType
— (String
)The source job type.
Status
— (map
)The status of the trial component.
PrimaryStatus
— (String
)The status of the trial component.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
Message
— (String
)If the component failed, a message describing why.
StartTime
— (Date
)When the component started.
EndTime
— (Date
)When the component ended.
CreationTime
— (Date
)When the component was created.
CreatedBy
— (map
)Who created the trial component.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)When the component was last modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
Parameters
— (map<map>
)The hyperparameters of the component.
StringValue
— (String
)The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the
NumberValue
parameter.NumberValue
— (Float
)The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the
StringValue
parameter.
InputArtifacts
— (map<map>
)The input artifacts of the component.
MediaType
— (String
)The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value
— required — (String
)The location of the artifact.
OutputArtifacts
— (map<map>
)The output artifacts of the component.
MediaType
— (String
)The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value
— required — (String
)The location of the artifact.
Metrics
— (Array<map>
)The metrics for the component.
MetricName
— (String
)The name of the metric.
SourceArn
— (String
)The Amazon Resource Name (ARN) of the source.
TimeStamp
— (Date
)When the metric was last updated.
Max
— (Float
)The maximum value of the metric.
Min
— (Float
)The minimum value of the metric.
Last
— (Float
)The most recent value of the metric.
Count
— (Integer
)The number of samples used to generate the metric.
Avg
— (Float
)The average value of the metric.
StdDev
— (Float
)The standard deviation of the metric.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
SourceDetail
— (map
)Details of the source of the component.
SourceArn
— (String
)The Amazon Resource Name (ARN) of the source.
TrainingJob
— (map
)Information about a training job that's the source of a trial component.
TrainingJobName
— (String
)The name of the training job.
TrainingJobArn
— (String
)The Amazon Resource Name (ARN) of the training job.
TuningJobArn
— (String
)The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn
— (String
)The Amazon Resource Name (ARN) of the job.
ModelArtifacts
— (map
)Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts
— required — (String
)The path of the S3 object that contains the model artifacts. For example,
s3://bucket-name/keynameprefix/model.tar.gz
.
TrainingJobStatus
— (String
)The status of the training job.
Training job statuses are:
-
InProgress
- The training is in progress. -
Completed
- The training job has completed. -
Failed
- The training job has failed. To see the reason for the failure, see theFailureReason
field in the response to aDescribeTrainingJobResponse
call. -
Stopping
- The training job is stopping. -
Stopped
- The training job has stopped.
For more detailed information, see
Possible values include:SecondaryStatus
."InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
-
SecondaryStatus
— (String
)Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see
StatusMessage
under SecondaryStatusTransition.SageMaker provides primary statuses and secondary statuses that apply to each of them:
- InProgress
-
-
Starting
- Starting the training job. -
Downloading
- An optional stage for algorithms that supportFile
training input mode. It indicates that data is being downloaded to the ML storage volumes. -
Training
- Training is in progress. -
Uploading
- Training is complete and the model artifacts are being uploaded to the S3 location.
-
- Completed
-
-
Completed
- The training job has completed.
-
- Failed
-
-
Failed
- The training job has failed. The reason for the failure is returned in theFailureReason
field ofDescribeTrainingJobResponse
.
-
- Stopped
-
-
MaxRuntimeExceeded
- The job stopped because it exceeded the maximum allowed runtime. -
Stopped
- The training job has stopped.
-
- Stopping
-
-
Stopping
- Stopping the training job.
-
Valid values for
SecondaryStatus
are subject to change.We no longer support the following secondary statuses:
-
LaunchingMLInstances
-
PreparingTrainingStack
-
DownloadingTrainingImage
"Starting"
"LaunchingMLInstances"
"PreparingTrainingStack"
"Downloading"
"DownloadingTrainingImage"
"Training"
"Uploading"
"Stopping"
"Stopped"
"MaxRuntimeExceeded"
"Completed"
"Failed"
"Interrupted"
"MaxWaitTimeExceeded"
"Updating"
"Restarting"
"Pending"
FailureReason
— (String
)If the training job failed, the reason it failed.
HyperParameters
— (map<String>
)Algorithm-specific parameters.
AlgorithmSpecification
— (map
)Information about the algorithm used for training, and algorithm metadata.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. For more information, see the note in theAlgorithmName
parameter description.AlgorithmName
— (String
)The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. Note that theAlgorithmName
parameter is mutually exclusive with theTrainingImage
parameter. If you specify a value for theAlgorithmName
parameter, you can't specify a value forTrainingImage
, and vice versa. If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise anull
error.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
MetricDefinitions
— (Array<map>
)A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
EnableSageMakerMetricsTimeSeries
— (Boolean
)To generate and save time-series metrics during training, set to
true
. The default isfalse
and time-series metrics aren't generated except in the following cases:-
You use one of the SageMaker built-in algorithms
-
You use one of the following Prebuilt SageMaker Docker Images:
-
Tensorflow (version >= 1.15)
-
MXNet (version >= 1.6)
-
PyTorch (version >= 1.3)
-
-
You specify at least one MetricDefinition
-
ContainerEntrypoint
— (Array<String>
)The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
ContainerArguments
— (Array<String>
)The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
TrainingImageConfig
— (map
)The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode
— required — (String
)The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to
Possible values include:Vpc
."Platform"
"Vpc"
TrainingRepositoryAuthConfig
— (map
)An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn
— (String
)The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig
— (Array<map>
)An array of
Channel
objects that describes each data input channel.Your input must be in the same Amazon Web Services region as your training job.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
OutputDataConfig
— (map
)The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
VpcConfig
— (map
)A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
StoppingCondition
— (map
)Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the
SIGTERM
signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
CreationTime
— (Date
)A timestamp that indicates when the training job was created.
TrainingStartTime
— (Date
)Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of
TrainingEndTime
. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.TrainingEndTime
— (Date
)Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of
TrainingStartTime
and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.LastModifiedTime
— (Date
)A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions
— (Array<map>
)A history of all of the secondary statuses that the training job has transitioned through.
Status
— required — (String
)Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
- InProgress
-
-
Starting
- Starting the training job. -
Downloading
- An optional stage for algorithms that supportFile
training input mode. It indicates that data is being downloaded to the ML storage volumes. -
Training
- Training is in progress. -
Uploading
- Training is complete and the model artifacts are being uploaded to the S3 location.
-
- Completed
-
-
Completed
- The training job has completed.
-
- Failed
-
-
Failed
- The training job has failed. The reason for the failure is returned in theFailureReason
field ofDescribeTrainingJobResponse
.
-
- Stopped
-
-
MaxRuntimeExceeded
- The job stopped because it exceeded the maximum allowed runtime. -
Stopped
- The training job has stopped.
-
- Stopping
-
-
Stopping
- Stopping the training job.
-
We no longer support the following secondary statuses:
-
LaunchingMLInstances
-
PreparingTrainingStack
-
DownloadingTrainingImage
"Starting"
"LaunchingMLInstances"
"PreparingTrainingStack"
"Downloading"
"DownloadingTrainingImage"
"Training"
"Uploading"
"Stopping"
"Stopped"
"MaxRuntimeExceeded"
"Completed"
"Failed"
"Interrupted"
"MaxWaitTimeExceeded"
"Updating"
"Restarting"
"Pending"
StartTime
— required — (Date
)A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime
— (Date
)A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage
— (String
)A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
- Starting
-
-
Starting the training job.
-
Launching requested ML instances.
-
Insufficient capacity error from EC2 while launching instances, retrying!
-
Launched instance was unhealthy, replacing it!
-
Preparing the instances for training.
-
- Training
-
-
Training image download completed. Training in progress.
-
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view
TrainingJobStatus
andSecondaryStatus
in DescribeTrainingJob, andStatusMessage
together. For example, at the start of a training job, you might see the following:-
TrainingJobStatus
- InProgress -
SecondaryStatus
- Training -
StatusMessage
- Downloading the training image
FinalMetricDataList
— (Array<map>
)A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
MetricName
— (String
)The name of the metric.
Value
— (Float
)The value of the metric.
Timestamp
— (Date
)The date and time that the algorithm emitted the metric.
EnableNetworkIsolation
— (Boolean
)If the
TrainingJob
was created with network isolation, the value is set totrue
. If network isolation is enabled, nodes can't communicate beyond the VPC they run in.EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.EnableManagedSpotTraining
— (Boolean
)When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training.
CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
TrainingTimeInSeconds
— (Integer
)The training time in seconds.
BillableTimeInSeconds
— (Integer
)The billable time in seconds.
DebugHookConfig
— (map
)Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the
DebugHookConfig
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.LocalPath
— (String
)Path to local storage location for metrics and tensors. Defaults to
/opt/ml/output/tensors/
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for metrics and tensors.
HookParameters
— (map<String>
)Configuration information for the Amazon SageMaker Debugger hook parameters.
CollectionConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the
CollectionConfiguration
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.CollectionName
— (String
)The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters
— (map<String>
)Parameter values for the tensor collection. The allowed parameters are
"name"
,"include_regex"
,"reduction_config"
,"save_config"
,"tensor_names"
, and"save_histogram"
.
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
DebugRuleConfigurations
— (Array<map>
)Information about the debug rule configuration.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a custom rule for debugging a training job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
TensorBoardOutputConfig
— (map
)Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath
— (String
)Path to local storage location for tensorBoard output. Defaults to
/opt/ml/output/tensorboard
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses
— (Array<map>
)Information about the evaluation status of the rules for the training job.
RuleConfigurationName
— (String
)The name of the rule configuration.
RuleEvaluationJobArn
— (String
)The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus
— (String
)Status of the rule evaluation.
Possible values include:"InProgress"
"NoIssuesFound"
"IssuesFound"
"Error"
"Stopping"
"Stopped"
StatusDetails
— (String
)Details from the rule evaluation.
LastModifiedTime
— (Date
)Timestamp when the rule evaluation status was last modified.
ProfilerConfig
— (map
)Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath
— (String
)Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds
— (Integer
)A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters
— (map<String>
)Configuration information for capturing framework metrics. Available key strings for different profiling options are
DetailedProfilingConfig
,PythonProfilingConfig
, andDataLoaderProfilingConfig
. The following codes are configuration structures for theProfilingParameters
parameter. To learn more about how to configure theProfilingParameters
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.DisableProfiler
— (Boolean
)Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to
True
.
Environment
— (map<String>
)The environment variables to set in the Docker container.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ProcessingJob
— (map
)Information about a processing job that's the source of a trial component.
ProcessingInputs
— (Array<map>
)List of input configurations for the processing job.
InputName
— required — (String
)The name for the processing job input.
AppManaged
— (Boolean
)When
True
, input operations such as data download are managed natively by the processing job application. WhenFalse
(default), input operations are managed by Amazon SageMaker.S3Input
— (map
)Configuration for downloading input data from Amazon S3 into the processing container.
S3Uri
— required — (String
)The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
LocalPath
— (String
)The local path in your container where you want Amazon SageMaker to write input data to.
LocalPath
is an absolute path to the input data and must begin with/opt/ml/processing/
.LocalPath
is a required parameter whenAppManaged
isFalse
(default).S3DataType
— required — (String
)Whether you use an
Possible values include:S3Prefix
or aManifestFile
for the data type. If you chooseS3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you chooseManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job."ManifestFile"
"S3Prefix"
S3InputMode
— (String
)Whether to use
Possible values include:File
orPipe
input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. InPipe
mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume."Pipe"
"File"
S3DataDistributionType
— (String
)Whether to distribute the data from Amazon S3 to all processing instances with
Possible values include:FullyReplicated
, or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance."FullyReplicated"
"ShardedByS3Key"
S3CompressionType
— (String
)Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container.
Possible values include:Gzip
can only be used whenPipe
mode is specified as theS3InputMode
. InPipe
mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume."None"
"Gzip"
DatasetDefinition
— (map
)Configuration for a Dataset Definition input.
AthenaDatasetDefinition
— (map
)Configuration for Athena Dataset Definition input.
Catalog
— required — (String
)The name of the data catalog used in Athena query execution.
Database
— required — (String
)The name of the database used in the Athena query execution.
QueryString
— required — (String
)The SQL query statements, to be executed.
WorkGroup
— (String
)The name of the workgroup in which the Athena query is being started.
OutputS3Uri
— required — (String
)The location in Amazon S3 where Athena query results are stored.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat
— required — (String
)The data storage format for Athena query results.
Possible values include:"PARQUET"
"ORC"
"AVRO"
"JSON"
"TEXTFILE"
OutputCompression
— (String
)The compression used for Athena query results.
Possible values include:"GZIP"
"SNAPPY"
"ZLIB"
RedshiftDatasetDefinition
— (map
)Configuration for Redshift Dataset Definition input.
ClusterId
— required — (String
)The Redshift cluster Identifier.
Database
— required — (String
)The name of the Redshift database used in Redshift query execution.
DbUser
— required — (String
)The database user name used in Redshift query execution.
QueryString
— required — (String
)The SQL query statements to be executed.
ClusterRoleArn
— required — (String
)The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri
— required — (String
)The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat
— required — (String
)The data storage format for Redshift query results.
Possible values include:"PARQUET"
"CSV"
OutputCompression
— (String
)The compression used for Redshift query results.
Possible values include:"None"
"GZIP"
"BZIP2"
"ZSTD"
"SNAPPY"
LocalPath
— (String
)The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job.
LocalPath
is an absolute path to the input data. This is a required parameter whenAppManaged
isFalse
(default).DataDistributionType
— (String
)Whether the generated dataset is
Possible values include:FullyReplicated
orShardedByS3Key
(default)."FullyReplicated"
"ShardedByS3Key"
InputMode
— (String
)Whether to use
Possible values include:File
orPipe
input mode. InFile
(default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. InPipe
mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume."Pipe"
"File"
ProcessingOutputConfig
— (map
)Configuration for uploading output from the processing container.
Outputs
— required — (Array<map>
)An array of outputs configuring the data to upload from the processing container.
OutputName
— required — (String
)The name for the processing job output.
S3Output
— (map
)Configuration for processing job outputs in Amazon S3.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath
— (String
)The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3.
LocalPath
is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.S3UploadMode
— required — (String
)Whether to upload the results of the processing job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
FeatureStoreOutput
— (map
)Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when
AppManaged
is specified.FeatureGroupName
— required — (String
)The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
AppManaged
— (Boolean
)When
True
, output operations such as data upload are managed natively by the processing job application. WhenFalse
(default), output operations are managed by Amazon SageMaker.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output.
KmsKeyId
can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. TheKmsKeyId
is applied to all outputs.
ProcessingJobName
— (String
)The name of the processing job.
ProcessingResources
— (map
)Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig
— required — (map
)The configuration for the resources in a cluster used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request aVolumeSizeInGB
greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.
StoppingCondition
— (map
)Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.
MaxRuntimeInSeconds
— required — (Integer
)Specifies the maximum runtime in seconds.
AppSpecification
— (map
)Configuration to run a processing job in a specified container image.
ImageUri
— required — (String
)The container image to be run by the processing job.
ContainerEntrypoint
— (Array<String>
)The entrypoint for a container used to run a processing job.
ContainerArguments
— (Array<String>
)The arguments for a container used to run a processing job.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
NetworkConfig
— (map
)Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The ARN of the role used to create the processing job.
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
ProcessingJobArn
— (String
)The ARN of the processing job.
ProcessingJobStatus
— (String
)The status of the processing job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
ExitMessage
— (String
)A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
FailureReason
— (String
)A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ProcessingEndTime
— (Date
)The time that the processing job ended.
ProcessingStartTime
— (Date
)The time that the processing job started.
LastModifiedTime
— (Date
)The time the processing job was last modified.
CreationTime
— (Date
)The time the processing job was created.
MonitoringScheduleArn
— (String
)The ARN of a monitoring schedule for an endpoint associated with this processing job.
AutoMLJobArn
— (String
)The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
TrainingJobArn
— (String
)The ARN of the training job associated with this processing job.
Tags
— (Array<map>
)An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
TransformJob
— (map
)Information about a transform job that's the source of a trial component.
TransformJobName
— (String
)The name of the transform job.
TransformJobArn
— (String
)The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus
— (String
)The status of the transform job.
Transform job statuses are:
-
InProgress
- The job is in progress. -
Completed
- The job has completed. -
Failed
- The transform job has failed. To see the reason for the failure, see theFailureReason
field in the response to aDescribeTransformJob
call. -
Stopping
- The transform job is stopping. -
Stopped
- The transform job has stopped.
"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
-
FailureReason
— (String
)If the transform job failed, the reason it failed.
ModelName
— (String
)The name of the model associated with the transform job.
MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. If
MaxConcurrentTransforms
is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value forMaxConcurrentTransforms
.ModelClientConfig
— (map
)Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds
— (Integer
)The timeout value in seconds for an invocation request. The default value is 600.
InvocationsMaxRetries
— (Integer
)The maximum number of retries when invocation requests are failing. The default value is 3.
MaxPayloadInMB
— (Integer
)The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in
MaxPayloadInMB
must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.BatchStrategy
— (String
)Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
Possible values include:"MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— (map
)Describes the input source of a transform job and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— (map
)Describes the results of a transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
DataCaptureConfig
— (map
)Configuration to control how SageMaker captures inference data for batch transform jobs.
DestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
GenerateInferenceId
— (Boolean
)Flag that indicates whether to append inference id to the output.
TransformResources
— (map
)Describes the resources, including ML instance types and ML instance count, to use for transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. The default value is
1
, and the maximum is100
. For distributed transform jobs, specify a value greater than1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
CreationTime
— (Date
)A timestamp that shows when the transform Job was created.
TransformStartTime
— (Date
)Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of
TransformEndTime
.TransformEndTime
— (Date
)Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of
TransformStartTime
.LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the labeling job that created the transform job.
AutoMLJobArn
— (String
)The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing
— (map
)The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
InputFilter
— (String
)A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
InputFilter
parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value$
.Examples:
"$"
,"$[1:]"
,"$.features"
OutputFilter
— (String
)A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value,
$
. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.Examples:
"$"
,"$[0,5:]"
,"$['id','SageMakerOutput']"
JoinSource
— (String
)Specifies the source of the data to join with the transformed data. The valid values are
None
andInput
. The default value isNone
, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, setJoinSource
toInput
. You can specifyOutputFilter
as an additional filter to select a portion of the joined dataset and store it in the output file.For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called
SageMakerOutput
. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under theSageMakerInput
key and the results are stored inSageMakerOutput
.For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
Possible values include:"Input"
"None"
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
Tags
— (Array<map>
)A list of tags associated with the transform job.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
LineageGroupArn
— (String
)The Amazon Resource Name (ARN) of the lineage group resource.
Tags
— (Array<map>
)The list of tags that are associated with the component. You can use Search API to search on the tags.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Parents
— (Array<map>
)An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.
TrialName
— (String
)The name of the trial.
ExperimentName
— (String
)The name of the experiment.
RunName
— (String
)The name of the experiment run.
Endpoint
— (map
)A hosted endpoint for real-time inference.
EndpointName
— required — (String
)The name of the endpoint.
EndpointArn
— required — (String
)The Amazon Resource Name (ARN) of the endpoint.
EndpointConfigName
— required — (String
)The endpoint configuration associated with the endpoint.
ProductionVariants
— (Array<map>
)A list of the production variants hosted on the endpoint. Each production variant is a model.
VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight, as specified in the
UpdateEndpointWeightsAndCapacities
request.CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in the
UpdateEndpointWeightsAndCapacities
request.VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for the endpoint update.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
DataCaptureConfig
— (map
)The currently active data capture configuration used by your Endpoint.
EnableCapture
— required — (Boolean
)Whether data capture is enabled or disabled.
CaptureStatus
— required — (String
)Whether data capture is currently functional.
Possible values include:"Started"
"Stopped"
CurrentSamplingPercentage
— required — (Integer
)The percentage of requests being captured by your Endpoint.
DestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
KmsKeyId
— required — (String
)The KMS key being used to encrypt the data in Amazon S3.
EndpointStatus
— required — (String
)The status of the endpoint.
Possible values include:"OutOfService"
"Creating"
"Updating"
"SystemUpdating"
"RollingBack"
"InService"
"Deleting"
"Failed"
"UpdateRollbackFailed"
FailureReason
— (String
)If the endpoint failed, the reason it failed.
CreationTime
— required — (Date
)The time that the endpoint was created.
LastModifiedTime
— required — (Date
)The last time the endpoint was modified.
MonitoringSchedules
— (Array<map>
)A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor.
MonitoringScheduleArn
— (String
)The Amazon Resource Name (ARN) of the monitoring schedule.
MonitoringScheduleName
— (String
)The name of the monitoring schedule.
MonitoringScheduleStatus
— (String
)The status of the monitoring schedule. This can be one of the following values.
-
PENDING
- The schedule is pending being created. -
FAILED
- The schedule failed. -
SCHEDULED
- The schedule was successfully created. -
STOPPED
- The schedule was stopped.
"Pending"
"Failed"
"Scheduled"
"Stopped"
-
MonitoringType
— (String
)The type of the monitoring job definition to schedule.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
FailureReason
— (String
)If the monitoring schedule failed, the reason it failed.
CreationTime
— (Date
)The time that the monitoring schedule was created.
LastModifiedTime
— (Date
)The last time the monitoring schedule was changed.
MonitoringScheduleConfig
— (map
)Configures the monitoring schedule and defines the monitoring job.
ScheduleConfig
— (map
)Configures the monitoring schedule.
ScheduleExpression
— required — (String
)A cron expression that describes details about the monitoring schedule.
The supported cron expressions are:
-
If you want to set the job to start every hour, use the following:
Hourly: cron(0 * ? * * *)
-
If you want to start the job daily:
cron(0 [00-23] ? * * *)
-
If you want to run the job one time, immediately, use the following keyword:
NOW
For example, the following are valid cron expressions:
-
Daily at noon UTC:
cron(0 12 ? * * *)
-
Daily at midnight UTC:
cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
-
Every 12 hours, starting at 5pm UTC:
cron(0 17/12 ? * * *)
-
Every two hours starting at midnight:
cron(0 0/2 ? * * *)
Note:- Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
- We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
You can also specify the keyword
NOW
to run the monitoring job immediately, one time, without recurring.-
DataAnalysisStartTime
— (String
)Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the
ScheduleExpression
parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify:"-PT5H"
.The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the
DataAnalysisEndTime
parameter.If you set
ScheduleExpression
toNOW
, this parameter is required.DataAnalysisEndTime
— (String
)Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the
ScheduleExpression
parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify:"-PT1H"
.The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the
DataAnalysisStartTime
parameter.If you set
ScheduleExpression
toNOW
, this parameter is required.
MonitoringJobDefinition
— (map
)Defines the monitoring job.
BaselineConfig
— (map
)Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName
— (String
)The name of the job that performs baselining for the monitoring job.
ConstraintsResource
— (map
)The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
StatisticsResource
— (map
)The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri
— (String
)The Amazon S3 URI for the statistics resource.
MonitoringInputs
— required — (Array<map>
)The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
EndpointInput
— (map
)The endpoint for a monitoring job.
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
MonitoringOutputConfig
— required — (map
)The array of outputs from the monitoring job to be uploaded to Amazon S3.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources
— required — (map
)Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification
— required — (map
)Configures the monitoring job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the monitoring job.
ContainerEntrypoint
— (Array<String>
)Specifies the entrypoint for a container used to run the monitoring job.
ContainerArguments
— (Array<String>
)An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition
— (map
)Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
NetworkConfig
— (map
)Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job definition to schedule.
MonitoringType
— (String
)The type of the monitoring job definition to schedule.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
EndpointName
— (String
)The endpoint that hosts the model being monitored.
LastMonitoringExecutionSummary
— (map
)Summary of information about the last monitoring job to run.
MonitoringScheduleName
— required — (String
)The name of the monitoring schedule.
ScheduledTime
— required — (Date
)The time the monitoring job was scheduled.
CreationTime
— required — (Date
)The time at which the monitoring job was created.
LastModifiedTime
— required — (Date
)A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus
— required — (String
)The status of the monitoring job.
Possible values include:"Pending"
"Completed"
"CompletedWithViolations"
"InProgress"
"Failed"
"Stopping"
"Stopped"
ProcessingJobArn
— (String
)The Amazon Resource Name (ARN) of the monitoring job.
EndpointName
— (String
)The name of the endpoint used to run the monitoring job.
FailureReason
— (String
)Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job.
MonitoringType
— (String
)The type of the monitoring job.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
Tags
— (Array<map>
)A list of the tags associated with the monitoring schedlue. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Tags
— (Array<map>
)A list of the tags associated with the endpoint. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ShadowProductionVariants
— (Array<map>
)A list of the shadow variants hosted on the endpoint. Each shadow variant is a model in shadow mode with production traffic replicated from the production variant.
VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight, as specified in the
UpdateEndpointWeightsAndCapacities
request.CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in the
UpdateEndpointWeightsAndCapacities
request.VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for the endpoint update.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
ModelPackage
— (map
)A versioned model that can be deployed for SageMaker inference.
ModelPackageName
— (String
)The name of the model.
ModelPackageGroupName
— (String
)The model group to which the model belongs.
ModelPackageVersion
— (Integer
)The version number of a versioned model.
ModelPackageArn
— (String
)The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription
— (String
)The description of the model package.
CreationTime
— (Date
)The time that the model package was created.
InferenceSpecification
— (map
)Defines how to perform inference generation after a training job is run.
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
SourceAlgorithmSpecification
— (map
)A list of algorithms that were used to create a model package.
SourceAlgorithms
— required — (Array<map>
)A list of the algorithms that were used to create a model package.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the algorithm.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
AlgorithmName
— required — (String
)The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ValidationSpecification
— (map
)Specifies batch transform jobs that SageMaker runs to validate your model package.
ValidationRole
— required — (String
)The IAM roles to be used for the validation of the model package.
ValidationProfiles
— required — (Array<map>
)An array of
ModelPackageValidationProfile
objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.ProfileName
— required — (String
)The name of the profile for the model package.
TransformJobDefinition
— required — (map
)The
TransformJobDefinition
object that describes the transform job used for the validation of the model package.MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB
— (Integer
)The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy
— (String
)A string that determines the number of records included in a single mini-batch.
SingleRecord
means only one record is used per mini-batch.MultiRecord
means a mini-batch is set to contain as many records that can fit within theMaxPayloadInMB
limit."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— required — (map
)A description of the input source and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— required — (map
)Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
TransformResources
— required — (map
)Identifies the ML compute instances for the transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. The default value is
1
, and the maximum is100
. For distributed transform jobs, specify a value greater than1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
ModelPackageStatus
— (String
)The status of the model package. This can be one of the following values.
-
PENDING
- The model package is pending being created. -
IN_PROGRESS
- The model package is in the process of being created. -
COMPLETED
- The model package was successfully created. -
FAILED
- The model package failed. -
DELETING
- The model package is in the process of being deleted.
"Pending"
"InProgress"
"Completed"
"Failed"
"Deleting"
-
ModelPackageStatusDetails
— (map
)Specifies the validation and image scan statuses of the model package.
ValidationStatuses
— required — (Array<map>
)The validation status of the model package.
Name
— required — (String
)The name of the model package for which the overall status is being reported.
Status
— required — (String
)The current status.
Possible values include:"NotStarted"
"InProgress"
"Completed"
"Failed"
FailureReason
— (String
)if the overall status is
Failed
, the reason for the failure.
ImageScanStatuses
— (Array<map>
)The status of the scan of the Docker image container for the model package.
Name
— required — (String
)The name of the model package for which the overall status is being reported.
Status
— required — (String
)The current status.
Possible values include:"NotStarted"
"InProgress"
"Completed"
"Failed"
FailureReason
— (String
)if the overall status is
Failed
, the reason for the failure.
CertifyForMarketplace
— (Boolean
)Whether the model package is to be certified to be listed on Amazon Web Services Marketplace. For information about listing model packages on Amazon Web Services Marketplace, see List Your Algorithm or Model Package on Amazon Web Services Marketplace.
ModelApprovalStatus
— (String
)The approval status of the model. This can be one of the following values.
-
APPROVED
- The model is approved -
REJECTED
- The model is rejected. -
PENDING_MANUAL_APPROVAL
- The model is waiting for manual approval.
"Approved"
"Rejected"
"PendingManualApproval"
-
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
MetadataProperties
— (map
)Metadata properties of the tracking entity, trial, or trial component.
CommitId
— (String
)The commit ID.
Repository
— (String
)The repository.
GeneratedBy
— (String
)The entity this entity was generated by.
ProjectId
— (String
)The project ID.
ModelMetrics
— (map
)Metrics for the model.
ModelQuality
— (map
)Metrics that measure the quality of a model.
Statistics
— (map
)Model quality statistics.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)Model quality constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
ModelDataQuality
— (map
)Metrics that measure the quality of the input data for a model.
Statistics
— (map
)Data quality statistics for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)Data quality constraints for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Bias
— (map
)Metrics that measure bias in a model.
Report
— (map
)The bias report for a model
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
PreTrainingReport
— (map
)The pre-training bias report for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
PostTrainingReport
— (map
)The post-training bias report for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Explainability
— (map
)Metrics that help explain a model.
Report
— (map
)The explainability report for a model.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
LastModifiedTime
— (Date
)The last time the model package was modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
ApprovalDescription
— (String
)A description provided when the model approval is set.
Domain
— (String
)The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
Task
— (String
)The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.
SamplePayloadUrl
— (String
)The Amazon Simple Storage Service path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
AdditionalInferenceSpecifications
— (Array<map>
)An array of additional Inference Specification objects.
Name
— required — (String
)A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description
— (String
)A description of the additional Inference specification
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
SourceUri
— (String
)The URI of the source for the model package.
SecurityConfig
— (map
)An optional Key Management Service key to encrypt, decrypt, and re-encrypt model package information for regulated workloads with highly sensitive data.
KmsKeyId
— required — (String
)The KMS Key ID (
KMSKeyId
) used for encryption of model package information.
ModelCard
— (map
)The model card associated with the model package. Since
ModelPackageModelCard
is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema ofModelCard
. TheModelPackageModelCard
schema does not includemodel_package_details
, andmodel_overview
is composed of themodel_creator
andmodel_artifact
properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.ModelCardContent
— (String
)The content of the model card. The content must follow the schema described in Model Package Model Card Schema.
ModelCardStatus
— (String
)The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft
: The model card is a work in progress. -
PendingReview
: The model card is pending review. -
Approved
: The model card is approved. -
Archived
: The model card is archived. No more updates can be made to the model card content. If you try to update the model card content, you will receive the messageModel Card is in Archived state
.
"Draft"
"PendingReview"
"Approved"
"Archived"
-
Tags
— (Array<map>
)A list of the tags associated with the model package. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
CustomerMetadataProperties
— (map<String>
)The metadata properties for the model package.
DriftCheckBaselines
— (map
)Represents the drift check baselines that can be used when the model monitor is set using the model package.
Bias
— (map
)Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
ConfigFile
— (map
)The bias config file for a model.
ContentType
— (String
)The type of content stored in the file source.
ContentDigest
— (String
)The digest of the file source.
S3Uri
— required — (String
)The Amazon S3 URI for the file source.
PreTrainingConstraints
— (map
)The pre-training constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
PostTrainingConstraints
— (map
)The post-training constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Explainability
— (map
)Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Constraints
— (map
)The drift check explainability constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
ConfigFile
— (map
)The explainability config file for the model.
ContentType
— (String
)The type of content stored in the file source.
ContentDigest
— (String
)The digest of the file source.
S3Uri
— required — (String
)The Amazon S3 URI for the file source.
ModelQuality
— (map
)Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Statistics
— (map
)The drift check model quality statistics.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)The drift check model quality constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
ModelDataQuality
— (map
)Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
Statistics
— (map
)The drift check model data quality statistics.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
Constraints
— (map
)The drift check model data quality constraints.
ContentType
— required — (String
)The metric source content type.
ContentDigest
— (String
)The hash key used for the metrics source.
S3Uri
— required — (String
)The S3 URI for the metrics source.
SkipModelValidation
— (String
)Indicates if you want to skip model validation.
Possible values include:"All"
"None"
ModelPackageGroup
— (map
)A group of versioned models in the model registry.
ModelPackageGroupName
— (String
)The name of the model group.
ModelPackageGroupArn
— (String
)The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription
— (String
)The description for the model group.
CreationTime
— (Date
)The time that the model group was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
ModelPackageGroupStatus
— (String
)The status of the model group. This can be one of the following values.
-
PENDING
- The model group is pending being created. -
IN_PROGRESS
- The model group is in the process of being created. -
COMPLETED
- The model group was successfully created. -
FAILED
- The model group failed. -
DELETING
- The model group is in the process of being deleted. -
DELETE_FAILED
- SageMaker failed to delete the model group.
"Pending"
"InProgress"
"Completed"
"Failed"
"Deleting"
"DeleteFailed"
-
Tags
— (Array<map>
)A list of the tags associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Pipeline
— (map
)A SageMaker Model Building Pipeline instance.
PipelineArn
— (String
)The Amazon Resource Name (ARN) of the pipeline.
PipelineName
— (String
)The name of the pipeline.
PipelineDisplayName
— (String
)The display name of the pipeline.
PipelineDescription
— (String
)The description of the pipeline.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the role that created the pipeline.
PipelineStatus
— (String
)The status of the pipeline.
Possible values include:"Active"
"Deleting"
CreationTime
— (Date
)The creation time of the pipeline.
LastModifiedTime
— (Date
)The time that the pipeline was last modified.
LastRunTime
— (Date
)The time when the pipeline was last run.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
ParallelismConfiguration
— (map
)The parallelism configuration applied to the pipeline.
MaxParallelExecutionSteps
— required — (Integer
)The max number of steps that can be executed in parallel.
Tags
— (Array<map>
)A list of tags that apply to the pipeline.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
PipelineExecution
— (map
)An execution of a pipeline.
PipelineArn
— (String
)The Amazon Resource Name (ARN) of the pipeline that was executed.
PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionDisplayName
— (String
)The display name of the pipeline execution.
PipelineExecutionStatus
— (String
)The status of the pipeline status.
Possible values include:"Executing"
"Stopping"
"Stopped"
"Failed"
"Succeeded"
PipelineExecutionDescription
— (String
)The description of the pipeline execution.
PipelineExperimentConfig
— (map
)Specifies the names of the experiment and trial created by a pipeline.
ExperimentName
— (String
)The name of the experiment.
TrialName
— (String
)The name of the trial.
FailureReason
— (String
)If the execution failed, a message describing why.
CreationTime
— (Date
)The creation time of the pipeline execution.
LastModifiedTime
— (Date
)The time that the pipeline execution was last modified.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
ParallelismConfiguration
— (map
)The parallelism configuration applied to the pipeline execution.
MaxParallelExecutionSteps
— required — (Integer
)The max number of steps that can be executed in parallel.
SelectiveExecutionConfig
— (map
)The selective execution configuration applied to the pipeline run.
SourcePipelineExecutionArn
— (String
)The ARN from a reference execution of the current pipeline. Used to copy input collaterals needed for the selected steps to run. The execution status of the pipeline can be either
Failed
orSuccess
.This field is required if the steps you specify for
SelectedSteps
depend on output collaterals from any non-specified pipeline steps. For more information, see Selective Execution for Pipeline Steps.SelectedSteps
— required — (Array<map>
)A list of pipeline steps to run. All step(s) in all path(s) between two selected steps should be included.
StepName
— required — (String
)The name of the pipeline step.
PipelineParameters
— (Array<map>
)Contains a list of pipeline parameters. This list can be empty.
Name
— required — (String
)The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value
— required — (String
)The literal value for the parameter.
FeatureGroup
— (map
)Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.
FeatureGroupArn
— (String
)The Amazon Resource Name (ARN) of a
FeatureGroup
.FeatureGroupName
— (String
)The name of the
FeatureGroup
.RecordIdentifierFeatureName
— (String
)The name of the
Feature
whose value uniquely identifies aRecord
defined in theFeatureGroup
FeatureDefinitions
.EventTimeFeatureName
— (String
)The name of the feature that stores the
EventTime
of a Record in aFeatureGroup
.A
EventTime
is point in time when a new event occurs that corresponds to the creation or update of aRecord
inFeatureGroup
. AllRecords
in theFeatureGroup
must have a correspondingEventTime
.FeatureDefinitions
— (Array<map>
)A list of
Feature
s. EachFeature
must include aFeatureName
and aFeatureType
.Valid
FeatureType
s areIntegral
,Fractional
andString
.FeatureName
s cannot be any of the following:is_deleted
,write_time
,api_invocation_time
.You can create up to 2,500
FeatureDefinition
s perFeatureGroup
.FeatureName
— required — (String
)The name of a feature. The type must be a string.
FeatureName
cannot be any of the following:is_deleted
,write_time
,api_invocation_time
.The name:
-
Must start with an alphanumeric character.
-
Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.
-
FeatureType
— required — (String
)The value type of a feature. Valid values are Integral, Fractional, or String.
Possible values include:"Integral"
"Fractional"
"String"
CollectionType
— (String
)A grouping of elements where each element within the collection must have the same feature type (
String
,Integral
, orFractional
).-
List
: An ordered collection of elements. -
Set
: An unordered collection of unique elements. -
Vector
: A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.
"List"
"Set"
"Vector"
-
CollectionConfig
— (map
)Configuration for your collection.
VectorConfig
— (map
)Configuration for your vector collection type.
-
Dimension
: The number of elements in your vector.
Dimension
— required — (Integer
)The number of elements in your vector.
-
CreationTime
— (Date
)The time a
FeatureGroup
was created.LastModifiedTime
— (Date
)A timestamp indicating the last time you updated the feature group.
OnlineStoreConfig
— (map
)Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or
KMSKeyId
, for at rest data encryption. You can turnOnlineStore
on or off by specifying theEnableOnlineStore
flag at General Assembly.The default value is
False
.SecurityConfig
— (map
)Use to specify KMS Key ID (
KMSKeyId
) for at-rest encryption of yourOnlineStore
.KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either user or IAM role) of
CreateFeatureGroup
must have below permissions to theOnlineStore
KmsKeyId
:-
"kms:Encrypt"
-
"kms:Decrypt"
-
"kms:DescribeKey"
-
"kms:CreateGrant"
-
"kms:RetireGrant"
-
"kms:ReEncryptFrom"
-
"kms:ReEncryptTo"
-
"kms:GenerateDataKey"
-
"kms:ListAliases"
-
"kms:ListGrants"
-
"kms:RevokeGrant"
The caller (either user or IAM role) to all DataPlane operations (
PutRecord
,GetRecord
,DeleteRecord
) must have the following permissions to theKmsKeyId
:-
"kms:Decrypt"
-
EnableOnlineStore
— (Boolean
)Turn
OnlineStore
off by specifyingFalse
for theEnableOnlineStore
flag. TurnOnlineStore
on by specifyingTrue
for theEnableOnlineStore
flag.The default value is
False
.TtlDuration
— (map
)Time to live duration, where the record is hard deleted after the expiration time is reached;
ExpiresAt
=EventTime
+TtlDuration
. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.Unit
— (String
)TtlDuration
time unit."Seconds"
"Minutes"
"Hours"
"Days"
"Weeks"
Value
— (Integer
)TtlDuration
time value.
StorageType
— (String
)Option for different tiers of low latency storage for real-time data retrieval.
-
Standard
: A managed low latency data store for feature groups. -
InMemory
: A managed data store for feature groups that supports very low latency retrieval.
"Standard"
"InMemory"
-
OfflineStoreConfig
— (map
)The configuration of an
OfflineStore
.Provide an
OfflineStoreConfig
in a request toCreateFeatureGroup
to create anOfflineStore
.To encrypt an
OfflineStore
using at rest data encryption, specify Amazon Web Services Key Management Service (KMS) key ID, orKMSKeyId
, inS3StorageConfig
.S3StorageConfig
— required — (map
)The Amazon Simple Storage (Amazon S3) location of
OfflineStore
.S3Uri
— required — (String
)The S3 URI, or location in Amazon S3, of
OfflineStore
.S3 URIs have a format similar to the following:
s3://example-bucket/prefix/
.KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the
OfflineStore
S3 location.The IAM
roleARN
that is passed as a parameter toCreateFeatureGroup
must have below permissions to theKmsKeyId
:-
"kms:GenerateDataKey"
-
ResolvedOutputS3Uri
— (String
)The S3 path where offline records are written.
DisableGlueTableCreation
— (Boolean
)Set to
True
to disable the automatic creation of an Amazon Web Services Glue table when configuring anOfflineStore
. If set toFalse
, Feature Store will name theOfflineStore
Glue table following Athena's naming recommendations.The default value is
False
.DataCatalogConfig
— (map
)The meta data of the Glue table that is autogenerated when an
OfflineStore
is created.TableName
— required — (String
)The name of the Glue table.
Catalog
— required — (String
)The name of the Glue table catalog.
Database
— required — (String
)The name of the Glue table database.
TableFormat
— (String
)Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg.
Possible values include:"Default"
"Glue"
"Iceberg"
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.
FeatureGroupStatus
— (String
)A
Possible values include:FeatureGroup
status."Creating"
"Created"
"CreateFailed"
"Deleting"
"DeleteFailed"
OfflineStoreStatus
— (map
)The status of
OfflineStore
.Status
— required — (String
)An
Possible values include:OfflineStore
status."Active"
"Blocked"
"Disabled"
BlockedReason
— (String
)The justification for why the OfflineStoreStatus is Blocked (if applicable).
LastUpdateStatus
— (map
)A value that indicates whether the feature group was updated successfully.
Status
— required — (String
)A value that indicates whether the update was made successful.
Possible values include:"Successful"
"Failed"
"InProgress"
FailureReason
— (String
)If the update wasn't successful, indicates the reason why it failed.
FailureReason
— (String
)The reason that the
FeatureGroup
failed to be replicated in theOfflineStore
. This is failure may be due to a failure to create aFeatureGroup
in or delete aFeatureGroup
from theOfflineStore
.Description
— (String
)A free form description of a
FeatureGroup
.Tags
— (Array<map>
)Tags used to define a
FeatureGroup
.Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
FeatureMetadata
— (map
)The feature metadata used to search through the features.
FeatureGroupArn
— (String
)The Amazon Resource Number (ARN) of the feature group.
FeatureGroupName
— (String
)The name of the feature group containing the feature.
FeatureName
— (String
)The name of feature.
FeatureType
— (String
)The data type of the feature.
Possible values include:"Integral"
"Fractional"
"String"
CreationTime
— (Date
)A timestamp indicating when the feature was created.
LastModifiedTime
— (Date
)A timestamp indicating when the feature was last modified.
Description
— (String
)An optional description that you specify to better describe the feature.
Parameters
— (Array<map>
)Optional key-value pairs that you specify to better describe the feature.
Key
— (String
)A key that must contain a value to describe the feature.
Value
— (String
)The value that belongs to a key.
Project
— (map
)The properties of a project.
ProjectArn
— (String
)The Amazon Resource Name (ARN) of the project.
ProjectName
— (String
)The name of the project.
ProjectId
— (String
)The ID of the project.
ProjectDescription
— (String
)The description of the project.
ServiceCatalogProvisioningDetails
— (map
)Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
ProductId
— required — (String
)The ID of the product to provision.
ProvisioningArtifactId
— (String
)The ID of the provisioning artifact.
PathId
— (String
)The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
ProvisioningParameters
— (Array<map>
)A list of key value pairs that you specify when you provision a product.
Key
— (String
)The key that identifies a provisioning parameter.
Value
— (String
)The value of the provisioning parameter.
ServiceCatalogProvisionedProductDetails
— (map
)Details of a provisioned service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
ProvisionedProductId
— (String
)The ID of the provisioned product.
ProvisionedProductStatusMessage
— (String
)The current status of the product.
-
AVAILABLE
- Stable state, ready to perform any operation. The most recent operation succeeded and completed. -
UNDER_CHANGE
- Transitive state. Operations performed might not have valid results. Wait for an AVAILABLE status before performing operations. -
TAINTED
- Stable state, ready to perform any operation. The stack has completed the requested operation but is not exactly what was requested. For example, a request to update to a new version failed and the stack rolled back to the current version. -
ERROR
- An unexpected error occurred. The provisioned product exists but the stack is not running. For example, CloudFormation received a parameter value that was not valid and could not launch the stack. -
PLAN_IN_PROGRESS
- Transitive state. The plan operations were performed to provision a new product, but resources have not yet been created. After reviewing the list of resources to be created, execute the plan. Wait for an AVAILABLE status before performing operations.
-
ProjectStatus
— (String
)The status of the project.
Possible values include:"Pending"
"CreateInProgress"
"CreateCompleted"
"CreateFailed"
"DeleteInProgress"
"DeleteFailed"
"DeleteCompleted"
"UpdateInProgress"
"UpdateCompleted"
"UpdateFailed"
CreatedBy
— (map
)Who created the project.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
CreationTime
— (Date
)A timestamp specifying when the project was created.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
LastModifiedTime
— (Date
)A timestamp container for when the project was last modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
HyperParameterTuningJob
— (map
)The properties of a hyperparameter tuning job.
HyperParameterTuningJobName
— (String
)The name of a hyperparameter tuning job.
HyperParameterTuningJobArn
— (String
)The Amazon Resource Name (ARN) of a hyperparameter tuning job.
HyperParameterTuningJobConfig
— (map
)Configures a hyperparameter tuning job.
Strategy
— required — (String
)Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.
Possible values include:"Bayesian"
"Random"
"Hyperband"
"Grid"
StrategyConfig
— (map
)The configuration for the
Hyperband
optimization strategy. This parameter should be provided only ifHyperband
is selected as the strategy forHyperParameterTuningJobConfig
.HyperbandStrategyConfig
— (map
)The configuration for the object that specifies the
Hyperband
strategy. This parameter is only supported for theHyperband
selection forStrategy
within theHyperParameterTuningJobConfig
API.MinResource
— (Integer
)The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for
MinResource
has not been reached, the training job is not stopped byHyperband
.MaxResource
— (Integer
)The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the
MaxResource
value, it is stopped. If a value forMaxResource
is not provided, andHyperband
is selected as the hyperparameter tuning strategy,HyperbandTraining
attempts to inferMaxResource
from the following keys (if present) in StaticsHyperParameters:-
epochs
-
numepochs
-
n-epochs
-
n_epochs
-
num_epochs
If
HyperbandStrategyConfig
is unable to infer a value forMaxResource
, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions. For distributed training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.-
HyperParameterTuningJobObjective
— (map
)The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
ResourceLimits
— required — (map
)The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
MaxNumberOfTrainingJobs
— (Integer
)The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs
— required — (Integer
)The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds
— (Integer
)The maximum time in seconds that a hyperparameter tuning job can run.
ParameterRanges
— (map
)The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
AutoParameters
— (Array<map>
)A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
Name
— required — (String
)The name of the hyperparameter to optimize using Autotune.
ValueHint
— required — (String
)An example value of the hyperparameter to optimize using Autotune.
TrainingJobEarlyStoppingType
— (String
)Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the
Hyperband
strategy has its own advanced internal early stopping mechanism,TrainingJobEarlyStoppingType
must beOFF
to useHyperband
. This parameter can take on one of the following values (the default value isOFF
):- OFF
-
Training jobs launched by the hyperparameter tuning job do not use early stopping.
- AUTO
-
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.
"Off"
"Auto"
TuningJobCompletionCriteria
— (map
)The tuning job's completion criteria.
TargetObjectiveMetricValue
— (Float
)The value of the objective metric.
BestObjectiveNotImproving
— (map
)A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
MaxNumberOfTrainingJobsNotImproving
— (Integer
)The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
ConvergenceDetected
— (map
)A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
CompleteOnConvergence
— (String
)A flag to stop a tuning job once AMT has detected that the job has converged.
Possible values include:"Disabled"
"Enabled"
RandomSeed
— (Integer
)A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
TrainingJobDefinition
— (map
)Defines the training jobs launched by a hyperparameter tuning job.
DefinitionName
— (String
)The job definition name.
TuningObjective
— (map
)Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the
Type
parameter. If you want to define a custom objective metric, see Define metrics and environment variables.Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
HyperParameterRanges
— (map
)Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note: The maximum number of items specified forArray Members
refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
AutoParameters
— (Array<map>
)A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
Name
— required — (String
)The name of the hyperparameter to optimize using Autotune.
ValueHint
— required — (String
)An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters
— (map<String>
)Specifies the values of hyperparameters that do not change for the tuning job.
AlgorithmSpecification
— required — (map
)The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
AlgorithmName
— (String
)The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for
TrainingImage
.MetricDefinitions
— (Array<map>
)An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig
— (Array<map>
)An array of Channel objects that specify the input for the training jobs that the tuning job launches.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
VpcConfig
— (map
)The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
OutputDataConfig
— required — (map
)Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.Note: If you want to use hyperparameter optimization with instance type flexibility, useHyperParameterTuningResourceConfig
instead.InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
HyperParameterTuningResourceConfig
— (map
)The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose
File
forTrainingInputMode
in theAlgorithmSpecification
parameter to additionally store training data in the storage volume (optional).InstanceType
— (String
)The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of compute instances of type
InstanceType
to use. For distributed training, select a value greater than 1.VolumeSizeInGB
— (Integer
)The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for
InstanceConfigs
is also specified.Some instance types have a fixed total local storage size. If you select one of these instances for training,
VolumeSizeInGB
cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.Note: SageMaker supports only the General Purpose SSD (gp2) storage volume type.VolumeKmsKeyId
— (String
)A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a
VolumeKmsKeyId
. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.AllocationStrategy
— (String
)The strategy that determines the order of preference for resources specified in
Possible values include:InstanceConfigs
used in hyperparameter optimization."Prioritized"
InstanceConfigs
— (Array<map>
)A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The
AllocationStrategy
controls the order in which multiple configurations provided inInstanceConfigs
are used.Note: If you only want to use a single instance configuration inside theHyperParameterTuningResourceConfig
API, do not provide a value forInstanceConfigs
. Instead, useInstanceType
,VolumeSizeInGB
andInstanceCount
. If you useInstanceConfigs
, do not provide values forInstanceType
,VolumeSizeInGB
orInstanceCount
.InstanceType
— required — (String
)The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)The number of instances of the type specified by
InstanceType
. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.VolumeSizeInGB
— required — (Integer
)The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition
— required — (map
)Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation
— (Boolean
)Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.EnableManagedSpotTraining
— (Boolean
)A Boolean indicating whether managed spot training is enabled (
True
) or not (False
).CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
Environment
— (map<String>
)An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note: The maximum number of items specified forMap Entries
refers to the maximum number of environment variables for eachTrainingJobDefinition
and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
TrainingJobDefinitions
— (Array<map>
)The job definitions included in a hyperparameter tuning job.
DefinitionName
— (String
)The job definition name.
TuningObjective
— (map
)Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the
Type
parameter. If you want to define a custom objective metric, see Define metrics and environment variables.Type
— required — (String
)Whether to minimize or maximize the objective metric.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the metric to use for the objective metric.
HyperParameterRanges
— (map
)Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note: The maximum number of items specified forArray Members
refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.IntegerParameterRanges
— (Array<map>
)The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the hyperparameter to search.
MinValue
— required — (String
)The minimum value of the hyperparameter to search.
MaxValue
— required — (String
)The maximum value of the hyperparameter to search.
ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
ContinuousParameterRanges
— (Array<map>
)The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the continuous hyperparameter to tune.
MinValue
— required — (String
)The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValue
for tuning.MaxValue
— required — (String
)The maximum value for the hyperparameter. The tuning job uses floating-point values between
MinValue
value and this value for tuning.ScalingType
— (String
)The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
- Auto
-
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
- Linear
-
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
- Logarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
- ReverseLogarithmic
-
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
"Auto"
"Linear"
"Logarithmic"
"ReverseLogarithmic"
CategoricalParameterRanges
— (Array<map>
)The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
Name
— required — (String
)The name of the categorical hyperparameter to tune.
Values
— required — (Array<String>
)A list of the categories for the hyperparameter.
AutoParameters
— (Array<map>
)A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
Name
— required — (String
)The name of the hyperparameter to optimize using Autotune.
ValueHint
— required — (String
)An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters
— (map<String>
)Specifies the values of hyperparameters that do not change for the tuning job.
AlgorithmSpecification
— required — (map
)The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
AlgorithmName
— (String
)The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for
TrainingImage
.MetricDefinitions
— (Array<map>
)An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig
— (Array<map>
)An array of Channel objects that specify the input for the training jobs that the tuning job launches.
ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
VpcConfig
— (map
)The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
OutputDataConfig
— required — (map
)Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.Note: If you want to use hyperparameter optimization with instance type flexibility, useHyperParameterTuningResourceConfig
instead.InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
HyperParameterTuningResourceConfig
— (map
)The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose
File
forTrainingInputMode
in theAlgorithmSpecification
parameter to additionally store training data in the storage volume (optional).InstanceType
— (String
)The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of compute instances of type
InstanceType
to use. For distributed training, select a value greater than 1.VolumeSizeInGB
— (Integer
)The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for
InstanceConfigs
is also specified.Some instance types have a fixed total local storage size. If you select one of these instances for training,
VolumeSizeInGB
cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.Note: SageMaker supports only the General Purpose SSD (gp2) storage volume type.VolumeKmsKeyId
— (String
)A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a
VolumeKmsKeyId
. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.AllocationStrategy
— (String
)The strategy that determines the order of preference for resources specified in
Possible values include:InstanceConfigs
used in hyperparameter optimization."Prioritized"
InstanceConfigs
— (Array<map>
)A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The
AllocationStrategy
controls the order in which multiple configurations provided inInstanceConfigs
are used.Note: If you only want to use a single instance configuration inside theHyperParameterTuningResourceConfig
API, do not provide a value forInstanceConfigs
. Instead, useInstanceType
,VolumeSizeInGB
andInstanceCount
. If you useInstanceConfigs
, do not provide values forInstanceType
,VolumeSizeInGB
orInstanceCount
.InstanceType
— required — (String
)The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)The number of instances of the type specified by
InstanceType
. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.VolumeSizeInGB
— required — (Integer
)The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition
— required — (map
)Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation
— (Boolean
)Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.EnableManagedSpotTraining
— (Boolean
)A Boolean indicating whether managed spot training is enabled (
True
) or not (False
).CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
Environment
— (map<String>
)An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note: The maximum number of items specified forMap Entries
refers to the maximum number of environment variables for eachTrainingJobDefinition
and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
HyperParameterTuningJobStatus
— (String
)The status of a hyperparameter tuning job.
Possible values include:"Completed"
"InProgress"
"Failed"
"Stopped"
"Stopping"
"Deleting"
"DeleteFailed"
CreationTime
— (Date
)The time that a hyperparameter tuning job was created.
HyperParameterTuningEndTime
— (Date
)The time that a hyperparameter tuning job ended.
LastModifiedTime
— (Date
)The time that a hyperparameter tuning job was last modified.
TrainingJobStatusCounters
— (map
)The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.
Completed
— (Integer
)The number of completed training jobs launched by the hyperparameter tuning job.
InProgress
— (Integer
)The number of in-progress training jobs launched by a hyperparameter tuning job.
RetryableError
— (Integer
)The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
NonRetryableError
— (Integer
)The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Stopped
— (Integer
)The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
ObjectiveStatusCounters
— (map
)Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.
Succeeded
— (Integer
)The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending
— (Integer
)The number of training jobs that are in progress and pending evaluation of their final objective metric.
Failed
— (Integer
)The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
BestTrainingJob
— (map
)The container for the summary information about a training job.
TrainingJobDefinitionName
— (String
)The training job definition name.
TrainingJobName
— required — (String
)The name of the training job.
TrainingJobArn
— required — (String
)The Amazon Resource Name (ARN) of the training job.
TuningJobName
— (String
)The HyperParameter tuning job that launched the training job.
CreationTime
— required — (Date
)The date and time that the training job was created.
TrainingStartTime
— (Date
)The date and time that the training job started.
TrainingEndTime
— (Date
)Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of
TrainingStartTime
and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.TrainingJobStatus
— required — (String
)The status of the training job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
TunedHyperParameters
— required — (map<String>
)A list of the hyperparameters for which you specified ranges to search.
FailureReason
— (String
)The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric
— (map
)The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type
— (String
)Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.
Value
— required — (Float
)The value of the objective metric.
ObjectiveStatus
— (String
)The status of the objective metric for the training job:
-
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
-
Pending: The training job is in progress and evaluation of its final objective metric is pending.
-
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
"Succeeded"
"Pending"
"Failed"
-
OverallBestTrainingJob
— (map
)The container for the summary information about a training job.
TrainingJobDefinitionName
— (String
)The training job definition name.
TrainingJobName
— required — (String
)The name of the training job.
TrainingJobArn
— required — (String
)The Amazon Resource Name (ARN) of the training job.
TuningJobName
— (String
)The HyperParameter tuning job that launched the training job.
CreationTime
— required — (Date
)The date and time that the training job was created.
TrainingStartTime
— (Date
)The date and time that the training job started.
TrainingEndTime
— (Date
)Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of
TrainingStartTime
and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.TrainingJobStatus
— required — (String
)The status of the training job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
TunedHyperParameters
— required — (map<String>
)A list of the hyperparameters for which you specified ranges to search.
FailureReason
— (String
)The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric
— (map
)The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type
— (String
)Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
Possible values include:"Maximize"
"Minimize"
MetricName
— required — (String
)The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.
Value
— required — (Float
)The value of the objective metric.
ObjectiveStatus
— (String
)The status of the objective metric for the training job:
-
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
-
Pending: The training job is in progress and evaluation of its final objective metric is pending.
-
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
"Succeeded"
"Pending"
"Failed"
-
WarmStartConfig
— (map
)Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
Note: All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.ParentHyperParameterTuningJobs
— required — (Array<map>
)An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
HyperParameterTuningJobName
— (String
)The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
WarmStartType
— required — (String
)Specifies one of the following:
- IDENTICAL_DATA_AND_ALGORITHM
-
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
- TRANSFER_LEARNING
-
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
"IdenticalDataAndAlgorithm"
"TransferLearning"
FailureReason
— (String
)The error that was created when a hyperparameter tuning job failed.
TuningJobCompletionDetails
— (map
)Information about either a current or completed hyperparameter tuning job.
NumberOfTrainingJobsObjectiveNotImproving
— (Integer
)The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.
ConvergenceDetectedTime
— (Date
)The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.
ConsumedResources
— (map
)The total amount of resources consumed by a hyperparameter tuning job.
RuntimeInSeconds
— (Integer
)The wall clock runtime in seconds used by your hyperparameter tuning job.
Tags
— (Array<map>
)The tags associated with a hyperparameter tuning job. For more information see Tagging Amazon Web Services resources.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ModelCard
— (map
)An Amazon SageMaker Model Card that documents details about a machine learning model.
ModelCardArn
— (String
)The Amazon Resource Name (ARN) of the model card.
ModelCardName
— (String
)The unique name of the model card.
ModelCardVersion
— (Integer
)The version of the model card.
Content
— (String
)The content of the model card. Content uses the model card JSON schema and provided as a string.
ModelCardStatus
— (String
)The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft
: The model card is a work in progress. -
PendingReview
: The model card is pending review. -
Approved
: The model card is approved. -
Archived
: The model card is archived. No more updates should be made to the model card, but it can still be exported.
"Draft"
"PendingReview"
"Approved"
"Archived"
-
SecurityConfig
— (map
)The security configuration used to protect model card data.
KmsKeyId
— (String
)A Key Management Service key ID to use for encrypting a model card.
CreationTime
— (Date
)The date and time that the model card was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)The date and time that the model card was last modified.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
Tags
— (Array<map>
)Key-value pairs used to manage metadata for the model card.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ModelId
— (String
)The unique name (ID) of the model.
RiskRating
— (String
)The risk rating of the model. Different organizations might have different criteria for model card risk ratings. For more information, see Risk ratings.
ModelPackageGroupName
— (String
)The model package group that contains the model package. Only relevant for model cards created for model packages in the Amazon SageMaker Model Registry.
Model
— (map
)A model displayed in the Amazon SageMaker Model Dashboard.
Model
— (map
)A model displayed in the Model Dashboard.
ModelName
— (String
)The name of the model.
PrimaryContainer
— (map
)Describes the container, as part of model definition.
ContainerHostname
— (String
)This parameter is ignored for models that contain only a
PrimaryContainer
.When a
ContainerDefinition
is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for aContainerDefinition
that is part of an inference pipeline, a unique name is automatically assigned based on the position of theContainerDefinition
in the pipeline. If you specify a value for theContainerHostName
for anyContainerDefinition
that is part of an inference pipeline, you must specify a value for theContainerHostName
parameter of everyContainerDefinition
in that pipeline.Image
— (String
)The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.ImageConfig
— (map
)Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.RepositoryAccessMode
— required — (String
)Set this to one of the following values:
-
Platform
- The model image is hosted in Amazon ECR. -
Vpc
- The model image is hosted in a private Docker registry in your VPC.
"Platform"
"Vpc"
-
RepositoryAuthConfig
— (map
)(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified
Vpc
as the value for theRepositoryAccessMode
field, and the private Docker registry where the model image is hosted requires authentication.RepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide.
Mode
— (String
)Whether the container hosts a single model or multiple models.
Possible values include:"SingleModel"
"MultiModel"
ModelDataUrl
— (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in
ModelDataUrl
.ModelDataSource
— (map
)Specifies the location of ML model data to deploy.
Note: Currently you cannot useModelDataSource
in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
AdditionalModelDataSources
— (Array<map>
)Data sources that are available to your model in addition to the one that you specify for
ModelDataSource
when you use theCreateModel
action.ChannelName
— required — (String
)A custom name for this
AdditionalModelDataSource
object.S3DataSource
— required — (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
Environment
— (map<String>
)The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.
The maximum length of each key and value in the
Environment
map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to aCreateModel
request, then the maximum length of all of their maps, combined, is also 32 KB.ModelPackageName
— (String
)The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
MultiModelConfig
— (map
)Specifies additional configuration for multi-model endpoints.
ModelCacheSetting
— (String
)Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to
Possible values include:Disabled
."Enabled"
"Disabled"
Containers
— (Array<map>
)The containers in the inference pipeline.
ContainerHostname
— (String
)This parameter is ignored for models that contain only a
PrimaryContainer
.When a
ContainerDefinition
is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for aContainerDefinition
that is part of an inference pipeline, a unique name is automatically assigned based on the position of theContainerDefinition
in the pipeline. If you specify a value for theContainerHostName
for anyContainerDefinition
that is part of an inference pipeline, you must specify a value for theContainerHostName
parameter of everyContainerDefinition
in that pipeline.Image
— (String
)The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.ImageConfig
— (map
)Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.RepositoryAccessMode
— required — (String
)Set this to one of the following values:
-
Platform
- The model image is hosted in Amazon ECR. -
Vpc
- The model image is hosted in a private Docker registry in your VPC.
"Platform"
"Vpc"
-
RepositoryAuthConfig
— (map
)(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified
Vpc
as the value for theRepositoryAccessMode
field, and the private Docker registry where the model image is hosted requires authentication.RepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide.
Mode
— (String
)Whether the container hosts a single model or multiple models.
Possible values include:"SingleModel"
"MultiModel"
ModelDataUrl
— (String
)The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in
ModelDataUrl
.ModelDataSource
— (map
)Specifies the location of ML model data to deploy.
Note: Currently you cannot useModelDataSource
in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
AdditionalModelDataSources
— (Array<map>
)Data sources that are available to your model in addition to the one that you specify for
ModelDataSource
when you use theCreateModel
action.ChannelName
— required — (String
)A custom name for this
AdditionalModelDataSource
object.S3DataSource
— required — (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
Environment
— (map<String>
)The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.
The maximum length of each key and value in the
Environment
map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to aCreateModel
request, then the maximum length of all of their maps, combined, is also 32 KB.ModelPackageName
— (String
)The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName
— (String
)The inference specification name in the model package version.
MultiModelConfig
— (map
)Specifies additional configuration for multi-model endpoints.
ModelCacheSetting
— (String
)Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to
Possible values include:Disabled
."Enabled"
"Disabled"
InferenceExecutionConfig
— (map
)Specifies details about how containers in a multi-container endpoint are run.
Mode
— required — (String
)How containers in a multi-container are run. The following values are valid.
-
SERIAL
- Containers run as a serial pipeline. -
DIRECT
- Only the individual container that you specify is run.
"Serial"
"Direct"
-
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
CreationTime
— (Date
)A timestamp that indicates when the model was created.
ModelArn
— (String
)The Amazon Resource Name (ARN) of the model.
EnableNetworkIsolation
— (Boolean
)Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
Tags
— (Array<map>
)A list of key-value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
DeploymentRecommendation
— (map
)A set of recommended deployment configurations for the model.
RecommendationStatus
— required — (String
)Status of the deployment recommendation. The status
Possible values include:NOT_APPLICABLE
means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status isIN_PROGRESS
, retry your API call after a few seconds to get aCOMPLETED
deployment recommendation."IN_PROGRESS"
"COMPLETED"
"FAILED"
"NOT_APPLICABLE"
RealTimeInferenceRecommendations
— (Array<map>
)A list of RealTimeInferenceRecommendation items.
RecommendationId
— required — (String
)The recommendation ID which uniquely identifies each recommendation.
InstanceType
— required — (String
)The recommended instance type for Real-Time Inference.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
Environment
— (map<String>
)The recommended environment variables to set in the model container for Real-Time Inference.
Endpoints
— (Array<map>
)The endpoints that host a model.
EndpointName
— required — (String
)The endpoint name.
EndpointArn
— required — (String
)The Amazon Resource Name (ARN) of the endpoint.
CreationTime
— required — (Date
)A timestamp that indicates when the endpoint was created.
LastModifiedTime
— required — (Date
)The last time the endpoint was modified.
EndpointStatus
— required — (String
)The endpoint status.
Possible values include:"OutOfService"
"Creating"
"Updating"
"SystemUpdating"
"RollingBack"
"InService"
"Deleting"
"Failed"
"UpdateRollbackFailed"
LastBatchTransformJob
— (map
)A batch transform job. For information about SageMaker batch transform, see Use Batch Transform.
TransformJobName
— (String
)The name of the transform job.
TransformJobArn
— (String
)The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus
— (String
)The status of the transform job.
Transform job statuses are:
-
InProgress
- The job is in progress. -
Completed
- The job has completed. -
Failed
- The transform job has failed. To see the reason for the failure, see theFailureReason
field in the response to aDescribeTransformJob
call. -
Stopping
- The transform job is stopping. -
Stopped
- The transform job has stopped.
"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
-
FailureReason
— (String
)If the transform job failed, the reason it failed.
ModelName
— (String
)The name of the model associated with the transform job.
MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests that can be sent to each instance in a transform job. If
MaxConcurrentTransforms
is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value forMaxConcurrentTransforms
.ModelClientConfig
— (map
)Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds
— (Integer
)The timeout value in seconds for an invocation request. The default value is 600.
InvocationsMaxRetries
— (Integer
)The maximum number of retries when invocation requests are failing. The default value is 3.
MaxPayloadInMB
— (Integer
)The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in
MaxPayloadInMB
must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.BatchStrategy
— (String
)Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
Possible values include:"MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— (map
)Describes the input source of a transform job and the way the transform job consumes it.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— (map
)Describes the results of a transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
DataCaptureConfig
— (map
)Configuration to control how SageMaker captures inference data for batch transform jobs.
DestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
GenerateInferenceId
— (Boolean
)Flag that indicates whether to append inference id to the output.
TransformResources
— (map
)Describes the resources, including ML instance types and ML instance count, to use for transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. The default value is
1
, and the maximum is100
. For distributed transform jobs, specify a value greater than1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
CreationTime
— (Date
)A timestamp that shows when the transform Job was created.
TransformStartTime
— (Date
)Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of
TransformEndTime
.TransformEndTime
— (Date
)Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of
TransformStartTime
.LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the labeling job that created the transform job.
AutoMLJobArn
— (String
)The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing
— (map
)The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
InputFilter
— (String
)A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
InputFilter
parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value$
.Examples:
"$"
,"$[1:]"
,"$.features"
OutputFilter
— (String
)A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value,
$
. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.Examples:
"$"
,"$[0,5:]"
,"$['id','SageMakerOutput']"
JoinSource
— (String
)Specifies the source of the data to join with the transformed data. The valid values are
None
andInput
. The default value isNone
, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, setJoinSource
toInput
. You can specifyOutputFilter
as an additional filter to select a portion of the joined dataset and store it in the output file.For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called
SageMakerOutput
. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under theSageMakerInput
key and the results are stored inSageMakerOutput
.For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
Possible values include:"Input"
"None"
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
Tags
— (Array<map>
)A list of tags associated with the transform job.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
MonitoringSchedules
— (Array<map>
)The monitoring schedules for a model.
MonitoringScheduleArn
— (String
)The Amazon Resource Name (ARN) of a monitoring schedule.
MonitoringScheduleName
— (String
)The name of a monitoring schedule.
MonitoringScheduleStatus
— (String
)The status of the monitoring schedule.
Possible values include:"Pending"
"Failed"
"Scheduled"
"Stopped"
MonitoringType
— (String
)The monitor type of a model monitor.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
FailureReason
— (String
)If a monitoring job failed, provides the reason.
CreationTime
— (Date
)A timestamp that indicates when the monitoring schedule was created.
LastModifiedTime
— (Date
)A timestamp that indicates when the monitoring schedule was last updated.
MonitoringScheduleConfig
— (map
)Configures the monitoring schedule and defines the monitoring job.
ScheduleConfig
— (map
)Configures the monitoring schedule.
ScheduleExpression
— required — (String
)A cron expression that describes details about the monitoring schedule.
The supported cron expressions are:
-
If you want to set the job to start every hour, use the following:
Hourly: cron(0 * ? * * *)
-
If you want to start the job daily:
cron(0 [00-23] ? * * *)
-
If you want to run the job one time, immediately, use the following keyword:
NOW
For example, the following are valid cron expressions:
-
Daily at noon UTC:
cron(0 12 ? * * *)
-
Daily at midnight UTC:
cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
-
Every 12 hours, starting at 5pm UTC:
cron(0 17/12 ? * * *)
-
Every two hours starting at midnight:
cron(0 0/2 ? * * *)
Note:- Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
- We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
You can also specify the keyword
NOW
to run the monitoring job immediately, one time, without recurring.-
DataAnalysisStartTime
— (String
)Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the
ScheduleExpression
parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify:"-PT5H"
.The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the
DataAnalysisEndTime
parameter.If you set
ScheduleExpression
toNOW
, this parameter is required.DataAnalysisEndTime
— (String
)Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the
ScheduleExpression
parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify:"-PT1H"
.The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the
DataAnalysisStartTime
parameter.If you set
ScheduleExpression
toNOW
, this parameter is required.
MonitoringJobDefinition
— (map
)Defines the monitoring job.
BaselineConfig
— (map
)Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName
— (String
)The name of the job that performs baselining for the monitoring job.
ConstraintsResource
— (map
)The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
StatisticsResource
— (map
)The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri
— (String
)The Amazon S3 URI for the statistics resource.
MonitoringInputs
— required — (Array<map>
)The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
EndpointInput
— (map
)The endpoint for a monitoring job.
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
MonitoringOutputConfig
— required — (map
)The array of outputs from the monitoring job to be uploaded to Amazon S3.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources
— required — (map
)Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification
— required — (map
)Configures the monitoring job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the monitoring job.
ContainerEntrypoint
— (Array<String>
)Specifies the entrypoint for a container used to run the monitoring job.
ContainerArguments
— (Array<String>
)An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition
— (map
)Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
NetworkConfig
— (map
)Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job definition to schedule.
MonitoringType
— (String
)The type of the monitoring job definition to schedule.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
EndpointName
— (String
)The endpoint which is monitored.
MonitoringAlertSummaries
— (Array<map>
)A JSON array where each element is a summary for a monitoring alert.
MonitoringAlertName
— required — (String
)The name of a monitoring alert.
CreationTime
— required — (Date
)A timestamp that indicates when a monitor alert was created.
LastModifiedTime
— required — (Date
)A timestamp that indicates when a monitor alert was last updated.
AlertStatus
— required — (String
)The current status of an alert.
Possible values include:"InAlert"
"OK"
DatapointsToAlert
— required — (Integer
)Within
EvaluationPeriod
, how many execution failures will raise an alert.EvaluationPeriod
— required — (Integer
)The number of most recent monitoring executions to consider when evaluating alert status.
Actions
— required — (map
)A list of alert actions taken in response to an alert going into
InAlert
status.ModelDashboardIndicator
— (map
)An alert action taken to light up an icon on the Model Dashboard when an alert goes into
InAlert
status.Enabled
— (Boolean
)Indicates whether the alert action is turned on.
LastMonitoringExecutionSummary
— (map
)Summary of information about the last monitoring job to run.
MonitoringScheduleName
— required — (String
)The name of the monitoring schedule.
ScheduledTime
— required — (Date
)The time the monitoring job was scheduled.
CreationTime
— required — (Date
)The time at which the monitoring job was created.
LastModifiedTime
— required — (Date
)A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus
— required — (String
)The status of the monitoring job.
Possible values include:"Pending"
"Completed"
"CompletedWithViolations"
"InProgress"
"Failed"
"Stopping"
"Stopped"
ProcessingJobArn
— (String
)The Amazon Resource Name (ARN) of the monitoring job.
EndpointName
— (String
)The name of the endpoint used to run the monitoring job.
FailureReason
— (String
)Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job.
MonitoringType
— (String
)The type of the monitoring job.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
ModelCard
— (map
)The model card for a model.
ModelCardArn
— (String
)The Amazon Resource Name (ARN) for a model card.
ModelCardName
— (String
)The name of a model card.
ModelCardVersion
— (Integer
)The model card version.
ModelCardStatus
— (String
)The model card status.
Possible values include:"Draft"
"PendingReview"
"Approved"
"Archived"
SecurityConfig
— (map
)The KMS Key ID (
KMSKeyId
) for encryption of model card information.KmsKeyId
— (String
)A Key Management Service key ID to use for encrypting a model card.
CreationTime
— (Date
)A timestamp that indicates when the model card was created.
CreatedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
LastModifiedTime
— (Date
)A timestamp that indicates when the model card was last updated.
LastModifiedBy
— (map
)Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn
— (String
)The Amazon Resource Name (ARN) of the user's profile.
UserProfileName
— (String
)The name of the user's profile.
DomainId
— (String
)The domain associated with the user.
IamIdentity
— (map
)The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn
— (String
)The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId
— (String
)The ID of the principal that assumes the IAM identity.
SourceIdentity
— (String
)The person or application which assumes the IAM identity.
Tags
— (Array<map>
)The tags associated with a model card.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
ModelId
— (String
)For models created in SageMaker, this is the model ARN. For models created outside of SageMaker, this is a user-customized string.
RiskRating
— (String
)A model card's risk rating. Can be low, medium, or high.
NextToken
— (String
)If the result of the previous
Search
request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.
-
(AWS.Response)
—
Returns:
sendPipelineExecutionStepFailure(params = {}, callback) ⇒ AWS.Request
Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
Service Reference:
Examples:
Calling the sendPipelineExecutionStepFailure operation
var params = { CallbackToken: 'STRING_VALUE', /* required */ ClientRequestToken: 'STRING_VALUE', FailureReason: 'STRING_VALUE' }; sagemaker.sendPipelineExecutionStepFailure(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CallbackToken
— (String
)The pipeline generated token from the Amazon SQS queue.
FailureReason
— (String
)A message describing why the step failed.
ClientRequestToken
— (String
)A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
If a token is not provided, the SDK will use a version 4 UUID.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
-
(AWS.Response)
—
Returns:
sendPipelineExecutionStepSuccess(params = {}, callback) ⇒ AWS.Request
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
Service Reference:
Examples:
Calling the sendPipelineExecutionStepSuccess operation
var params = { CallbackToken: 'STRING_VALUE', /* required */ ClientRequestToken: 'STRING_VALUE', OutputParameters: [ { Name: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.sendPipelineExecutionStepSuccess(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CallbackToken
— (String
)The pipeline generated token from the Amazon SQS queue.
OutputParameters
— (Array<map>
)A list of the output parameters of the callback step.
Name
— required — (String
)The name of the output parameter.
Value
— required — (String
)The value of the output parameter.
ClientRequestToken
— (String
)A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
If a token is not provided, the SDK will use a version 4 UUID.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
-
(AWS.Response)
—
Returns:
startEdgeDeploymentStage(params = {}, callback) ⇒ AWS.Request
Starts a stage in an edge deployment plan.
Service Reference:
Examples:
Calling the startEdgeDeploymentStage operation
var params = { EdgeDeploymentPlanName: 'STRING_VALUE', /* required */ StageName: 'STRING_VALUE' /* required */ }; sagemaker.startEdgeDeploymentStage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgeDeploymentPlanName
— (String
)The name of the edge deployment plan to start.
StageName
— (String
)The name of the stage to start.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
startInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Starts an inference experiment.
Service Reference:
Examples:
Calling the startInferenceExperiment operation
var params = { Name: 'STRING_VALUE' /* required */ }; sagemaker.startInferenceExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
Name
— (String
)The name of the inference experiment to start.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceExperimentArn
— (String
)The ARN of the started inference experiment to start.
-
(AWS.Response)
—
Returns:
startMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Programmatically start an MLflow Tracking Server.
Service Reference:
Examples:
Calling the startMlflowTrackingServer operation
var params = { TrackingServerName: 'STRING_VALUE' /* required */ }; sagemaker.startMlflowTrackingServer(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrackingServerName
— (String
)The name of the tracking server to start.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrackingServerArn
— (String
)The ARN of the started tracking server.
-
(AWS.Response)
—
Returns:
startMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Starts a previously stopped monitoring schedule.
Note: By default, when you successfully create a new schedule, the status of a monitoring schedule isscheduled
.Service Reference:
Examples:
Calling the startMonitoringSchedule operation
var params = { MonitoringScheduleName: 'STRING_VALUE' /* required */ }; sagemaker.startMonitoringSchedule(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)The name of the schedule to start.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
startNotebookInstance(params = {}, callback) ⇒ AWS.Request
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker sets the notebook instance status to
InService
. A notebook instance's status must beInService
before you can connect to your Jupyter notebook.Service Reference:
Examples:
Calling the startNotebookInstance operation
var params = { NotebookInstanceName: 'STRING_VALUE' /* required */ }; sagemaker.startNotebookInstance(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceName
— (String
)The name of the notebook instance to start.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
startPipelineExecution(params = {}, callback) ⇒ AWS.Request
Starts a pipeline execution.
Service Reference:
Examples:
Calling the startPipelineExecution operation
var params = { ClientRequestToken: 'STRING_VALUE', /* required */ PipelineName: 'STRING_VALUE', /* required */ ParallelismConfiguration: { MaxParallelExecutionSteps: 'NUMBER_VALUE' /* required */ }, PipelineExecutionDescription: 'STRING_VALUE', PipelineExecutionDisplayName: 'STRING_VALUE', PipelineParameters: [ { Name: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ], SelectiveExecutionConfig: { SelectedSteps: [ /* required */ { StepName: 'STRING_VALUE' /* required */ }, /* more items */ ], SourcePipelineExecutionArn: 'STRING_VALUE' } }; sagemaker.startPipelineExecution(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineName
— (String
)The name or Amazon Resource Name (ARN) of the pipeline.
PipelineExecutionDisplayName
— (String
)The display name of the pipeline execution.
PipelineParameters
— (Array<map>
)Contains a list of pipeline parameters. This list can be empty.
Name
— required — (String
)The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value
— required — (String
)The literal value for the parameter.
PipelineExecutionDescription
— (String
)The description of the pipeline execution.
ClientRequestToken
— (String
)A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
If a token is not provided, the SDK will use a version 4 UUID.ParallelismConfiguration
— (map
)This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.
MaxParallelExecutionSteps
— required — (Integer
)The max number of steps that can be executed in parallel.
SelectiveExecutionConfig
— (map
)The selective execution configuration applied to the pipeline run.
SourcePipelineExecutionArn
— (String
)The ARN from a reference execution of the current pipeline. Used to copy input collaterals needed for the selected steps to run. The execution status of the pipeline can be either
Failed
orSuccess
.This field is required if the steps you specify for
SelectedSteps
depend on output collaterals from any non-specified pipeline steps. For more information, see Selective Execution for Pipeline Steps.SelectedSteps
— required — (Array<map>
)A list of pipeline steps to run. All step(s) in all path(s) between two selected steps should be included.
StepName
— required — (String
)The name of the pipeline step.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
-
(AWS.Response)
—
Returns:
stopAutoMLJob(params = {}, callback) ⇒ AWS.Request
A method for forcing a running job to shut down.
Service Reference:
Examples:
Calling the stopAutoMLJob operation
var params = { AutoMLJobName: 'STRING_VALUE' /* required */ }; sagemaker.stopAutoMLJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AutoMLJobName
— (String
)The name of the object you are requesting.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopCompilationJob(params = {}, callback) ⇒ AWS.Request
Stops a model compilation job.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.
When it receives a
StopCompilationJob
request, Amazon SageMaker changes theCompilationJobStatus
of the job toStopping
. After Amazon SageMaker stops the job, it sets theCompilationJobStatus
toStopped
.Service Reference:
Examples:
Calling the stopCompilationJob operation
var params = { CompilationJobName: 'STRING_VALUE' /* required */ }; sagemaker.stopCompilationJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CompilationJobName
— (String
)The name of the model compilation job to stop.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopEdgeDeploymentStage(params = {}, callback) ⇒ AWS.Request
Stops a stage in an edge deployment plan.
Service Reference:
Examples:
Calling the stopEdgeDeploymentStage operation
var params = { EdgeDeploymentPlanName: 'STRING_VALUE', /* required */ StageName: 'STRING_VALUE' /* required */ }; sagemaker.stopEdgeDeploymentStage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgeDeploymentPlanName
— (String
)The name of the edge deployment plan to stop.
StageName
— (String
)The name of the stage to stop.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopEdgePackagingJob(params = {}, callback) ⇒ AWS.Request
Request to stop an edge packaging job.
Service Reference:
Examples:
Calling the stopEdgePackagingJob operation
var params = { EdgePackagingJobName: 'STRING_VALUE' /* required */ }; sagemaker.stopEdgePackagingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EdgePackagingJobName
— (String
)The name of the edge packaging job.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopHyperParameterTuningJob(params = {}, callback) ⇒ AWS.Request
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the
Stopped
state, it releases all reserved resources for the tuning job.Service Reference:
Examples:
Calling the stopHyperParameterTuningJob operation
var params = { HyperParameterTuningJobName: 'STRING_VALUE' /* required */ }; sagemaker.stopHyperParameterTuningJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HyperParameterTuningJobName
— (String
)The name of the tuning job to stop.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Stops an inference experiment.
Service Reference:
Examples:
Calling the stopInferenceExperiment operation
var params = { ModelVariantActions: { /* required */ '<ModelVariantName>': Retain | Remove | Promote, /* '<ModelVariantName>': ... */ }, Name: 'STRING_VALUE', /* required */ DesiredModelVariants: [ { InfrastructureConfig: { /* required */ InfrastructureType: RealTimeInference, /* required */ RealTimeInferenceConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge /* required */ } }, ModelName: 'STRING_VALUE', /* required */ VariantName: 'STRING_VALUE' /* required */ }, /* more items */ ], DesiredState: Completed | Cancelled, Reason: 'STRING_VALUE' }; sagemaker.stopInferenceExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
Name
— (String
)The name of the inference experiment to stop.
ModelVariantActions
— (map<String>
)Array of key-value pairs, with names of variants mapped to actions. The possible actions are the following:
-
Promote
- Promote the shadow variant to a production variant -
Remove
- Delete the variant -
Retain
- Keep the variant as it is
-
DesiredModelVariants
— (Array<map>
)An array of
ModelVariantConfig
objects. There is one for each variant that you want to deploy after the inference experiment stops. EachModelVariantConfig
describes the infrastructure configuration for deploying the corresponding variant.ModelName
— required — (String
)The name of the Amazon SageMaker Model entity.
VariantName
— required — (String
)The name of the variant.
InfrastructureConfig
— required — (map
)The configuration for the infrastructure that the model will be deployed to.
InfrastructureType
— required — (String
)The inference option to which to deploy your model. Possible values are the following:
-
RealTime
: Deploy to real-time inference.
"RealTimeInference"
-
RealTimeInferenceConfig
— required — (map
)The infrastructure configuration for deploying the model to real-time inference.
InstanceType
— required — (String
)The instance type the model is deployed to.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
InstanceCount
— required — (Integer
)The number of instances of the type specified by
InstanceType
.
DesiredState
— (String
)The desired state of the experiment after stopping. The possible states are the following:
-
Completed
: The experiment completed successfully -
Cancelled
: The experiment was canceled
"Completed"
"Cancelled"
-
Reason
— (String
)The reason for stopping the experiment.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceExperimentArn
— (String
)The ARN of the stopped inference experiment.
-
(AWS.Response)
—
Returns:
stopInferenceRecommendationsJob(params = {}, callback) ⇒ AWS.Request
Stops an Inference Recommender job.
Service Reference:
Examples:
Calling the stopInferenceRecommendationsJob operation
var params = { JobName: 'STRING_VALUE' /* required */ }; sagemaker.stopInferenceRecommendationsJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
JobName
— (String
)The name of the job you want to stop.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopLabelingJob(params = {}, callback) ⇒ AWS.Request
Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
Service Reference:
Examples:
Calling the stopLabelingJob operation
var params = { LabelingJobName: 'STRING_VALUE' /* required */ }; sagemaker.stopLabelingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
LabelingJobName
— (String
)The name of the labeling job to stop.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Programmatically stop an MLflow Tracking Server.
Service Reference:
Examples:
Calling the stopMlflowTrackingServer operation
var params = { TrackingServerName: 'STRING_VALUE' /* required */ }; sagemaker.stopMlflowTrackingServer(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrackingServerName
— (String
)The name of the tracking server to stop.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrackingServerArn
— (String
)The ARN of the stopped tracking server.
-
(AWS.Response)
—
Returns:
stopMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Stops a previously started monitoring schedule.
Service Reference:
Examples:
Calling the stopMonitoringSchedule operation
var params = { MonitoringScheduleName: 'STRING_VALUE' /* required */ }; sagemaker.stopMonitoringSchedule(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)The name of the schedule to stop.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopNotebookInstance(params = {}, callback) ⇒ AWS.Request
Terminates the ML compute instance. Before terminating the instance, SageMaker disconnects the ML storage volume from it. SageMaker preserves the ML storage volume. SageMaker stops charging you for the ML compute instance when you call
StopNotebookInstance
.To access data on the ML storage volume for a notebook instance that has been terminated, call the
StartNotebookInstance
API.StartNotebookInstance
launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.Service Reference:
Examples:
Calling the stopNotebookInstance operation
var params = { NotebookInstanceName: 'STRING_VALUE' /* required */ }; sagemaker.stopNotebookInstance(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceName
— (String
)The name of the notebook instance to terminate.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopOptimizationJob(params = {}, callback) ⇒ AWS.Request
Ends a running inference optimization job.
Service Reference:
Examples:
Calling the stopOptimizationJob operation
var params = { OptimizationJobName: 'STRING_VALUE' /* required */ }; sagemaker.stopOptimizationJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
OptimizationJobName
— (String
)The name that you assigned to the optimization job.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopPipelineExecution(params = {}, callback) ⇒ AWS.Request
Stops a pipeline execution.
Callback Step
A pipeline execution won't stop while a callback step is running. When you call
StopPipelineExecution
on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping".You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to
SendPipelineExecutionStepSuccess
orSendPipelineExecutionStepFailure
.Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.
Lambda Step
A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is
Stopped
. If the timeout is hit the pipeline execution status isFailed
.Service Reference:
Examples:
Calling the stopPipelineExecution operation
var params = { ClientRequestToken: 'STRING_VALUE', /* required */ PipelineExecutionArn: 'STRING_VALUE' /* required */ }; sagemaker.stopPipelineExecution(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
ClientRequestToken
— (String
)A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
If a token is not provided, the SDK will use a version 4 UUID.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
-
(AWS.Response)
—
Returns:
stopProcessingJob(params = {}, callback) ⇒ AWS.Request
Stops a processing job.
Service Reference:
Examples:
Calling the stopProcessingJob operation
var params = { ProcessingJobName: 'STRING_VALUE' /* required */ }; sagemaker.stopProcessingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ProcessingJobName
— (String
)The name of the processing job to stop.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopTrainingJob(params = {}, callback) ⇒ AWS.Request
Stops a training job. To stop a job, SageMaker sends the algorithm the
SIGTERM
signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.When it receives a
StopTrainingJob
request, SageMaker changes the status of the job toStopping
. After SageMaker stops the job, it sets the status toStopped
.Service Reference:
Examples:
Calling the stopTrainingJob operation
var params = { TrainingJobName: 'STRING_VALUE' /* required */ }; sagemaker.stopTrainingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrainingJobName
— (String
)The name of the training job to stop.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
stopTransformJob(params = {}, callback) ⇒ AWS.Request
Stops a batch transform job.
When Amazon SageMaker receives a
StopTransformJob
request, the status of the job changes toStopping
. After Amazon SageMaker stops the job, the status is set toStopped
. When you stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.Service Reference:
Examples:
Calling the stopTransformJob operation
var params = { TransformJobName: 'STRING_VALUE' /* required */ }; sagemaker.stopTransformJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TransformJobName
— (String
)The name of the batch transform job to stop.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
updateAction(params = {}, callback) ⇒ AWS.Request
Updates an action.
Service Reference:
Examples:
Calling the updateAction operation
var params = { ActionName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', Properties: { '<StringParameterValue>': 'STRING_VALUE', /* '<StringParameterValue>': ... */ }, PropertiesToRemove: [ 'STRING_VALUE', /* more items */ ], Status: Unknown | InProgress | Completed | Failed | Stopping | Stopped }; sagemaker.updateAction(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ActionName
— (String
)The name of the action to update.
Description
— (String
)The new description for the action.
Status
— (String
)The new status for the action.
Possible values include:"Unknown"
"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
Properties
— (map<String>
)The new list of properties. Overwrites the current property list.
PropertiesToRemove
— (Array<String>
)A list of properties to remove.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ActionArn
— (String
)The Amazon Resource Name (ARN) of the action.
-
(AWS.Response)
—
Returns:
updateAppImageConfig(params = {}, callback) ⇒ AWS.Request
Updates the properties of an AppImageConfig.
Service Reference:
Examples:
Calling the updateAppImageConfig operation
var params = { AppImageConfigName: 'STRING_VALUE', /* required */ CodeEditorAppImageConfig: { ContainerConfig: { ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], ContainerEnvironmentVariables: { '<NonEmptyString256>': 'STRING_VALUE', /* '<NonEmptyString256>': ... */ } }, FileSystemConfig: { DefaultGid: 'NUMBER_VALUE', DefaultUid: 'NUMBER_VALUE', MountPath: 'STRING_VALUE' } }, JupyterLabAppImageConfig: { ContainerConfig: { ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], ContainerEnvironmentVariables: { '<NonEmptyString256>': 'STRING_VALUE', /* '<NonEmptyString256>': ... */ } }, FileSystemConfig: { DefaultGid: 'NUMBER_VALUE', DefaultUid: 'NUMBER_VALUE', MountPath: 'STRING_VALUE' } }, KernelGatewayImageConfig: { KernelSpecs: [ /* required */ { Name: 'STRING_VALUE', /* required */ DisplayName: 'STRING_VALUE' }, /* more items */ ], FileSystemConfig: { DefaultGid: 'NUMBER_VALUE', DefaultUid: 'NUMBER_VALUE', MountPath: 'STRING_VALUE' } } }; sagemaker.updateAppImageConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
AppImageConfigName
— (String
)The name of the AppImageConfig to update.
KernelGatewayImageConfig
— (map
)The new KernelGateway app to run on the image.
KernelSpecs
— required — (Array<map>
)The specification of the Jupyter kernels in the image.
Name
— required — (String
)The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName
— (String
)The display name of the kernel.
FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
JupyterLabAppImageConfig
— (map
)The JupyterLab app running on the image.
FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
ContainerConfig
— (map
)The configuration used to run the application image container.
ContainerArguments
— (Array<String>
)The arguments for the container when you're running the application.
ContainerEntrypoint
— (Array<String>
)The entrypoint used to run the application in the container.
ContainerEnvironmentVariables
— (map<String>
)The environment variables to set in the container
CodeEditorAppImageConfig
— (map
)The Code Editor app running on the image.
FileSystemConfig
— (map
)The Amazon Elastic File System storage configuration for a SageMaker image.
MountPath
— (String
)The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
DefaultUid
— (Integer
)The default POSIX user ID (UID). If not specified, defaults to
1000
.DefaultGid
— (Integer
)The default POSIX group ID (GID). If not specified, defaults to
100
.
ContainerConfig
— (map
)The configuration used to run the application image container.
ContainerArguments
— (Array<String>
)The arguments for the container when you're running the application.
ContainerEntrypoint
— (Array<String>
)The entrypoint used to run the application in the container.
ContainerEnvironmentVariables
— (map<String>
)The environment variables to set in the container
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:AppImageConfigArn
— (String
)The ARN for the AppImageConfig.
-
(AWS.Response)
—
Returns:
updateArtifact(params = {}, callback) ⇒ AWS.Request
Updates an artifact.
Service Reference:
Examples:
Calling the updateArtifact operation
var params = { ArtifactArn: 'STRING_VALUE', /* required */ ArtifactName: 'STRING_VALUE', Properties: { '<StringParameterValue>': 'STRING_VALUE', /* '<StringParameterValue>': ... */ }, PropertiesToRemove: [ 'STRING_VALUE', /* more items */ ] }; sagemaker.updateArtifact(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ArtifactArn
— (String
)The Amazon Resource Name (ARN) of the artifact to update.
ArtifactName
— (String
)The new name for the artifact.
Properties
— (map<String>
)The new list of properties. Overwrites the current property list.
PropertiesToRemove
— (Array<String>
)A list of properties to remove.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ArtifactArn
— (String
)The Amazon Resource Name (ARN) of the artifact.
-
(AWS.Response)
—
Returns:
updateCluster(params = {}, callback) ⇒ AWS.Request
Updates a SageMaker HyperPod cluster.
Service Reference:
Examples:
Calling the updateCluster operation
var params = { ClusterName: 'STRING_VALUE', /* required */ InstanceGroups: [ /* required */ { ExecutionRole: 'STRING_VALUE', /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceGroupName: 'STRING_VALUE', /* required */ InstanceType: ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.c5n.large | ml.c5n.2xlarge | ml.c5n.4xlarge | ml.c5n.9xlarge | ml.c5n.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge, /* required */ LifeCycleConfig: { /* required */ OnCreate: 'STRING_VALUE', /* required */ SourceS3Uri: 'STRING_VALUE' /* required */ }, InstanceStorageConfigs: [ { EbsVolumeConfig: { VolumeSizeInGB: 'NUMBER_VALUE' /* required */ } }, /* more items */ ], ThreadsPerCore: 'NUMBER_VALUE' }, /* more items */ ] }; sagemaker.updateCluster(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ClusterName
— (String
)Specify the name of the SageMaker HyperPod cluster you want to update.
InstanceGroups
— (Array<map>
)Specify the instance groups to update.
InstanceCount
— required — (Integer
)Specifies the number of instances to add to the instance group of a SageMaker HyperPod cluster.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.c5n.large"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
LifeCycleConfig
— required — (map
)Specifies the LifeCycle configuration for the instance group.
SourceS3Uri
— required — (String
)An Amazon S3 bucket path where your lifecycle scripts are stored.
Make sure that the S3 bucket path starts with
s3://sagemaker-
. The IAM role for SageMaker HyperPod has the managedAmazonSageMakerClusterInstanceRolePolicy
attached, which allows access to S3 buckets with the specific prefixsagemaker-
.OnCreate
— required — (String
)The file name of the entrypoint script of lifecycle scripts under
SourceS3Uri
. This entrypoint script runs during cluster creation.
ExecutionRole
— required — (String
)Specifies an IAM execution role to be assumed by the instance group.
ThreadsPerCore
— (Integer
)Specifies the value for Threads per core. For instance types that support multithreading, you can specify
1
for disabling multithreading and2
for enabling multithreading. For instance types that doesn't support multithreading, specify1
. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.InstanceStorageConfigs
— (Array<map>
)Specifies the additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.
EbsVolumeConfig
— (map
)Defines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to
/opt/sagemaker
.VolumeSizeInGB
— required — (Integer
)The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to
/opt/sagemaker
.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ClusterArn
— (String
)The Amazon Resource Name (ARN) of the updated SageMaker HyperPod cluster.
-
(AWS.Response)
—
Returns:
updateClusterSoftware(params = {}, callback) ⇒ AWS.Request
Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see Update the SageMaker HyperPod platform software of a cluster.
Service Reference:
Examples:
Calling the updateClusterSoftware operation
var params = { ClusterName: 'STRING_VALUE' /* required */ }; sagemaker.updateClusterSoftware(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ClusterName
— (String
)Specify the name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster you want to update for security patching.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ClusterArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster being updated for security patching.
-
(AWS.Response)
—
Returns:
updateCodeRepository(params = {}, callback) ⇒ AWS.Request
Updates the specified Git repository with the specified values.
Service Reference:
Examples:
Calling the updateCodeRepository operation
var params = { CodeRepositoryName: 'STRING_VALUE', /* required */ GitConfig: { SecretArn: 'STRING_VALUE' } }; sagemaker.updateCodeRepository(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
CodeRepositoryName
— (String
)The name of the Git repository to update.
GitConfig
— (map
)The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of
AWSCURRENT
and must be in the following format:{"username": UserName, "password": Password}
SecretArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of
AWSCURRENT
and must be in the following format:{"username": UserName, "password": Password}
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CodeRepositoryArn
— (String
)The ARN of the Git repository.
-
(AWS.Response)
—
Returns:
updateContext(params = {}, callback) ⇒ AWS.Request
Updates a context.
Service Reference:
Examples:
Calling the updateContext operation
var params = { ContextName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', Properties: { '<StringParameterValue>': 'STRING_VALUE', /* '<StringParameterValue>': ... */ }, PropertiesToRemove: [ 'STRING_VALUE', /* more items */ ] }; sagemaker.updateContext(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ContextName
— (String
)The name of the context to update.
Description
— (String
)The new description for the context.
Properties
— (map<String>
)The new list of properties. Overwrites the current property list.
PropertiesToRemove
— (Array<String>
)A list of properties to remove.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ContextArn
— (String
)The Amazon Resource Name (ARN) of the context.
-
(AWS.Response)
—
Returns:
updateDeviceFleet(params = {}, callback) ⇒ AWS.Request
Updates a fleet of devices.
Service Reference:
Examples:
Calling the updateDeviceFleet operation
var params = { DeviceFleetName: 'STRING_VALUE', /* required */ OutputConfig: { /* required */ S3OutputLocation: 'STRING_VALUE', /* required */ KmsKeyId: 'STRING_VALUE', PresetDeploymentConfig: 'STRING_VALUE', PresetDeploymentType: GreengrassV2Component }, Description: 'STRING_VALUE', EnableIotRoleAlias: true || false, RoleArn: 'STRING_VALUE' }; sagemaker.updateDeviceFleet(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DeviceFleetName
— (String
)The name of the fleet.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the device.
Description
— (String
)Description of the fleet.
OutputConfig
— (map
)Output configuration for storing sample data collected by the fleet.
S3OutputLocation
— required — (String
)The Amazon Simple Storage (S3) bucker URI.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
PresetDeploymentType
— (String
)The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
Possible values include:"GreengrassV2Component"
PresetDeploymentConfig
— (String
)The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
-
ComponentName
(optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job. -
ComponentDescription
(optional) - Description of the component. -
ComponentVersion
(optional) - The version of the component.Note: Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification. -
PlatformOS
(optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux. -
PlatformArchitecture
(optional) - The processor architecture for the platform.Supported architectures Windows include: Windows32_x86, Windows64_x64.
Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
-
EnableIotRoleAlias
— (Boolean
)Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-
{DeviceFleetName}
".For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
updateDevices(params = {}, callback) ⇒ AWS.Request
Updates one or more devices in a fleet.
Service Reference:
Examples:
Calling the updateDevices operation
var params = { DeviceFleetName: 'STRING_VALUE', /* required */ Devices: [ /* required */ { DeviceName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', IotThingName: 'STRING_VALUE' }, /* more items */ ] }; sagemaker.updateDevices(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DeviceFleetName
— (String
)The name of the fleet the devices belong to.
Devices
— (Array<map>
)List of devices to register with Edge Manager agent.
DeviceName
— required — (String
)The name of the device.
Description
— (String
)Description of the device.
IotThingName
— (String
)Amazon Web Services Internet of Things (IoT) object name.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
updateDomain(params = {}, callback) ⇒ AWS.Request
Updates the default settings for new user profiles in the domain.
Service Reference:
Examples:
Calling the updateDomain operation
var params = { DomainId: 'STRING_VALUE', /* required */ AppNetworkAccessType: PublicInternetOnly | VpcOnly, AppSecurityGroupManagement: Service | Customer, DefaultSpaceSettings: { CustomFileSystemConfigs: [ { EFSFileSystemConfig: { FileSystemId: 'STRING_VALUE', /* required */ FileSystemPath: 'STRING_VALUE' } }, /* more items */ ], CustomPosixUserConfig: { Gid: 'NUMBER_VALUE', /* required */ Uid: 'NUMBER_VALUE' /* required */ }, ExecutionRole: 'STRING_VALUE', JupyterLabAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE', LifecycleManagement: ENABLED | DISABLED, MaxIdleTimeoutInMinutes: 'NUMBER_VALUE', MinIdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, EmrSettings: { AssumableRoleArns: [ 'STRING_VALUE', /* more items */ ], ExecutionRoleArns: [ 'STRING_VALUE', /* more items */ ] }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, JupyterServerAppSettings: { CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, KernelGatewayAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, SecurityGroups: [ 'STRING_VALUE', /* more items */ ], SpaceStorageSettings: { DefaultEbsStorageSettings: { DefaultEbsVolumeSizeInGb: 'NUMBER_VALUE', /* required */ MaximumEbsVolumeSizeInGb: 'NUMBER_VALUE' /* required */ } } }, DefaultUserSettings: { AutoMountHomeEFS: Enabled | Disabled | DefaultAsDomain, CanvasAppSettings: { DirectDeploySettings: { Status: ENABLED | DISABLED }, EmrServerlessSettings: { ExecutionRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, GenerativeAiSettings: { AmazonBedrockRoleArn: 'STRING_VALUE' }, IdentityProviderOAuthSettings: [ { DataSourceName: SalesforceGenie | Snowflake, SecretArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, /* more items */ ], KendraSettings: { Status: ENABLED | DISABLED }, ModelRegisterSettings: { CrossAccountModelRegisterRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, TimeSeriesForecastingSettings: { AmazonForecastRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, WorkspaceSettings: { S3ArtifactPath: 'STRING_VALUE', S3KmsKeyId: 'STRING_VALUE' } }, CodeEditorAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE', LifecycleManagement: ENABLED | DISABLED, MaxIdleTimeoutInMinutes: 'NUMBER_VALUE', MinIdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, CustomFileSystemConfigs: [ { EFSFileSystemConfig: { FileSystemId: 'STRING_VALUE', /* required */ FileSystemPath: 'STRING_VALUE' } }, /* more items */ ], CustomPosixUserConfig: { Gid: 'NUMBER_VALUE', /* required */ Uid: 'NUMBER_VALUE' /* required */ }, DefaultLandingUri: 'STRING_VALUE', ExecutionRole: 'STRING_VALUE', JupyterLabAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE', LifecycleManagement: ENABLED | DISABLED, MaxIdleTimeoutInMinutes: 'NUMBER_VALUE', MinIdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, EmrSettings: { AssumableRoleArns: [ 'STRING_VALUE', /* more items */ ], ExecutionRoleArns: [ 'STRING_VALUE', /* more items */ ] }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, JupyterServerAppSettings: { CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, KernelGatewayAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, RSessionAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } }, RStudioServerProAppSettings: { AccessStatus: ENABLED | DISABLED, UserGroup: R_STUDIO_ADMIN | R_STUDIO_USER }, SecurityGroups: [ 'STRING_VALUE', /* more items */ ], SharingSettings: { NotebookOutputOption: Allowed | Disabled, S3KmsKeyId: 'STRING_VALUE', S3OutputPath: 'STRING_VALUE' }, SpaceStorageSettings: { DefaultEbsStorageSettings: { DefaultEbsVolumeSizeInGb: 'NUMBER_VALUE', /* required */ MaximumEbsVolumeSizeInGb: 'NUMBER_VALUE' /* required */ } }, StudioWebPortal: ENABLED | DISABLED, StudioWebPortalSettings: { HiddenAppTypes: [ JupyterServer | KernelGateway | DetailedProfiler | TensorBoard | CodeEditor | JupyterLab | RStudioServerPro | RSessionGateway | Canvas, /* more items */ ], HiddenMlTools: [ DataWrangler | FeatureStore | EmrClusters | AutoMl | Experiments | Training | ModelEvaluation | Pipelines | Models | JumpStart | InferenceRecommender | Endpoints | Projects | InferenceOptimization, /* more items */ ] }, TensorBoardAppSettings: { DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } } }, DomainSettingsForUpdate: { AmazonQSettings: { QProfileArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, DockerSettings: { EnableDockerAccess: ENABLED | DISABLED, VpcOnlyTrustedAccounts: [ 'STRING_VALUE', /* more items */ ] }, ExecutionRoleIdentityConfig: USER_PROFILE_NAME | DISABLED, RStudioServerProDomainSettingsForUpdate: { DomainExecutionRoleArn: 'STRING_VALUE', /* required */ DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, RStudioConnectUrl: 'STRING_VALUE', RStudioPackageManagerUrl: 'STRING_VALUE' }, SecurityGroupIds: [ 'STRING_VALUE', /* more items */ ] }, SubnetIds: [ 'STRING_VALUE', /* more items */ ] }; sagemaker.updateDomain(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The ID of the domain to be updated.
DefaultUserSettings
— (map
)A collection of settings.
ExecutionRole
— (String
)The execution role for the user.
SecurityGroups
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the
CreateDomain.AppNetworkAccessType
parameter is set toPublicInternetOnly
.Required when the
CreateDomain.AppNetworkAccessType
parameter is set toVpcOnly
, unless specified as part of theDefaultUserSettings
for the domain.Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SharingSettings
— (map
)Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption
— (String
)Whether to include the notebook cell output when sharing the notebook. The default is
Possible values include:Disabled
."Allowed"
"Disabled"
S3OutputPath
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon S3 bucket used to store the shared notebook snapshots.S3KmsKeyId
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings
— (map
)The Jupyter server's app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The kernel gateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
TensorBoardAppSettings
— (map
)The TensorBoard app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings
— (map
)A collection of settings that configure user interaction with the
RStudioServerPro
app.AccessStatus
— (String
)Indicates whether the current user has access to the
Possible values include:RStudioServerPro
app."ENABLED"
"DISABLED"
UserGroup
— (String
)The level of permissions that the user has within the
Possible values include:RStudioServerPro
app. This value defaults toUser
. TheAdmin
value allows the user access to the RStudio Administrative Dashboard."R_STUDIO_ADMIN"
"R_STUDIO_USER"
RSessionAppSettings
— (map
)A collection of settings that configure the
RSessionGateway
app.DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a RSession app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
CanvasAppSettings
— (map
)The Canvas app settings.
TimeSeriesForecastingSettings
— (map
)Time series forecast settings for the SageMaker Canvas application.
Status
— (String
)Describes whether time series forecasting is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
AmazonForecastRoleArn
— (String
)The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the
UserProfile
that launches the Canvas application. If an execution role is not specified in theUserProfile
, Canvas uses the execution role specified in the Domain that owns theUserProfile
. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached andforecast.amazonaws.com
added in the trust relationship as a service principal.
ModelRegisterSettings
— (map
)The model registry settings for the SageMaker Canvas application.
Status
— (String
)Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CrossAccountModelRegisterRoleArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings
— (map
)The workspace settings for the SageMaker Canvas application.
S3ArtifactPath
— (String
)The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings
— (Array<map>
)The settings for connecting to an external data source with OAuth.
DataSourceName
— (String
)The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Possible values include:"SalesforceGenie"
"Snowflake"
Status
— (String
)Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
SecretArn
— (String
)The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings
— (map
)The model deployment settings for the SageMaker Canvas application.
Status
— (String
)Describes whether model deployment permissions are enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
KendraSettings
— (map
)The settings for document querying.
Status
— (String
)Describes whether the document querying feature is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
GenerativeAiSettings
— (map
)The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn
— (String
)The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes
bedrock.amazonaws.com
as a service principal.
EmrServerlessSettings
— (map
)The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status
— (String
)Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CodeEditorAppSettings
— (map
)The Code Editor application settings.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a Code Editor app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a JupyterLab app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set
LifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings
— (map
)The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
ExecutionRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
SpaceStorageSettings
— (map
)The storage settings for a space.
DefaultEbsStorageSettings
— (map
)The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb
— required — (Integer
)The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb
— required — (Integer
)The maximum size of the EBS storage volume for a space.
DefaultLandingUri
— (String
)The default experience that the user is directed to when accessing the domain. The supported values are:
-
studio::
: Indicates that Studio is the default experience. This value can only be passed ifStudioWebPortal
is set toENABLED
. -
app:JupyterServer:
: Indicates that Studio Classic is the default experience.
-
StudioWebPortal
— (String
)Whether the user can access Studio. If this value is set to
Possible values include:DISABLED
, the user cannot access Studio, even if that is the default experience for the domain."ENABLED"
"DISABLED"
CustomPosixUserConfig
— (map
)Details about the POSIX identity that is used for file system operations.
Uid
— required — (Integer
)The POSIX user ID.
Gid
— required — (Integer
)The POSIX group ID.
CustomFileSystemConfigs
— (Array<map>
)The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystemConfig
— (map
)The settings for a custom Amazon EFS file system.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
FileSystemPath
— (String
)The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings
— (map
)Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools
— (Array<String>
)The machine learning tools that are hidden from the Studio left navigation pane.
HiddenAppTypes
— (Array<String>
)The Applications supported in Studio that are hidden from the Studio left navigation pane.
AutoMountHomeEFS
— (String
)Indicates whether auto-mounting of an EFS volume is supported for the user profile. The
Possible values include:DefaultAsDomain
value is only supported for user profiles. Do not use theDefaultAsDomain
value when setting this parameter for a domain."Enabled"
"Disabled"
"DefaultAsDomain"
DomainSettingsForUpdate
— (map
)A collection of
DomainSettings
configuration values to update.RStudioServerProDomainSettingsForUpdate
— (map
)A collection of
RStudioServerPro
Domain-level app settings to update. A singleRStudioServerPro
application is created for a domain.DomainExecutionRoleArn
— required — (String
)The execution role for the
RStudioServerPro
Domain-level app.DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioConnectUrl
— (String
)A URL pointing to an RStudio Connect server.
RStudioPackageManagerUrl
— (String
)A URL pointing to an RStudio Package Manager server.
ExecutionRoleIdentityConfig
— (String
)The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key. This configuration can only be modified if there are no apps in the
Possible values include:InService
orPending
state."USER_PROFILE_NAME"
"DISABLED"
SecurityGroupIds
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud that the
Domain
uses for communication between Domain-level apps and user apps.DockerSettings
— (map
)A collection of settings that configure the domain's Docker interaction.
EnableDockerAccess
— (String
)Indicates whether the domain can access Docker.
Possible values include:"ENABLED"
"DISABLED"
VpcOnlyTrustedAccounts
— (Array<String>
)The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
AmazonQSettings
— (map
)A collection of settings that configure the Amazon Q experience within the domain.
Status
— (String
)Whether Amazon Q has been enabled within the domain.
Possible values include:"ENABLED"
"DISABLED"
QProfileArn
— (String
)The ARN of the Amazon Q profile used within the domain.
AppSecurityGroupManagement
— (String
)The entity that creates and manages the required security groups for inter-app communication in
Possible values include:VPCOnly
mode. Required whenCreateDomain.AppNetworkAccessType
isVPCOnly
andDomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn
is provided. If setting up the domain for use with RStudio, this value must be set toService
."Service"
"Customer"
DefaultSpaceSettings
— (map
)The default settings used to create a space within the domain.
ExecutionRole
— (String
)The ARN of the execution role for the space.
SecurityGroups
— (Array<String>
)The security group IDs for the Amazon VPC that the space uses for communication.
JupyterServerAppSettings
— (map
)The JupyterServer app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The KernelGateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a JupyterLab app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set
LifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings
— (map
)The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
ExecutionRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
SpaceStorageSettings
— (map
)The default storage settings for a space.
DefaultEbsStorageSettings
— (map
)The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb
— required — (Integer
)The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb
— required — (Integer
)The maximum size of the EBS storage volume for a space.
CustomPosixUserConfig
— (map
)Details about the POSIX identity that is used for file system operations.
Uid
— required — (Integer
)The POSIX user ID.
Gid
— required — (Integer
)The POSIX group ID.
CustomFileSystemConfigs
— (Array<map>
)The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystemConfig
— (map
)The settings for a custom Amazon EFS file system.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
FileSystemPath
— (String
)The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
SubnetIds
— (Array<String>
)The VPC subnets that Studio uses for communication.
If removing subnets, ensure there are no apps in the
InService
,Pending
, orDeleting
state.AppNetworkAccessType
— (String
)Specifies the VPC used for non-EFS traffic.
-
PublicInternetOnly
- Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access. -
VpcOnly
- All Studio traffic is through the specified VPC and subnets.
This configuration can only be modified if there are no apps in the
Possible values include:InService
,Pending
, orDeleting
state. The configuration cannot be updated ifDomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn
is already set orDomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn
is provided as part of the same request."PublicInternetOnly"
"VpcOnly"
-
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:DomainArn
— (String
)The Amazon Resource Name (ARN) of the domain.
-
(AWS.Response)
—
Returns:
updateEndpoint(params = {}, callback) ⇒ AWS.Request
Deploys the
EndpointConfig
specified in the request to a new fleet of instances. SageMaker shifts endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances using the previousEndpointConfig
(there is no availability loss). For more information about how to control the update and traffic shifting process, see Update models in production.When SageMaker receives the request, it sets the endpoint status to
Updating
. After updating the endpoint, it sets the status toInService
. To check the status of an endpoint, use the DescribeEndpoint API.Note: You must not delete anEndpointConfig
in use by an endpoint that is live or while theUpdateEndpoint
orCreateEndpoint
operations are being performed on the endpoint. To update an endpoint, you must create a newEndpointConfig
. If you delete theEndpointConfig
of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.Service Reference:
Examples:
Calling the updateEndpoint operation
var params = { EndpointConfigName: 'STRING_VALUE', /* required */ EndpointName: 'STRING_VALUE', /* required */ DeploymentConfig: { AutoRollbackConfiguration: { Alarms: [ { AlarmName: 'STRING_VALUE' }, /* more items */ ] }, BlueGreenUpdatePolicy: { TrafficRoutingConfiguration: { /* required */ Type: ALL_AT_ONCE | CANARY | LINEAR, /* required */ WaitIntervalInSeconds: 'NUMBER_VALUE', /* required */ CanarySize: { Type: INSTANCE_COUNT | CAPACITY_PERCENT, /* required */ Value: 'NUMBER_VALUE' /* required */ }, LinearStepSize: { Type: INSTANCE_COUNT | CAPACITY_PERCENT, /* required */ Value: 'NUMBER_VALUE' /* required */ } }, MaximumExecutionTimeoutInSeconds: 'NUMBER_VALUE', TerminationWaitInSeconds: 'NUMBER_VALUE' }, RollingUpdatePolicy: { MaximumBatchSize: { /* required */ Type: INSTANCE_COUNT | CAPACITY_PERCENT, /* required */ Value: 'NUMBER_VALUE' /* required */ }, WaitIntervalInSeconds: 'NUMBER_VALUE', /* required */ MaximumExecutionTimeoutInSeconds: 'NUMBER_VALUE', RollbackMaximumBatchSize: { Type: INSTANCE_COUNT | CAPACITY_PERCENT, /* required */ Value: 'NUMBER_VALUE' /* required */ } } }, ExcludeRetainedVariantProperties: [ { VariantPropertyType: DesiredInstanceCount | DesiredWeight | DataCaptureConfig /* required */ }, /* more items */ ], RetainAllVariantProperties: true || false, RetainDeploymentConfig: true || false }; sagemaker.updateEndpoint(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (String
)The name of the endpoint whose configuration you want to update.
EndpointConfigName
— (String
)The name of the new endpoint configuration.
RetainAllVariantProperties
— (Boolean
)When updating endpoint resources, enables or disables the retention of variant properties, such as the instance count or the variant weight. To retain the variant properties of an endpoint when updating it, set
RetainAllVariantProperties
totrue
. To use the variant properties specified in a newEndpointConfig
call when updating an endpoint, setRetainAllVariantProperties
tofalse
. The default isfalse
.ExcludeRetainedVariantProperties
— (Array<map>
)When you are updating endpoint resources with
RetainAllVariantProperties
, whose value is set totrue
,ExcludeRetainedVariantProperties
specifies the list of type VariantProperty to override with the values provided byEndpointConfig
. If you don't specify a value forExcludeRetainedVariantProperties
, no variant properties are overridden.VariantPropertyType
— required — (String
)The type of variant property. The supported values are:
-
DesiredInstanceCount
: Overrides the existing variant instance counts using theInitialInstanceCount
values in theProductionVariants
of CreateEndpointConfig. -
DesiredWeight
: Overrides the existing variant weights using theInitialVariantWeight
values in theProductionVariants
of CreateEndpointConfig. -
DataCaptureConfig
: (Not currently supported.)
"DesiredInstanceCount"
"DesiredWeight"
"DataCaptureConfig"
-
DeploymentConfig
— (map
)The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.
BlueGreenUpdatePolicy
— (map
)Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
TrafficRoutingConfiguration
— required — (map
)Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.
Type
— required — (String
)Traffic routing strategy type.
-
ALL_AT_ONCE
: Endpoint traffic shifts to the new fleet in a single step. -
CANARY
: Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic. -
LINEAR
: Endpoint traffic shifts to the new fleet in n steps of a configurable size.
"ALL_AT_ONCE"
"CANARY"
"LINEAR"
-
WaitIntervalInSeconds
— required — (Integer
)The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.
CanarySize
— (map
)Batch size for the first step to turn on traffic on the new endpoint fleet.
Value
must be less than or equal to 50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
LinearStepSize
— (map
)Batch size for each step to turn on traffic on the new endpoint fleet.
Value
must be 10-50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
TerminationWaitInSeconds
— (Integer
)Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.
MaximumExecutionTimeoutInSeconds
— (Integer
)Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in
TerminationWaitInSeconds
andWaitIntervalInSeconds
.
RollingUpdatePolicy
— (map
)Specifies a rolling deployment strategy for updating a SageMaker endpoint.
MaximumBatchSize
— required — (map
)Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.
Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
WaitIntervalInSeconds
— required — (Integer
)The length of the baking period, during which SageMaker monitors alarms for each batch on the new fleet.
MaximumExecutionTimeoutInSeconds
— (Integer
)The time limit for the total deployment. Exceeding this limit causes a timeout.
RollbackMaximumBatchSize
— (map
)Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.
Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
AutoRollbackConfiguration
— (map
)Automatic rollback configuration for handling endpoint deployment failures and recovery.
Alarms
— (Array<map>
)List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.
AlarmName
— (String
)The name of a CloudWatch alarm in your account.
RetainDeploymentConfig
— (Boolean
)Specifies whether to reuse the last deployment configuration. The default value is false (the configuration is not reused).
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointArn
— (String
)The Amazon Resource Name (ARN) of the endpoint.
-
(AWS.Response)
—
Returns:
updateEndpointWeightsAndCapacities(params = {}, callback) ⇒ AWS.Request
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to
Updating
. After updating the endpoint, it sets the status toInService
. To check the status of an endpoint, use the DescribeEndpoint API.Service Reference:
Examples:
Calling the updateEndpointWeightsAndCapacities operation
var params = { DesiredWeightsAndCapacities: [ /* required */ { VariantName: 'STRING_VALUE', /* required */ DesiredInstanceCount: 'NUMBER_VALUE', DesiredWeight: 'NUMBER_VALUE', ServerlessUpdateConfig: { MaxConcurrency: 'NUMBER_VALUE', ProvisionedConcurrency: 'NUMBER_VALUE' } }, /* more items */ ], EndpointName: 'STRING_VALUE' /* required */ }; sagemaker.updateEndpointWeightsAndCapacities(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
EndpointName
— (String
)The name of an existing SageMaker endpoint.
DesiredWeightsAndCapacities
— (Array<map>
)An object that provides new capacity and weight values for a variant.
VariantName
— required — (String
)The name of the variant to update.
DesiredWeight
— (Float
)The variant's weight.
DesiredInstanceCount
— (Integer
)The variant's capacity.
ServerlessUpdateConfig
— (map
)Specifies the serverless update concurrency configuration for an endpoint variant.
MaxConcurrency
— (Integer
)The updated maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The updated amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointArn
— (String
)The Amazon Resource Name (ARN) of the updated endpoint.
-
(AWS.Response)
—
Returns:
updateExperiment(params = {}, callback) ⇒ AWS.Request
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
Service Reference:
Examples:
Calling the updateExperiment operation
var params = { ExperimentName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', DisplayName: 'STRING_VALUE' }; sagemaker.updateExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ExperimentName
— (String
)The name of the experiment to update.
DisplayName
— (String
)The name of the experiment as displayed. The name doesn't need to be unique. If
DisplayName
isn't specified,ExperimentName
is displayed.Description
— (String
)The description of the experiment.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ExperimentArn
— (String
)The Amazon Resource Name (ARN) of the experiment.
-
(AWS.Response)
—
Returns:
updateFeatureGroup(params = {}, callback) ⇒ AWS.Request
Updates the feature group by either adding features or updating the online store configuration. Use one of the following request parameters at a time while using the
UpdateFeatureGroup
API.You can add features for your feature group using the
FeatureAdditions
request parameter. Features cannot be removed from a feature group.You can update the online store configuration by using the
OnlineStoreConfig
request parameter. If aTtlDuration
is specified, the defaultTtlDuration
applies for all records added to the feature group after the feature group is updated. If a record levelTtlDuration
exists from using thePutRecord
API, the record levelTtlDuration
applies to that record instead of the defaultTtlDuration
. To remove the defaultTtlDuration
from an existing feature group, use theUpdateFeatureGroup
API and set theTtlDuration
Unit
andValue
tonull
.Service Reference:
Examples:
Calling the updateFeatureGroup operation
var params = { FeatureGroupName: 'STRING_VALUE', /* required */ FeatureAdditions: [ { FeatureName: 'STRING_VALUE', /* required */ FeatureType: Integral | Fractional | String, /* required */ CollectionConfig: { VectorConfig: { Dimension: 'NUMBER_VALUE' /* required */ } }, CollectionType: List | Set | Vector }, /* more items */ ], OnlineStoreConfig: { TtlDuration: { Unit: Seconds | Minutes | Hours | Days | Weeks, Value: 'NUMBER_VALUE' } }, ThroughputConfig: { ProvisionedReadCapacityUnits: 'NUMBER_VALUE', ProvisionedWriteCapacityUnits: 'NUMBER_VALUE', ThroughputMode: OnDemand | Provisioned } }; sagemaker.updateFeatureGroup(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FeatureGroupName
— (String
)The name or Amazon Resource Name (ARN) of the feature group that you're updating.
FeatureAdditions
— (Array<map>
)Updates the feature group. Updating a feature group is an asynchronous operation. When you get an HTTP 200 response, you've made a valid request. It takes some time after you've made a valid request for Feature Store to update the feature group.
FeatureName
— required — (String
)The name of a feature. The type must be a string.
FeatureName
cannot be any of the following:is_deleted
,write_time
,api_invocation_time
.The name:
-
Must start with an alphanumeric character.
-
Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.
-
FeatureType
— required — (String
)The value type of a feature. Valid values are Integral, Fractional, or String.
Possible values include:"Integral"
"Fractional"
"String"
CollectionType
— (String
)A grouping of elements where each element within the collection must have the same feature type (
String
,Integral
, orFractional
).-
List
: An ordered collection of elements. -
Set
: An unordered collection of unique elements. -
Vector
: A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.
"List"
"Set"
"Vector"
-
CollectionConfig
— (map
)Configuration for your collection.
VectorConfig
— (map
)Configuration for your vector collection type.
-
Dimension
: The number of elements in your vector.
Dimension
— required — (Integer
)The number of elements in your vector.
-
OnlineStoreConfig
— (map
)Updates the feature group online store configuration.
TtlDuration
— (map
)Time to live duration, where the record is hard deleted after the expiration time is reached;
ExpiresAt
=EventTime
+TtlDuration
. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.Unit
— (String
)TtlDuration
time unit."Seconds"
"Minutes"
"Hours"
"Days"
"Weeks"
Value
— (Integer
)TtlDuration
time value.
ThroughputConfig
— (map
)The new throughput configuration for the feature group. You can switch between on-demand and provisioned modes or update the read / write capacity of provisioned feature groups. You can switch a feature group to on-demand only once in a 24 hour period.
ThroughputMode
— (String
)Target throughput mode of the feature group. Throughput update is an asynchronous operation, and the outcome should be monitored by polling
Possible values include:LastUpdateStatus
field inDescribeFeatureGroup
response. You cannot update a feature group's throughput while another update is in progress."OnDemand"
"Provisioned"
ProvisionedReadCapacityUnits
— (Integer
)For provisioned feature groups with online store enabled, this indicates the read throughput you are billed for and can consume without throttling.
ProvisionedWriteCapacityUnits
— (Integer
)For provisioned feature groups, this indicates the write throughput you are billed for and can consume without throttling.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:FeatureGroupArn
— (String
)The Amazon Resource Number (ARN) of the feature group that you're updating.
-
(AWS.Response)
—
Returns:
updateFeatureMetadata(params = {}, callback) ⇒ AWS.Request
Updates the description and parameters of the feature group.
Service Reference:
Examples:
Calling the updateFeatureMetadata operation
var params = { FeatureGroupName: 'STRING_VALUE', /* required */ FeatureName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', ParameterAdditions: [ { Key: 'STRING_VALUE', Value: 'STRING_VALUE' }, /* more items */ ], ParameterRemovals: [ 'STRING_VALUE', /* more items */ ] }; sagemaker.updateFeatureMetadata(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
FeatureGroupName
— (String
)The name or Amazon Resource Name (ARN) of the feature group containing the feature that you're updating.
FeatureName
— (String
)The name of the feature that you're updating.
Description
— (String
)A description that you can write to better describe the feature.
ParameterAdditions
— (Array<map>
)A list of key-value pairs that you can add to better describe the feature.
Key
— (String
)A key that must contain a value to describe the feature.
Value
— (String
)The value that belongs to a key.
ParameterRemovals
— (Array<String>
)A list of parameter keys that you can specify to remove parameters that describe your feature.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
updateHub(params = {}, callback) ⇒ AWS.Request
Update a hub.
Service Reference:
Examples:
Calling the updateHub operation
var params = { HubName: 'STRING_VALUE', /* required */ HubDescription: 'STRING_VALUE', HubDisplayName: 'STRING_VALUE', HubSearchKeywords: [ 'STRING_VALUE', /* more items */ ] }; sagemaker.updateHub(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
HubName
— (String
)The name of the hub to update.
HubDescription
— (String
)A description of the updated hub.
HubDisplayName
— (String
)The display name of the hub.
HubSearchKeywords
— (Array<String>
)The searchable keywords for the hub.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:HubArn
— (String
)The Amazon Resource Name (ARN) of the updated hub.
-
(AWS.Response)
—
Returns:
updateImage(params = {}, callback) ⇒ AWS.Request
Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
Service Reference:
Examples:
Calling the updateImage operation
var params = { ImageName: 'STRING_VALUE', /* required */ DeleteProperties: [ 'STRING_VALUE', /* more items */ ], Description: 'STRING_VALUE', DisplayName: 'STRING_VALUE', RoleArn: 'STRING_VALUE' }; sagemaker.updateImage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DeleteProperties
— (Array<String>
)A list of properties to delete. Only the
Description
andDisplayName
properties can be deleted.Description
— (String
)The new description for the image.
DisplayName
— (String
)The new display name for the image.
ImageName
— (String
)The name of the image to update.
RoleArn
— (String
)The new ARN for the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ImageArn
— (String
)The ARN of the image.
-
(AWS.Response)
—
Returns:
updateImageVersion(params = {}, callback) ⇒ AWS.Request
Updates the properties of a SageMaker image version.
Service Reference:
Examples:
Calling the updateImageVersion operation
var params = { ImageName: 'STRING_VALUE', /* required */ Alias: 'STRING_VALUE', AliasesToAdd: [ 'STRING_VALUE', /* more items */ ], AliasesToDelete: [ 'STRING_VALUE', /* more items */ ], Horovod: true || false, JobType: TRAINING | INFERENCE | NOTEBOOK_KERNEL, MLFramework: 'STRING_VALUE', Processor: CPU | GPU, ProgrammingLang: 'STRING_VALUE', ReleaseNotes: 'STRING_VALUE', VendorGuidance: NOT_PROVIDED | STABLE | TO_BE_ARCHIVED | ARCHIVED, Version: 'NUMBER_VALUE' }; sagemaker.updateImageVersion(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ImageName
— (String
)The name of the image.
Alias
— (String
)The alias of the image version.
Version
— (Integer
)The version of the image.
AliasesToAdd
— (Array<String>
)A list of aliases to add.
AliasesToDelete
— (Array<String>
)A list of aliases to delete.
VendorGuidance
— (String
)The availability of the image version specified by the maintainer.
-
NOT_PROVIDED
: The maintainers did not provide a status for image version stability. -
STABLE
: The image version is stable. -
TO_BE_ARCHIVED
: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months. -
ARCHIVED
: The image version is archived. Archived image versions are not searchable and are no longer actively supported.
"NOT_PROVIDED"
"STABLE"
"TO_BE_ARCHIVED"
"ARCHIVED"
-
JobType
— (String
)Indicates SageMaker job type compatibility.
-
TRAINING
: The image version is compatible with SageMaker training jobs. -
INFERENCE
: The image version is compatible with SageMaker inference jobs. -
NOTEBOOK_KERNEL
: The image version is compatible with SageMaker notebook kernels.
"TRAINING"
"INFERENCE"
"NOTEBOOK_KERNEL"
-
MLFramework
— (String
)The machine learning framework vended in the image version.
ProgrammingLang
— (String
)The supported programming language and its version.
Processor
— (String
)Indicates CPU or GPU compatibility.
-
CPU
: The image version is compatible with CPU. -
GPU
: The image version is compatible with GPU.
"CPU"
"GPU"
-
Horovod
— (Boolean
)Indicates Horovod compatibility.
ReleaseNotes
— (String
)The maintainer description of the image version.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ImageVersionArn
— (String
)The ARN of the image version.
-
(AWS.Response)
—
Returns:
updateInferenceComponent(params = {}, callback) ⇒ AWS.Request
Updates an inference component.
Service Reference:
Examples:
Calling the updateInferenceComponent operation
var params = { InferenceComponentName: 'STRING_VALUE', /* required */ RuntimeConfig: { CopyCount: 'NUMBER_VALUE' /* required */ }, Specification: { ComputeResourceRequirements: { /* required */ MinMemoryRequiredInMb: 'NUMBER_VALUE', /* required */ MaxMemoryRequiredInMb: 'NUMBER_VALUE', NumberOfAcceleratorDevicesRequired: 'NUMBER_VALUE', NumberOfCpuCoresRequired: 'NUMBER_VALUE' }, Container: { ArtifactUrl: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Image: 'STRING_VALUE' }, ModelName: 'STRING_VALUE', StartupParameters: { ContainerStartupHealthCheckTimeoutInSeconds: 'NUMBER_VALUE', ModelDataDownloadTimeoutInSeconds: 'NUMBER_VALUE' } } }; sagemaker.updateInferenceComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
InferenceComponentName
— (String
)The name of the inference component.
Specification
— (map
)Details about the resources to deploy with this inference component, including the model, container, and compute resources.
ModelName
— (String
)The name of an existing SageMaker model object in your account that you want to deploy with the inference component.
Container
— (map
)Defines a container that provides the runtime environment for a model that you deploy with an inference component.
Image
— (String
)The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.
ArtifactUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the Environment string-to-string map can have length of up to 1024. We support up to 16 entries in the map.
StartupParameters
— (map
)Settings that take effect while the model container starts up.
ModelDataDownloadTimeoutInSeconds
— (Integer
)The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.
ContainerStartupHealthCheckTimeoutInSeconds
— (Integer
)The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
ComputeResourceRequirements
— required — (map
)The compute resources allocated to run the model assigned to the inference component.
NumberOfCpuCoresRequired
— (Float
)The number of CPU cores to allocate to run a model that you assign to an inference component.
NumberOfAcceleratorDevicesRequired
— (Float
)The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia.
MinMemoryRequiredInMb
— required — (Integer
)The minimum MB of memory to allocate to run a model that you assign to an inference component.
MaxMemoryRequiredInMb
— (Integer
)The maximum MB of memory to allocate to run a model that you assign to an inference component.
RuntimeConfig
— (map
)Runtime settings for a model that is deployed with an inference component.
CopyCount
— required — (Integer
)The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceComponentArn
— (String
)The Amazon Resource Name (ARN) of the inference component.
-
(AWS.Response)
—
Returns:
updateInferenceComponentRuntimeConfig(params = {}, callback) ⇒ AWS.Request
Runtime settings for a model that is deployed with an inference component.
Service Reference:
Examples:
Calling the updateInferenceComponentRuntimeConfig operation
var params = { DesiredRuntimeConfig: { /* required */ CopyCount: 'NUMBER_VALUE' /* required */ }, InferenceComponentName: 'STRING_VALUE' /* required */ }; sagemaker.updateInferenceComponentRuntimeConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
InferenceComponentName
— (String
)The name of the inference component to update.
DesiredRuntimeConfig
— (map
)Runtime settings for a model that is deployed with an inference component.
CopyCount
— required — (Integer
)The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceComponentArn
— (String
)The Amazon Resource Name (ARN) of the inference component.
-
(AWS.Response)
—
Returns:
updateInferenceExperiment(params = {}, callback) ⇒ AWS.Request
Updates an inference experiment that you created. The status of the inference experiment has to be either
Created
,Running
. For more information on the status of an inference experiment, see DescribeInferenceExperiment.Service Reference:
Examples:
Calling the updateInferenceExperiment operation
var params = { Name: 'STRING_VALUE', /* required */ DataStorageConfig: { Destination: 'STRING_VALUE', /* required */ ContentType: { CsvContentTypes: [ 'STRING_VALUE', /* more items */ ], JsonContentTypes: [ 'STRING_VALUE', /* more items */ ] }, KmsKey: 'STRING_VALUE' }, Description: 'STRING_VALUE', ModelVariants: [ { InfrastructureConfig: { /* required */ InfrastructureType: RealTimeInference, /* required */ RealTimeInferenceConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge /* required */ } }, ModelName: 'STRING_VALUE', /* required */ VariantName: 'STRING_VALUE' /* required */ }, /* more items */ ], Schedule: { EndTime: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, StartTime: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789 }, ShadowModeConfig: { ShadowModelVariants: [ /* required */ { SamplingPercentage: 'NUMBER_VALUE', /* required */ ShadowModelVariantName: 'STRING_VALUE' /* required */ }, /* more items */ ], SourceModelVariantName: 'STRING_VALUE' /* required */ } }; sagemaker.updateInferenceExperiment(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
Name
— (String
)The name of the inference experiment to be updated.
Schedule
— (map
)The duration for which the inference experiment will run. If the status of the inference experiment is
Created
, then you can update both the start and end dates. If the status of the inference experiment isRunning
, then you can update only the end date.StartTime
— (Date
)The timestamp at which the inference experiment started or will start.
EndTime
— (Date
)The timestamp at which the inference experiment ended or will end.
Description
— (String
)The description of the inference experiment.
ModelVariants
— (Array<map>
)An array of
ModelVariantConfig
objects. There is one for each variant, whose infrastructure configuration you want to update.ModelName
— required — (String
)The name of the Amazon SageMaker Model entity.
VariantName
— required — (String
)The name of the variant.
InfrastructureConfig
— required — (map
)The configuration for the infrastructure that the model will be deployed to.
InfrastructureType
— required — (String
)The inference option to which to deploy your model. Possible values are the following:
-
RealTime
: Deploy to real-time inference.
"RealTimeInference"
-
RealTimeInferenceConfig
— required — (map
)The infrastructure configuration for deploying the model to real-time inference.
InstanceType
— required — (String
)The instance type the model is deployed to.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
InstanceCount
— required — (Integer
)The number of instances of the type specified by
InstanceType
.
DataStorageConfig
— (map
)The Amazon S3 location and configuration for storing inference request and response data.
Destination
— required — (String
)The Amazon S3 bucket where the inference request and response data is stored.
KmsKey
— (String
)The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
ContentType
— (map
)Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker will by default base64 encode when capturing the data.
CsvContentTypes
— (Array<String>
)The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.
JsonContentTypes
— (Array<String>
)The list of all content type headers that SageMaker will treat as JSON and capture accordingly.
ShadowModeConfig
— (map
)The configuration of
ShadowMode
inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.SourceModelVariantName
— required — (String
)The name of the production variant, which takes all the inference requests.
ShadowModelVariants
— required — (Array<map>
)List of shadow variant configurations.
ShadowModelVariantName
— required — (String
)The name of the shadow variant.
SamplingPercentage
— required — (Integer
)The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:InferenceExperimentArn
— (String
)The ARN of the updated inference experiment.
-
(AWS.Response)
—
Returns:
updateMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request
Updates properties of an existing MLflow Tracking Server.
Service Reference:
Examples:
Calling the updateMlflowTrackingServer operation
var params = { TrackingServerName: 'STRING_VALUE', /* required */ ArtifactStoreUri: 'STRING_VALUE', AutomaticModelRegistration: true || false, TrackingServerSize: Small | Medium | Large, WeeklyMaintenanceWindowStart: 'STRING_VALUE' }; sagemaker.updateMlflowTrackingServer(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrackingServerName
— (String
)The name of the MLflow Tracking Server to update.
ArtifactStoreUri
— (String
)The new S3 URI for the general purpose bucket to use as the artifact store for the MLflow Tracking Server.
TrackingServerSize
— (String
)The new size for the MLflow Tracking Server.
Possible values include:"Small"
"Medium"
"Large"
AutomaticModelRegistration
— (Boolean
)Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to
True
. To disable automatic model registration, set this value toFalse
. If not specified,AutomaticModelRegistration
defaults toFalse
WeeklyMaintenanceWindowStart
— (String
)The new weekly maintenance window start day and time to update. The maintenance window day and time should be in Coordinated Universal Time (UTC) 24-hour standard time. For example: TUE:03:30.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrackingServerArn
— (String
)The ARN of the updated MLflow Tracking Server.
-
(AWS.Response)
—
Returns:
updateModelCard(params = {}, callback) ⇒ AWS.Request
Update an Amazon SageMaker Model Card.
You cannot update both model card content and model card status in a single call.
Service Reference:
Examples:
Calling the updateModelCard operation
var params = { ModelCardName: 'STRING_VALUE', /* required */ Content: 'STRING_VALUE', ModelCardStatus: Draft | PendingReview | Approved | Archived }; sagemaker.updateModelCard(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelCardName
— (String
)The name or Amazon Resource Name (ARN) of the model card to update.
Content
— (String
)The updated model card content. Content must be in model card JSON schema and provided as a string.
When updating model card content, be sure to include the full content and not just updated content.
ModelCardStatus
— (String
)The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft
: The model card is a work in progress. -
PendingReview
: The model card is pending review. -
Approved
: The model card is approved. -
Archived
: The model card is archived. No more updates should be made to the model card, but it can still be exported.
"Draft"
"PendingReview"
"Approved"
"Archived"
-
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelCardArn
— (String
)The Amazon Resource Name (ARN) of the updated model card.
-
(AWS.Response)
—
Returns:
updateModelPackage(params = {}, callback) ⇒ AWS.Request
Updates a versioned model.
Service Reference:
Examples:
Calling the updateModelPackage operation
var params = { ModelPackageArn: 'STRING_VALUE', /* required */ AdditionalInferenceSpecificationsToAdd: [ { Containers: [ /* required */ { Image: 'STRING_VALUE', /* required */ AdditionalS3DataSource: { S3DataType: S3Object | S3Prefix, /* required */ S3Uri: 'STRING_VALUE', /* required */ CompressionType: None | Gzip }, ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Framework: 'STRING_VALUE', FrameworkVersion: 'STRING_VALUE', ImageDigest: 'STRING_VALUE', ModelDataSource: { S3DataSource: { CompressionType: None | Gzip, /* required */ S3DataType: S3Prefix | S3Object, /* required */ S3Uri: 'STRING_VALUE', /* required */ HubAccessConfig: { HubContentArn: 'STRING_VALUE' /* required */ }, ModelAccessConfig: { AcceptEula: true || false /* required */ } } }, ModelDataUrl: 'STRING_VALUE', ModelInput: { DataInputConfig: 'STRING_VALUE' /* required */ }, NearestModelName: 'STRING_VALUE', ProductId: 'STRING_VALUE' }, /* more items */ ], Name: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', SupportedContentTypes: [ 'STRING_VALUE', /* more items */ ], SupportedRealtimeInferenceInstanceTypes: [ ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.12xlarge | ml.m5d.24xlarge | ml.c4.large | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.large | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.12xlarge | ml.r5.24xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.12xlarge | ml.r5d.24xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.dl1.24xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.p4d.24xlarge | ml.c7g.large | ml.c7g.xlarge | ml.c7g.2xlarge | ml.c7g.4xlarge | ml.c7g.8xlarge | ml.c7g.12xlarge | ml.c7g.16xlarge | ml.m6g.large | ml.m6g.xlarge | ml.m6g.2xlarge | ml.m6g.4xlarge | ml.m6g.8xlarge | ml.m6g.12xlarge | ml.m6g.16xlarge | ml.m6gd.large | ml.m6gd.xlarge | ml.m6gd.2xlarge | ml.m6gd.4xlarge | ml.m6gd.8xlarge | ml.m6gd.12xlarge | ml.m6gd.16xlarge | ml.c6g.large | ml.c6g.xlarge | ml.c6g.2xlarge | ml.c6g.4xlarge | ml.c6g.8xlarge | ml.c6g.12xlarge | ml.c6g.16xlarge | ml.c6gd.large | ml.c6gd.xlarge | ml.c6gd.2xlarge | ml.c6gd.4xlarge | ml.c6gd.8xlarge | ml.c6gd.12xlarge | ml.c6gd.16xlarge | ml.c6gn.large | ml.c6gn.xlarge | ml.c6gn.2xlarge | ml.c6gn.4xlarge | ml.c6gn.8xlarge | ml.c6gn.12xlarge | ml.c6gn.16xlarge | ml.r6g.large | ml.r6g.xlarge | ml.r6g.2xlarge | ml.r6g.4xlarge | ml.r6g.8xlarge | ml.r6g.12xlarge | ml.r6g.16xlarge | ml.r6gd.large | ml.r6gd.xlarge | ml.r6gd.2xlarge | ml.r6gd.4xlarge | ml.r6gd.8xlarge | ml.r6gd.12xlarge | ml.r6gd.16xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.inf2.xlarge | ml.inf2.8xlarge | ml.inf2.24xlarge | ml.inf2.48xlarge | ml.p5.48xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge, /* more items */ ], SupportedResponseMIMETypes: [ 'STRING_VALUE', /* more items */ ], SupportedTransformInstanceTypes: [ ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge, /* more items */ ] }, /* more items */ ], ApprovalDescription: 'STRING_VALUE', CustomerMetadataProperties: { '<CustomerMetadataKey>': 'STRING_VALUE', /* '<CustomerMetadataKey>': ... */ }, CustomerMetadataPropertiesToRemove: [ 'STRING_VALUE', /* more items */ ], InferenceSpecification: { Containers: [ /* required */ { Image: 'STRING_VALUE', /* required */ AdditionalS3DataSource: { S3DataType: S3Object | S3Prefix, /* required */ S3Uri: 'STRING_VALUE', /* required */ CompressionType: None | Gzip }, ContainerHostname: 'STRING_VALUE', Environment: { '<EnvironmentKey>': 'STRING_VALUE', /* '<EnvironmentKey>': ... */ }, Framework: 'STRING_VALUE', FrameworkVersion: 'STRING_VALUE', ImageDigest: 'STRING_VALUE', ModelDataSource: { S3DataSource: { CompressionType: None | Gzip, /* required */ S3DataType: S3Prefix | S3Object, /* required */ S3Uri: 'STRING_VALUE', /* required */ HubAccessConfig: { HubContentArn: 'STRING_VALUE' /* required */ }, ModelAccessConfig: { AcceptEula: true || false /* required */ } } }, ModelDataUrl: 'STRING_VALUE', ModelInput: { DataInputConfig: 'STRING_VALUE' /* required */ }, NearestModelName: 'STRING_VALUE', ProductId: 'STRING_VALUE' }, /* more items */ ], SupportedContentTypes: [ 'STRING_VALUE', /* more items */ ], SupportedRealtimeInferenceInstanceTypes: [ ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.12xlarge | ml.m5d.24xlarge | ml.c4.large | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.large | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.12xlarge | ml.r5.24xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.12xlarge | ml.r5d.24xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.dl1.24xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.p4d.24xlarge | ml.c7g.large | ml.c7g.xlarge | ml.c7g.2xlarge | ml.c7g.4xlarge | ml.c7g.8xlarge | ml.c7g.12xlarge | ml.c7g.16xlarge | ml.m6g.large | ml.m6g.xlarge | ml.m6g.2xlarge | ml.m6g.4xlarge | ml.m6g.8xlarge | ml.m6g.12xlarge | ml.m6g.16xlarge | ml.m6gd.large | ml.m6gd.xlarge | ml.m6gd.2xlarge | ml.m6gd.4xlarge | ml.m6gd.8xlarge | ml.m6gd.12xlarge | ml.m6gd.16xlarge | ml.c6g.large | ml.c6g.xlarge | ml.c6g.2xlarge | ml.c6g.4xlarge | ml.c6g.8xlarge | ml.c6g.12xlarge | ml.c6g.16xlarge | ml.c6gd.large | ml.c6gd.xlarge | ml.c6gd.2xlarge | ml.c6gd.4xlarge | ml.c6gd.8xlarge | ml.c6gd.12xlarge | ml.c6gd.16xlarge | ml.c6gn.large | ml.c6gn.xlarge | ml.c6gn.2xlarge | ml.c6gn.4xlarge | ml.c6gn.8xlarge | ml.c6gn.12xlarge | ml.c6gn.16xlarge | ml.r6g.large | ml.r6g.xlarge | ml.r6g.2xlarge | ml.r6g.4xlarge | ml.r6g.8xlarge | ml.r6g.12xlarge | ml.r6g.16xlarge | ml.r6gd.large | ml.r6gd.xlarge | ml.r6gd.2xlarge | ml.r6gd.4xlarge | ml.r6gd.8xlarge | ml.r6gd.12xlarge | ml.r6gd.16xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.inf2.xlarge | ml.inf2.8xlarge | ml.inf2.24xlarge | ml.inf2.48xlarge | ml.p5.48xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge, /* more items */ ], SupportedResponseMIMETypes: [ 'STRING_VALUE', /* more items */ ], SupportedTransformInstanceTypes: [ ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.12xlarge | ml.g5.16xlarge | ml.g5.24xlarge | ml.g5.48xlarge, /* more items */ ] }, ModelApprovalStatus: Approved | Rejected | PendingManualApproval, ModelCard: { ModelCardContent: 'STRING_VALUE', ModelCardStatus: Draft | PendingReview | Approved | Archived }, SourceUri: 'STRING_VALUE' }; sagemaker.updateModelPackage(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ModelPackageArn
— (String
)The Amazon Resource Name (ARN) of the model package.
ModelApprovalStatus
— (String
)The approval status of the model.
Possible values include:"Approved"
"Rejected"
"PendingManualApproval"
ApprovalDescription
— (String
)A description for the approval status of the model.
CustomerMetadataProperties
— (map<String>
)The metadata properties associated with the model package versions.
CustomerMetadataPropertiesToRemove
— (Array<String>
)The metadata properties associated with the model package versions to remove.
AdditionalInferenceSpecificationsToAdd
— (Array<map>
)An array of additional Inference Specification objects to be added to the existing array additional Inference Specification. Total number of additional Inference Specifications can not exceed 15. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
Name
— required — (String
)A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description
— (String
)A description of the additional Inference specification
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
InferenceSpecification
— (map
)Specifies details about inference jobs that you can run with models based on this model package, including the following information:
-
The Amazon ECR paths of containers that contain the inference code and model artifacts.
-
The instance types that the model package supports for transform jobs and real-time endpoints used for inference.
-
The input and output content formats that the model package supports for inference.
Containers
— required — (Array<map>
)The Amazon ECR registry path of the Docker image that contains the inference code.
ContainerHostname
— (String
)The DNS host name for the Docker container.
Image
— required — (String
)The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.ImageDigest
— (String
)An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl
— (String
)The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single
gzip
compressed tar archive (.tar.gz
suffix).Note: The model artifacts must be in an S3 bucket that is in the same region as the model package.ModelDataSource
— (map
)Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource
— (map
)Specifies the S3 location of ML model data to deploy.
S3Uri
— required — (String
)Specifies the S3 path of ML model data to deploy.
S3DataType
— required — (String
)Specifies the type of ML model data to deploy.
If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified byS3Uri
always ends with a forward slash (/).If you choose
Possible values include:S3Object
,S3Uri
identifies an object that is the ML model data to deploy."S3Prefix"
"S3Object"
CompressionType
— required — (String
)Specifies how the ML model data is prepared.
If you choose
Gzip
and chooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.If you choose
None
and choooseS3Object
as the value ofS3DataType
,S3Uri
identifies an object that represents an uncompressed ML model to deploy.If you choose None and choose
S3Prefix
as the value ofS3DataType
,S3Uri
identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
-
If you choose
S3Object
as the value ofS3DataType
, then SageMaker will split the key of the S3 object referenced byS3Uri
by slash (/), and use the last part as the filename of the file holding the content of the S3 object. -
If you choose
S3Prefix
as the value ofS3DataType
, then for each S3 object under the key name pefix referenced byS3Uri
, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to/opt/ml/model
) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. -
Do not use any of the following as file names or directory names:
-
An empty or blank string
-
A string which contains null bytes
-
A string longer than 255 bytes
-
A single dot (
.
) -
A double dot (
..
)
-
-
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects
s3://mybucket/model/weights
ands3://mybucket/model/weights/part1
and you specifys3://mybucket/model/
as the value ofS3Uri
andS3Prefix
as the value ofS3DataType
, then it will result in name clash between/opt/ml/model/weights
(a regular file) and/opt/ml/model/weights/
(a directory). -
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
"None"
"Gzip"
-
ModelAccessConfig
— (map
)Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig
. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.AcceptEula
— required — (Boolean
)Specifies agreement to the model end-user license agreement (EULA). The
AcceptEula
value must be explicitly defined asTrue
in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig
— (map
)Configuration information for hub access.
HubContentArn
— required — (String
)The ARN of the hub content for which deployment access is allowed.
ProductId
— (String
)The Amazon Web Services Marketplace product ID of the model package.
Environment
— (map<String>
)The environment variables to set in the Docker container. Each key and value in the
Environment
string to string map can have length of up to 1024. We support up to 16 entries in the map.ModelInput
— (map
)A structure with Model Input details.
DataInputConfig
— required — (String
)The input configuration object for the model.
Framework
— (String
)The machine learning framework of the model package container image.
FrameworkVersion
— (String
)The framework version of the Model Package Container Image.
NearestModelName
— (String
)The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling
ListModelMetadata
.AdditionalS3DataSource
— (map
)The additional data source that is used during inference in the Docker container for your model package.
S3DataType
— required — (String
)The data type of the additional data source that you specify for use in inference or training.
Possible values include:"S3Object"
"S3Prefix"
S3Uri
— required — (String
)The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType
— (String
)The type of compression used for an additional data source used in inference or training. Specify
Possible values include:None
if your additional data source is not compressed."None"
"Gzip"
SupportedTransformInstanceTypes
— (Array<String>
)A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes
— (Array<String>
)A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes
— (Array<String>
)The supported MIME types for the input data.
SupportedResponseMIMETypes
— (Array<String>
)The supported MIME types for the output data.
-
SourceUri
— (String
)The URI of the source for the model package.
ModelCard
— (map
)The model card associated with the model package. Since
ModelPackageModelCard
is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema ofModelCard
. TheModelPackageModelCard
schema does not includemodel_package_details
, andmodel_overview
is composed of themodel_creator
andmodel_artifact
properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.ModelCardContent
— (String
)The content of the model card. The content must follow the schema described in Model Package Model Card Schema.
ModelCardStatus
— (String
)The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
-
Draft
: The model card is a work in progress. -
PendingReview
: The model card is pending review. -
Approved
: The model card is approved. -
Archived
: The model card is archived. No more updates can be made to the model card content. If you try to update the model card content, you will receive the messageModel Card is in Archived state
.
"Draft"
"PendingReview"
"Approved"
"Archived"
-
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ModelPackageArn
— (String
)The Amazon Resource Name (ARN) of the model.
-
(AWS.Response)
—
Returns:
updateMonitoringAlert(params = {}, callback) ⇒ AWS.Request
Update the parameters of a model monitor alert.
Service Reference:
Examples:
Calling the updateMonitoringAlert operation
var params = { DatapointsToAlert: 'NUMBER_VALUE', /* required */ EvaluationPeriod: 'NUMBER_VALUE', /* required */ MonitoringAlertName: 'STRING_VALUE', /* required */ MonitoringScheduleName: 'STRING_VALUE' /* required */ }; sagemaker.updateMonitoringAlert(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)The name of a monitoring schedule.
MonitoringAlertName
— (String
)The name of a monitoring alert.
DatapointsToAlert
— (Integer
)Within
EvaluationPeriod
, how many execution failures will raise an alert.EvaluationPeriod
— (Integer
)The number of most recent monitoring executions to consider when evaluating alert status.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:MonitoringScheduleArn
— (String
)The Amazon Resource Name (ARN) of the monitoring schedule.
MonitoringAlertName
— (String
)The name of a monitoring alert.
-
(AWS.Response)
—
Returns:
updateMonitoringSchedule(params = {}, callback) ⇒ AWS.Request
Updates a previously created schedule.
Service Reference:
Examples:
Calling the updateMonitoringSchedule operation
var params = { MonitoringScheduleConfig: { /* required */ MonitoringJobDefinition: { MonitoringAppSpecification: { /* required */ ImageUri: 'STRING_VALUE', /* required */ ContainerArguments: [ 'STRING_VALUE', /* more items */ ], ContainerEntrypoint: [ 'STRING_VALUE', /* more items */ ], PostAnalyticsProcessorSourceUri: 'STRING_VALUE', RecordPreprocessorSourceUri: 'STRING_VALUE' }, MonitoringInputs: [ /* required */ { BatchTransformInput: { DataCapturedDestinationS3Uri: 'STRING_VALUE', /* required */ DatasetFormat: { /* required */ Csv: { Header: true || false }, Json: { Line: true || false }, Parquet: { } }, LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' }, EndpointInput: { EndpointName: 'STRING_VALUE', /* required */ LocalPath: 'STRING_VALUE', /* required */ EndTimeOffset: 'STRING_VALUE', ExcludeFeaturesAttribute: 'STRING_VALUE', FeaturesAttribute: 'STRING_VALUE', InferenceAttribute: 'STRING_VALUE', ProbabilityAttribute: 'STRING_VALUE', ProbabilityThresholdAttribute: 'NUMBER_VALUE', S3DataDistributionType: FullyReplicated | ShardedByS3Key, S3InputMode: Pipe | File, StartTimeOffset: 'STRING_VALUE' } }, /* more items */ ], MonitoringOutputConfig: { /* required */ MonitoringOutputs: [ /* required */ { S3Output: { /* required */ LocalPath: 'STRING_VALUE', /* required */ S3Uri: 'STRING_VALUE', /* required */ S3UploadMode: Continuous | EndOfJob } }, /* more items */ ], KmsKeyId: 'STRING_VALUE' }, MonitoringResources: { /* required */ ClusterConfig: { /* required */ InstanceCount: 'NUMBER_VALUE', /* required */ InstanceType: ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge, /* required */ VolumeSizeInGB: 'NUMBER_VALUE', /* required */ VolumeKmsKeyId: 'STRING_VALUE' } }, RoleArn: 'STRING_VALUE', /* required */ BaselineConfig: { BaseliningJobName: 'STRING_VALUE', ConstraintsResource: { S3Uri: 'STRING_VALUE' }, StatisticsResource: { S3Uri: 'STRING_VALUE' } }, Environment: { '<ProcessingEnvironmentKey>': 'STRING_VALUE', /* '<ProcessingEnvironmentKey>': ... */ }, NetworkConfig: { EnableInterContainerTrafficEncryption: true || false, EnableNetworkIsolation: true || false, VpcConfig: { SecurityGroupIds: [ /* required */ 'STRING_VALUE', /* more items */ ], Subnets: [ /* required */ 'STRING_VALUE', /* more items */ ] } }, StoppingCondition: { MaxRuntimeInSeconds: 'NUMBER_VALUE' /* required */ } }, MonitoringJobDefinitionName: 'STRING_VALUE', MonitoringType: DataQuality | ModelQuality | ModelBias | ModelExplainability, ScheduleConfig: { ScheduleExpression: 'STRING_VALUE', /* required */ DataAnalysisEndTime: 'STRING_VALUE', DataAnalysisStartTime: 'STRING_VALUE' } }, MonitoringScheduleName: 'STRING_VALUE' /* required */ }; sagemaker.updateMonitoringSchedule(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
MonitoringScheduleName
— (String
)The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
MonitoringScheduleConfig
— (map
)The configuration object that specifies the monitoring schedule and defines the monitoring job.
ScheduleConfig
— (map
)Configures the monitoring schedule.
ScheduleExpression
— required — (String
)A cron expression that describes details about the monitoring schedule.
The supported cron expressions are:
-
If you want to set the job to start every hour, use the following:
Hourly: cron(0 * ? * * *)
-
If you want to start the job daily:
cron(0 [00-23] ? * * *)
-
If you want to run the job one time, immediately, use the following keyword:
NOW
For example, the following are valid cron expressions:
-
Daily at noon UTC:
cron(0 12 ? * * *)
-
Daily at midnight UTC:
cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
-
Every 12 hours, starting at 5pm UTC:
cron(0 17/12 ? * * *)
-
Every two hours starting at midnight:
cron(0 0/2 ? * * *)
Note:- Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
- We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
You can also specify the keyword
NOW
to run the monitoring job immediately, one time, without recurring.-
DataAnalysisStartTime
— (String
)Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the
ScheduleExpression
parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify:"-PT5H"
.The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the
DataAnalysisEndTime
parameter.If you set
ScheduleExpression
toNOW
, this parameter is required.DataAnalysisEndTime
— (String
)Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the
ScheduleExpression
parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify:"-PT1H"
.The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the
DataAnalysisStartTime
parameter.If you set
ScheduleExpression
toNOW
, this parameter is required.
MonitoringJobDefinition
— (map
)Defines the monitoring job.
BaselineConfig
— (map
)Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName
— (String
)The name of the job that performs baselining for the monitoring job.
ConstraintsResource
— (map
)The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri
— (String
)The Amazon S3 URI for the constraints resource.
StatisticsResource
— (map
)The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri
— (String
)The Amazon S3 URI for the statistics resource.
MonitoringInputs
— required — (Array<map>
)The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
EndpointInput
— (map
)The endpoint for a monitoring job.
EndpointName
— required — (String
)An endpoint in customer's account which has enabled
DataCaptureConfig
enabled.LocalPath
— required — (String
)Path to the filesystem where the endpoint data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
BatchTransformInput
— (map
)Input object for the batch transform job.
DataCapturedDestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
DatasetFormat
— required — (map
)The dataset format for your batch transform job.
Csv
— (map
)The CSV dataset used in the monitoring job.
Header
— (Boolean
)Indicates if the CSV data has a header.
Json
— (map
)The JSON dataset used in the monitoring job
Line
— (Boolean
)Indicates if the file should be read as a JSON object per line.
Parquet
— (map
)The Parquet dataset used in the monitoring job
LocalPath
— required — (String
)Path to the filesystem where the batch transform data is available to the container.
S3InputMode
— (String
)Whether the
Possible values include:Pipe
orFile
is used as the input mode for transferring data for the monitoring job.Pipe
mode is recommended for large datasets.File
mode is useful for small files that fit in memory. Defaults toFile
."Pipe"
"File"
S3DataDistributionType
— (String
)Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
Possible values include:FullyReplicated
"FullyReplicated"
"ShardedByS3Key"
FeaturesAttribute
— (String
)The attributes of the input data that are the input features.
InferenceAttribute
— (String
)The attribute of the input data that represents the ground truth label.
ProbabilityAttribute
— (String
)In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute
— (Float
)The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset
— (String
)If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
EndTimeOffset
— (String
)If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
ExcludeFeaturesAttribute
— (String
)The attributes of the input data to exclude from the analysis.
MonitoringOutputConfig
— required — (map
)The array of outputs from the monitoring job to be uploaded to Amazon S3.
MonitoringOutputs
— required — (Array<map>
)Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
S3Output
— required — (map
)The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath
— required — (String
)The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode
— (String
)Whether to upload the results of the monitoring job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
KmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources
— required — (map
)Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig
— required — (map
)The configuration for the cluster resources used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId
— (String
)The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification
— required — (map
)Configures the monitoring job to run a specified Docker container image.
ImageUri
— required — (String
)The container image to be run by the monitoring job.
ContainerEntrypoint
— (Array<String>
)Specifies the entrypoint for a container used to run the monitoring job.
ContainerArguments
— (Array<String>
)An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri
— (String
)An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri
— (String
)An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition
— (map
)Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds
— required — (Integer
)The maximum runtime allowed in seconds.
Note: TheMaxRuntimeInSeconds
cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment
— (map<String>
)Sets the environment variables in the Docker container.
NetworkConfig
— (map
)Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— required — (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName
— (String
)The name of the monitoring job definition to schedule.
MonitoringType
— (String
)The type of the monitoring job definition to schedule.
Possible values include:"DataQuality"
"ModelQuality"
"ModelBias"
"ModelExplainability"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:MonitoringScheduleArn
— (String
)The Amazon Resource Name (ARN) of the monitoring schedule.
-
(AWS.Response)
—
Returns:
updateNotebookInstance(params = {}, callback) ⇒ AWS.Request
Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.
Service Reference:
Examples:
Calling the updateNotebookInstance operation
var params = { NotebookInstanceName: 'STRING_VALUE', /* required */ AcceleratorTypes: [ ml.eia1.medium | ml.eia1.large | ml.eia1.xlarge | ml.eia2.medium | ml.eia2.large | ml.eia2.xlarge, /* more items */ ], AdditionalCodeRepositories: [ 'STRING_VALUE', /* more items */ ], DefaultCodeRepository: 'STRING_VALUE', DisassociateAcceleratorTypes: true || false, DisassociateAdditionalCodeRepositories: true || false, DisassociateDefaultCodeRepository: true || false, DisassociateLifecycleConfig: true || false, InstanceMetadataServiceConfiguration: { MinimumInstanceMetadataServiceVersion: 'STRING_VALUE' /* required */ }, InstanceType: ml.t2.medium | ml.t2.large | ml.t2.xlarge | ml.t2.2xlarge | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.c5d.xlarge | ml.c5d.2xlarge | ml.c5d.4xlarge | ml.c5d.9xlarge | ml.c5d.18xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.inf1.xlarge | ml.inf1.2xlarge | ml.inf1.6xlarge | ml.inf1.24xlarge | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge, LifecycleConfigName: 'STRING_VALUE', RoleArn: 'STRING_VALUE', RootAccess: Enabled | Disabled, VolumeSizeInGB: 'NUMBER_VALUE' }; sagemaker.updateNotebookInstance(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceName
— (String
)The name of the notebook instance to update.
InstanceType
— (String
)The Amazon ML compute instance type.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access the notebook instance. For more information, see SageMaker Roles.
Note: To be able to pass this role to SageMaker, the caller of this API must have theiam:PassRole
permission.LifecycleConfigName
— (String
)The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
DisassociateLifecycleConfig
— (Boolean
)Set to
true
to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so SageMaker can't determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can't decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.
DefaultCodeRepository
— (String
)The Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
AdditionalCodeRepositories
— (Array<String>
)An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
AcceleratorTypes
— (Array<String>
)A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
DisassociateAcceleratorTypes
— (Boolean
)A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.
DisassociateDefaultCodeRepository
— (Boolean
)The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
DisassociateAdditionalCodeRepositories
— (Boolean
)A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
RootAccess
— (String
)Whether root access is enabled or disabled for users of the notebook instance. The default value is
Enabled
.Note: If you set this toPossible values include:Disabled
, users don't have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions."Enabled"
"Disabled"
InstanceMetadataServiceConfiguration
— (map
)Information on the IMDS configuration of the notebook instance
MinimumInstanceMetadataServiceVersion
— required — (String
)Indicates the minimum IMDS version that the notebook instance supports. When passed as part of
CreateNotebookInstance
, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part ofUpdateNotebookInstance
, there is no default.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
updateNotebookInstanceLifecycleConfig(params = {}, callback) ⇒ AWS.Request
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
Service Reference:
Examples:
Calling the updateNotebookInstanceLifecycleConfig operation
var params = { NotebookInstanceLifecycleConfigName: 'STRING_VALUE', /* required */ OnCreate: [ { Content: 'STRING_VALUE' }, /* more items */ ], OnStart: [ { Content: 'STRING_VALUE' }, /* more items */ ] }; sagemaker.updateNotebookInstanceLifecycleConfig(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
NotebookInstanceLifecycleConfigName
— (String
)The name of the lifecycle configuration.
OnCreate
— (Array<map>
)The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
Content
— (String
)A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
OnStart
— (Array<map>
)The shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
Content
— (String
)A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs.
-
(AWS.Response)
—
Returns:
updatePipeline(params = {}, callback) ⇒ AWS.Request
Updates a pipeline.
Service Reference:
Examples:
Calling the updatePipeline operation
var params = { PipelineName: 'STRING_VALUE', /* required */ ParallelismConfiguration: { MaxParallelExecutionSteps: 'NUMBER_VALUE' /* required */ }, PipelineDefinition: 'STRING_VALUE', PipelineDefinitionS3Location: { Bucket: 'STRING_VALUE', /* required */ ObjectKey: 'STRING_VALUE', /* required */ VersionId: 'STRING_VALUE' }, PipelineDescription: 'STRING_VALUE', PipelineDisplayName: 'STRING_VALUE', RoleArn: 'STRING_VALUE' }; sagemaker.updatePipeline(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineName
— (String
)The name of the pipeline to update.
PipelineDisplayName
— (String
)The display name of the pipeline.
PipelineDefinition
— (String
)The JSON pipeline definition.
PipelineDefinitionS3Location
— (map
)The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.
Bucket
— required — (String
)Name of the S3 bucket.
ObjectKey
— required — (String
)The object key (or key name) uniquely identifies the object in an S3 bucket.
VersionId
— (String
)Version Id of the pipeline definition file. If not specified, Amazon SageMaker will retrieve the latest version.
PipelineDescription
— (String
)The description of the pipeline.
RoleArn
— (String
)The Amazon Resource Name (ARN) that the pipeline uses to execute.
ParallelismConfiguration
— (map
)If specified, it applies to all executions of this pipeline by default.
MaxParallelExecutionSteps
— required — (Integer
)The max number of steps that can be executed in parallel.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineArn
— (String
)The Amazon Resource Name (ARN) of the updated pipeline.
-
(AWS.Response)
—
Returns:
updatePipelineExecution(params = {}, callback) ⇒ AWS.Request
Updates a pipeline execution.
Service Reference:
Examples:
Calling the updatePipelineExecution operation
var params = { PipelineExecutionArn: 'STRING_VALUE', /* required */ ParallelismConfiguration: { MaxParallelExecutionSteps: 'NUMBER_VALUE' /* required */ }, PipelineExecutionDescription: 'STRING_VALUE', PipelineExecutionDisplayName: 'STRING_VALUE' }; sagemaker.updatePipelineExecution(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionDescription
— (String
)The description of the pipeline execution.
PipelineExecutionDisplayName
— (String
)The display name of the pipeline execution.
ParallelismConfiguration
— (map
)This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.
MaxParallelExecutionSteps
— required — (Integer
)The max number of steps that can be executed in parallel.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:PipelineExecutionArn
— (String
)The Amazon Resource Name (ARN) of the updated pipeline execution.
-
(AWS.Response)
—
Returns:
updateProject(params = {}, callback) ⇒ AWS.Request
Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.
Note: You must not update a project that is in use. If you update theServiceCatalogProvisioningUpdateDetails
of a project that is active or being created, or updated, you may lose resources already created by the project.Service Reference:
Examples:
Calling the updateProject operation
var params = { ProjectName: 'STRING_VALUE', /* required */ ProjectDescription: 'STRING_VALUE', ServiceCatalogProvisioningUpdateDetails: { ProvisioningArtifactId: 'STRING_VALUE', ProvisioningParameters: [ { Key: 'STRING_VALUE', Value: 'STRING_VALUE' }, /* more items */ ] }, Tags: [ { Key: 'STRING_VALUE', /* required */ Value: 'STRING_VALUE' /* required */ }, /* more items */ ] }; sagemaker.updateProject(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
ProjectName
— (String
)The name of the project.
ProjectDescription
— (String
)The description for the project.
ServiceCatalogProvisioningUpdateDetails
— (map
)The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.
ProvisioningArtifactId
— (String
)The ID of the provisioning artifact.
ProvisioningParameters
— (Array<map>
)A list of key value pairs that you specify when you provision a product.
Key
— (String
)The key that identifies a provisioning parameter.
Value
— (String
)The value of the provisioning parameter.
Tags
— (Array<map>
)An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources. In addition, the project must have tag update constraints set in order to include this parameter in the request. For more information, see Amazon Web Services Service Catalog Tag Update Constraints.
Key
— required — (String
)The tag key. Tag keys must be unique per resource.
Value
— required — (String
)The tag value.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ProjectArn
— (String
)The Amazon Resource Name (ARN) of the project.
-
(AWS.Response)
—
Returns:
updateSpace(params = {}, callback) ⇒ AWS.Request
Updates the settings of a space.
Service Reference:
Examples:
Calling the updateSpace operation
var params = { DomainId: 'STRING_VALUE', /* required */ SpaceName: 'STRING_VALUE', /* required */ SpaceDisplayName: 'STRING_VALUE', SpaceSettings: { AppType: JupyterServer | KernelGateway | DetailedProfiler | TensorBoard | CodeEditor | JupyterLab | RStudioServerPro | RSessionGateway | Canvas, CodeEditorAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE' } }, DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } }, CustomFileSystems: [ { EFSFileSystem: { FileSystemId: 'STRING_VALUE' /* required */ } }, /* more items */ ], JupyterLabAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } }, JupyterServerAppSettings: { CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, KernelGatewayAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, SpaceStorageSettings: { EbsStorageSettings: { EbsVolumeSizeInGb: 'NUMBER_VALUE' /* required */ } } } }; sagemaker.updateSpace(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The ID of the associated domain.
SpaceName
— (String
)The name of the space.
SpaceSettings
— (map
)A collection of space settings.
JupyterServerAppSettings
— (map
)The JupyterServer app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The KernelGateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
CodeEditorAppSettings
— (map
)The Code Editor application settings.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of CodeEditor applications in a space.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of JupyterLab applications in a space.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
AppType
— (String
)The type of app created within the space.
Possible values include:"JupyterServer"
"KernelGateway"
"DetailedProfiler"
"TensorBoard"
"CodeEditor"
"JupyterLab"
"RStudioServerPro"
"RSessionGateway"
"Canvas"
SpaceStorageSettings
— (map
)The storage settings for a space.
EbsStorageSettings
— (map
)A collection of EBS storage settings for a space.
EbsVolumeSizeInGb
— required — (Integer
)The size of an EBS storage volume for a space.
CustomFileSystems
— (Array<map>
)A file system, created by you, that you assign to a space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystem
— (map
)A custom file system in Amazon EFS.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
SpaceDisplayName
— (String
)The name of the space that appears in the Amazon SageMaker Studio UI.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:SpaceArn
— (String
)The space's Amazon Resource Name (ARN).
-
(AWS.Response)
—
Returns:
updateTrainingJob(params = {}, callback) ⇒ AWS.Request
Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
Service Reference:
Examples:
Calling the updateTrainingJob operation
var params = { TrainingJobName: 'STRING_VALUE', /* required */ ProfilerConfig: { DisableProfiler: true || false, ProfilingIntervalInMilliseconds: 'NUMBER_VALUE', ProfilingParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ }, S3OutputPath: 'STRING_VALUE' }, ProfilerRuleConfigurations: [ { RuleConfigurationName: 'STRING_VALUE', /* required */ RuleEvaluatorImage: 'STRING_VALUE', /* required */ InstanceType: ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m4.xlarge | ml.m4.2xlarge | ml.m4.4xlarge | ml.m4.10xlarge | ml.m4.16xlarge | ml.c4.xlarge | ml.c4.2xlarge | ml.c4.4xlarge | ml.c4.8xlarge | ml.p2.xlarge | ml.p2.8xlarge | ml.p2.16xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.18xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.12xlarge | ml.m5.24xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.r5d.large | ml.r5d.xlarge | ml.r5d.2xlarge | ml.r5d.4xlarge | ml.r5d.8xlarge | ml.r5d.12xlarge | ml.r5d.16xlarge | ml.r5d.24xlarge, LocalPath: 'STRING_VALUE', RuleParameters: { '<ConfigKey>': 'STRING_VALUE', /* '<ConfigKey>': ... */ }, S3OutputPath: 'STRING_VALUE', VolumeSizeInGB: 'NUMBER_VALUE' }, /* more items */ ], RemoteDebugConfig: { EnableRemoteDebug: true || false }, ResourceConfig: { KeepAlivePeriodInSeconds: 'NUMBER_VALUE' /* required */ } }; sagemaker.updateTrainingJob(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrainingJobName
— (String
)The name of a training job to update the Debugger profiling configuration.
ProfilerConfig
— (map
)Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath
— (String
)Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds
— (Integer
)A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters
— (map<String>
)Configuration information for capturing framework metrics. Available key strings for different profiling options are
DetailedProfilingConfig
,PythonProfilingConfig
, andDataLoaderProfilingConfig
. The following codes are configuration structures for theProfilingParameters
parameter. To learn more about how to configure theProfilingParameters
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.DisableProfiler
— (Boolean
)To turn off Amazon SageMaker Debugger monitoring and profiling while a training job is in progress, set to
True
.
ProfilerRuleConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container Registry Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a custom rule for profiling a training job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
ResourceConfig
— (map
)The training job
ResourceConfig
to update warm pool retention length.KeepAlivePeriodInSeconds
— required — (Integer
)The
KeepAlivePeriodInSeconds
value specified in theResourceConfig
to update.
RemoteDebugConfig
— (map
)Configuration for remote debugging while the training job is running. You can update the remote debugging configuration when the
SecondaryStatus
of the job isDownloading
orTraining
.To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.EnableRemoteDebug
— (Boolean
)If set to True, enables remote debugging.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrainingJobArn
— (String
)The Amazon Resource Name (ARN) of the training job.
-
(AWS.Response)
—
Returns:
updateTrial(params = {}, callback) ⇒ AWS.Request
Updates the display name of a trial.
Service Reference:
Examples:
Calling the updateTrial operation
var params = { TrialName: 'STRING_VALUE', /* required */ DisplayName: 'STRING_VALUE' }; sagemaker.updateTrial(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialName
— (String
)The name of the trial to update.
DisplayName
— (String
)The name of the trial as displayed. The name doesn't need to be unique. If
DisplayName
isn't specified,TrialName
is displayed.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialArn
— (String
)The Amazon Resource Name (ARN) of the trial.
-
(AWS.Response)
—
Returns:
updateTrialComponent(params = {}, callback) ⇒ AWS.Request
Updates one or more properties of a trial component.
Service Reference:
Examples:
Calling the updateTrialComponent operation
var params = { TrialComponentName: 'STRING_VALUE', /* required */ DisplayName: 'STRING_VALUE', EndTime: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, InputArtifacts: { '<TrialComponentKey128>': { Value: 'STRING_VALUE', /* required */ MediaType: 'STRING_VALUE' }, /* '<TrialComponentKey128>': ... */ }, InputArtifactsToRemove: [ 'STRING_VALUE', /* more items */ ], OutputArtifacts: { '<TrialComponentKey128>': { Value: 'STRING_VALUE', /* required */ MediaType: 'STRING_VALUE' }, /* '<TrialComponentKey128>': ... */ }, OutputArtifactsToRemove: [ 'STRING_VALUE', /* more items */ ], Parameters: { '<TrialComponentKey320>': { NumberValue: 'NUMBER_VALUE', StringValue: 'STRING_VALUE' }, /* '<TrialComponentKey320>': ... */ }, ParametersToRemove: [ 'STRING_VALUE', /* more items */ ], StartTime: new Date || 'Wed Dec 31 1969 16:00:00 GMT-0800 (PST)' || 123456789, Status: { Message: 'STRING_VALUE', PrimaryStatus: InProgress | Completed | Failed | Stopping | Stopped } }; sagemaker.updateTrialComponent(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
TrialComponentName
— (String
)The name of the component to update.
DisplayName
— (String
)The name of the component as displayed. The name doesn't need to be unique. If
DisplayName
isn't specified,TrialComponentName
is displayed.Status
— (map
)The new status of the component.
PrimaryStatus
— (String
)The status of the trial component.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
Message
— (String
)If the component failed, a message describing why.
StartTime
— (Date
)When the component started.
EndTime
— (Date
)When the component ended.
Parameters
— (map<map>
)Replaces all of the component's hyperparameters with the specified hyperparameters or add new hyperparameters. Existing hyperparameters are replaced if the trial component is updated with an identical hyperparameter key.
StringValue
— (String
)The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the
NumberValue
parameter.NumberValue
— (Float
)The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the
StringValue
parameter.
ParametersToRemove
— (Array<String>
)The hyperparameters to remove from the component.
InputArtifacts
— (map<map>
)Replaces all of the component's input artifacts with the specified artifacts or adds new input artifacts. Existing input artifacts are replaced if the trial component is updated with an identical input artifact key.
MediaType
— (String
)The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value
— required — (String
)The location of the artifact.
InputArtifactsToRemove
— (Array<String>
)The input artifacts to remove from the component.
OutputArtifacts
— (map<map>
)Replaces all of the component's output artifacts with the specified artifacts or adds new output artifacts. Existing output artifacts are replaced if the trial component is updated with an identical output artifact key.
MediaType
— (String
)The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value
— required — (String
)The location of the artifact.
OutputArtifactsToRemove
— (Array<String>
)The output artifacts to remove from the component.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrialComponentArn
— (String
)The Amazon Resource Name (ARN) of the trial component.
-
(AWS.Response)
—
Returns:
updateUserProfile(params = {}, callback) ⇒ AWS.Request
Updates a user profile.
Service Reference:
Examples:
Calling the updateUserProfile operation
var params = { DomainId: 'STRING_VALUE', /* required */ UserProfileName: 'STRING_VALUE', /* required */ UserSettings: { AutoMountHomeEFS: Enabled | Disabled | DefaultAsDomain, CanvasAppSettings: { DirectDeploySettings: { Status: ENABLED | DISABLED }, EmrServerlessSettings: { ExecutionRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, GenerativeAiSettings: { AmazonBedrockRoleArn: 'STRING_VALUE' }, IdentityProviderOAuthSettings: [ { DataSourceName: SalesforceGenie | Snowflake, SecretArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, /* more items */ ], KendraSettings: { Status: ENABLED | DISABLED }, ModelRegisterSettings: { CrossAccountModelRegisterRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, TimeSeriesForecastingSettings: { AmazonForecastRoleArn: 'STRING_VALUE', Status: ENABLED | DISABLED }, WorkspaceSettings: { S3ArtifactPath: 'STRING_VALUE', S3KmsKeyId: 'STRING_VALUE' } }, CodeEditorAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE', LifecycleManagement: ENABLED | DISABLED, MaxIdleTimeoutInMinutes: 'NUMBER_VALUE', MinIdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, CustomFileSystemConfigs: [ { EFSFileSystemConfig: { FileSystemId: 'STRING_VALUE', /* required */ FileSystemPath: 'STRING_VALUE' } }, /* more items */ ], CustomPosixUserConfig: { Gid: 'NUMBER_VALUE', /* required */ Uid: 'NUMBER_VALUE' /* required */ }, DefaultLandingUri: 'STRING_VALUE', ExecutionRole: 'STRING_VALUE', JupyterLabAppSettings: { AppLifecycleManagement: { IdleSettings: { IdleTimeoutInMinutes: 'NUMBER_VALUE', LifecycleManagement: ENABLED | DISABLED, MaxIdleTimeoutInMinutes: 'NUMBER_VALUE', MinIdleTimeoutInMinutes: 'NUMBER_VALUE' } }, CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, EmrSettings: { AssumableRoleArns: [ 'STRING_VALUE', /* more items */ ], ExecutionRoleArns: [ 'STRING_VALUE', /* more items */ ] }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, JupyterServerAppSettings: { CodeRepositories: [ { RepositoryUrl: 'STRING_VALUE' /* required */ }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, KernelGatewayAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' }, LifecycleConfigArns: [ 'STRING_VALUE', /* more items */ ] }, RSessionAppSettings: { CustomImages: [ { AppImageConfigName: 'STRING_VALUE', /* required */ ImageName: 'STRING_VALUE', /* required */ ImageVersionNumber: 'NUMBER_VALUE' }, /* more items */ ], DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } }, RStudioServerProAppSettings: { AccessStatus: ENABLED | DISABLED, UserGroup: R_STUDIO_ADMIN | R_STUDIO_USER }, SecurityGroups: [ 'STRING_VALUE', /* more items */ ], SharingSettings: { NotebookOutputOption: Allowed | Disabled, S3KmsKeyId: 'STRING_VALUE', S3OutputPath: 'STRING_VALUE' }, SpaceStorageSettings: { DefaultEbsStorageSettings: { DefaultEbsVolumeSizeInGb: 'NUMBER_VALUE', /* required */ MaximumEbsVolumeSizeInGb: 'NUMBER_VALUE' /* required */ } }, StudioWebPortal: ENABLED | DISABLED, StudioWebPortalSettings: { HiddenAppTypes: [ JupyterServer | KernelGateway | DetailedProfiler | TensorBoard | CodeEditor | JupyterLab | RStudioServerPro | RSessionGateway | Canvas, /* more items */ ], HiddenMlTools: [ DataWrangler | FeatureStore | EmrClusters | AutoMl | Experiments | Training | ModelEvaluation | Pipelines | Models | JumpStart | InferenceRecommender | Endpoints | Projects | InferenceOptimization, /* more items */ ] }, TensorBoardAppSettings: { DefaultResourceSpec: { InstanceType: system | ml.t3.micro | ml.t3.small | ml.t3.medium | ml.t3.large | ml.t3.xlarge | ml.t3.2xlarge | ml.m5.large | ml.m5.xlarge | ml.m5.2xlarge | ml.m5.4xlarge | ml.m5.8xlarge | ml.m5.12xlarge | ml.m5.16xlarge | ml.m5.24xlarge | ml.m5d.large | ml.m5d.xlarge | ml.m5d.2xlarge | ml.m5d.4xlarge | ml.m5d.8xlarge | ml.m5d.12xlarge | ml.m5d.16xlarge | ml.m5d.24xlarge | ml.c5.large | ml.c5.xlarge | ml.c5.2xlarge | ml.c5.4xlarge | ml.c5.9xlarge | ml.c5.12xlarge | ml.c5.18xlarge | ml.c5.24xlarge | ml.p3.2xlarge | ml.p3.8xlarge | ml.p3.16xlarge | ml.p3dn.24xlarge | ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge | ml.g4dn.8xlarge | ml.g4dn.12xlarge | ml.g4dn.16xlarge | ml.r5.large | ml.r5.xlarge | ml.r5.2xlarge | ml.r5.4xlarge | ml.r5.8xlarge | ml.r5.12xlarge | ml.r5.16xlarge | ml.r5.24xlarge | ml.g5.xlarge | ml.g5.2xlarge | ml.g5.4xlarge | ml.g5.8xlarge | ml.g5.16xlarge | ml.g5.12xlarge | ml.g5.24xlarge | ml.g5.48xlarge | ml.g6.xlarge | ml.g6.2xlarge | ml.g6.4xlarge | ml.g6.8xlarge | ml.g6.12xlarge | ml.g6.16xlarge | ml.g6.24xlarge | ml.g6.48xlarge | ml.geospatial.interactive | ml.p4d.24xlarge | ml.p4de.24xlarge | ml.trn1.2xlarge | ml.trn1.32xlarge | ml.trn1n.32xlarge | ml.p5.48xlarge | ml.m6i.large | ml.m6i.xlarge | ml.m6i.2xlarge | ml.m6i.4xlarge | ml.m6i.8xlarge | ml.m6i.12xlarge | ml.m6i.16xlarge | ml.m6i.24xlarge | ml.m6i.32xlarge | ml.m7i.large | ml.m7i.xlarge | ml.m7i.2xlarge | ml.m7i.4xlarge | ml.m7i.8xlarge | ml.m7i.12xlarge | ml.m7i.16xlarge | ml.m7i.24xlarge | ml.m7i.48xlarge | ml.c6i.large | ml.c6i.xlarge | ml.c6i.2xlarge | ml.c6i.4xlarge | ml.c6i.8xlarge | ml.c6i.12xlarge | ml.c6i.16xlarge | ml.c6i.24xlarge | ml.c6i.32xlarge | ml.c7i.large | ml.c7i.xlarge | ml.c7i.2xlarge | ml.c7i.4xlarge | ml.c7i.8xlarge | ml.c7i.12xlarge | ml.c7i.16xlarge | ml.c7i.24xlarge | ml.c7i.48xlarge | ml.r6i.large | ml.r6i.xlarge | ml.r6i.2xlarge | ml.r6i.4xlarge | ml.r6i.8xlarge | ml.r6i.12xlarge | ml.r6i.16xlarge | ml.r6i.24xlarge | ml.r6i.32xlarge | ml.r7i.large | ml.r7i.xlarge | ml.r7i.2xlarge | ml.r7i.4xlarge | ml.r7i.8xlarge | ml.r7i.12xlarge | ml.r7i.16xlarge | ml.r7i.24xlarge | ml.r7i.48xlarge | ml.m6id.large | ml.m6id.xlarge | ml.m6id.2xlarge | ml.m6id.4xlarge | ml.m6id.8xlarge | ml.m6id.12xlarge | ml.m6id.16xlarge | ml.m6id.24xlarge | ml.m6id.32xlarge | ml.c6id.large | ml.c6id.xlarge | ml.c6id.2xlarge | ml.c6id.4xlarge | ml.c6id.8xlarge | ml.c6id.12xlarge | ml.c6id.16xlarge | ml.c6id.24xlarge | ml.c6id.32xlarge | ml.r6id.large | ml.r6id.xlarge | ml.r6id.2xlarge | ml.r6id.4xlarge | ml.r6id.8xlarge | ml.r6id.12xlarge | ml.r6id.16xlarge | ml.r6id.24xlarge | ml.r6id.32xlarge, LifecycleConfigArn: 'STRING_VALUE', SageMakerImageArn: 'STRING_VALUE', SageMakerImageVersionAlias: 'STRING_VALUE', SageMakerImageVersionArn: 'STRING_VALUE' } } } }; sagemaker.updateUserProfile(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
DomainId
— (String
)The domain ID.
UserProfileName
— (String
)The user profile name.
UserSettings
— (map
)A collection of settings.
ExecutionRole
— (String
)The execution role for the user.
SecurityGroups
— (Array<String>
)The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the
CreateDomain.AppNetworkAccessType
parameter is set toPublicInternetOnly
.Required when the
CreateDomain.AppNetworkAccessType
parameter is set toVpcOnly
, unless specified as part of theDefaultUserSettings
for the domain.Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SharingSettings
— (map
)Specifies options for sharing Amazon SageMaker Studio notebooks.
NotebookOutputOption
— (String
)Whether to include the notebook cell output when sharing the notebook. The default is
Possible values include:Disabled
."Allowed"
"Disabled"
S3OutputPath
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon S3 bucket used to store the shared notebook snapshots.S3KmsKeyId
— (String
)When
NotebookOutputOption
isAllowed
, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings
— (map
)The Jupyter server's app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the
LifecycleConfigArns
parameter, then this parameter is also required.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the
DefaultResourceSpec
parameter is also required.Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
KernelGatewayAppSettings
— (map
)The kernel gateway app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note: The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a KernelGateway app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note: To remove a Lifecycle Config, you must setLifecycleConfigArns
to an empty list.
TensorBoardAppSettings
— (map
)The TensorBoard app settings.
DefaultResourceSpec
— (map
)The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
RStudioServerProAppSettings
— (map
)A collection of settings that configure user interaction with the
RStudioServerPro
app.AccessStatus
— (String
)Indicates whether the current user has access to the
Possible values include:RStudioServerPro
app."ENABLED"
"DISABLED"
UserGroup
— (String
)The level of permissions that the user has within the
Possible values include:RStudioServerPro
app. This value defaults toUser
. TheAdmin
value allows the user access to the RStudio Administrative Dashboard."R_STUDIO_ADMIN"
"R_STUDIO_USER"
RSessionAppSettings
— (map
)A collection of settings that configure the
RSessionGateway
app.DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a RSession app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
CanvasAppSettings
— (map
)The Canvas app settings.
TimeSeriesForecastingSettings
— (map
)Time series forecast settings for the SageMaker Canvas application.
Status
— (String
)Describes whether time series forecasting is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
AmazonForecastRoleArn
— (String
)The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the
UserProfile
that launches the Canvas application. If an execution role is not specified in theUserProfile
, Canvas uses the execution role specified in the Domain that owns theUserProfile
. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached andforecast.amazonaws.com
added in the trust relationship as a service principal.
ModelRegisterSettings
— (map
)The model registry settings for the SageMaker Canvas application.
Status
— (String
)Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CrossAccountModelRegisterRoleArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings
— (map
)The workspace settings for the SageMaker Canvas application.
S3ArtifactPath
— (String
)The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId
— (String
)The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings
— (Array<map>
)The settings for connecting to an external data source with OAuth.
DataSourceName
— (String
)The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Possible values include:"SalesforceGenie"
"Snowflake"
Status
— (String
)Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
SecretArn
— (String
)The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings
— (map
)The model deployment settings for the SageMaker Canvas application.
Status
— (String
)Describes whether model deployment permissions are enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
KendraSettings
— (map
)The settings for document querying.
Status
— (String
)Describes whether the document querying feature is enabled or disabled in the Canvas application.
Possible values include:"ENABLED"
"DISABLED"
GenerativeAiSettings
— (map
)The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn
— (String
)The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes
bedrock.amazonaws.com
as a service principal.
EmrServerlessSettings
— (map
)The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn
— (String
)The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status
— (String
)Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
Possible values include:"ENABLED"
"DISABLED"
CodeEditorAppSettings
— (map
)The Code Editor application settings.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a Code Editor app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
AppLifecycleManagement
— (map
)Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
JupyterLabAppSettings
— (map
)The settings for the JupyterLab application.
DefaultResourceSpec
— (map
)Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn
— (String
)The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn
— (String
)The ARN of the image version created on the instance.
SageMakerImageVersionAlias
— (String
)The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType
— (String
)The instance type that the image version runs on.
Note: JupyterServer apps only support thePossible values include:system
value. For KernelGateway apps, thesystem
value is translated toml.t3.medium
. KernelGateway apps also support all other values for available instance types."system"
"ml.t3.micro"
"ml.t3.small"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.8xlarge"
"ml.m5.12xlarge"
"ml.m5.16xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.12xlarge"
"ml.c5.18xlarge"
"ml.c5.24xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.geospatial.interactive"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
LifecycleConfigArn
— (String
)The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages
— (Array<map>
)A list of custom SageMaker images that are configured to run as a JupyterLab app.
ImageName
— required — (String
)The name of the CustomImage. Must be unique to your account.
ImageVersionNumber
— (Integer
)The version number of the CustomImage.
AppImageConfigName
— required — (String
)The name of the AppImageConfig.
LifecycleConfigArns
— (Array<String>
)The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set
LifecycleConfigArns
to an empty list.CodeRepositories
— (Array<map>
)A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
RepositoryUrl
— required — (String
)The URL of the Git repository.
AppLifecycleManagement
— (map
)Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings
— (map
)Settings related to idle shutdown of Studio applications.
LifecycleManagement
— (String
)Indicates whether idle shutdown is activated for the application type.
Possible values include:"ENABLED"
"DISABLED"
IdleTimeoutInMinutes
— (Integer
)The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes
— (Integer
)The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes
— (Integer
)The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings
— (map
)The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
ExecutionRoleArns
— (Array<String>
)An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
SpaceStorageSettings
— (map
)The storage settings for a space.
DefaultEbsStorageSettings
— (map
)The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb
— required — (Integer
)The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb
— required — (Integer
)The maximum size of the EBS storage volume for a space.
DefaultLandingUri
— (String
)The default experience that the user is directed to when accessing the domain. The supported values are:
-
studio::
: Indicates that Studio is the default experience. This value can only be passed ifStudioWebPortal
is set toENABLED
. -
app:JupyterServer:
: Indicates that Studio Classic is the default experience.
-
StudioWebPortal
— (String
)Whether the user can access Studio. If this value is set to
Possible values include:DISABLED
, the user cannot access Studio, even if that is the default experience for the domain."ENABLED"
"DISABLED"
CustomPosixUserConfig
— (map
)Details about the POSIX identity that is used for file system operations.
Uid
— required — (Integer
)The POSIX user ID.
Gid
— required — (Integer
)The POSIX group ID.
CustomFileSystemConfigs
— (Array<map>
)The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystemConfig
— (map
)The settings for a custom Amazon EFS file system.
FileSystemId
— required — (String
)The ID of your Amazon EFS file system.
FileSystemPath
— (String
)The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
StudioWebPortalSettings
— (map
)Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools
— (Array<String>
)The machine learning tools that are hidden from the Studio left navigation pane.
HiddenAppTypes
— (Array<String>
)The Applications supported in Studio that are hidden from the Studio left navigation pane.
AutoMountHomeEFS
— (String
)Indicates whether auto-mounting of an EFS volume is supported for the user profile. The
Possible values include:DefaultAsDomain
value is only supported for user profiles. Do not use theDefaultAsDomain
value when setting this parameter for a domain."Enabled"
"Disabled"
"DefaultAsDomain"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:UserProfileArn
— (String
)The user profile Amazon Resource Name (ARN).
-
(AWS.Response)
—
Returns:
updateWorkforce(params = {}, callback) ⇒ AWS.Request
Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.
The worker portal is now supported in VPC and public internet.
Use
SourceIpConfig
to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get aNot Found
error message on the worker portal.To restrict access to all the workers in public internet, add the
SourceIpConfig
CIDR value as "10.0.0.0/16".Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.
Use
OidcConfig
to update the configuration of a workforce created using your own OIDC IdP.You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation.
After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation.
This operation only applies to private workforces.
Service Reference:
Examples:
Calling the updateWorkforce operation
var params = { WorkforceName: 'STRING_VALUE', /* required */ OidcConfig: { AuthorizationEndpoint: 'STRING_VALUE', /* required */ ClientId: 'STRING_VALUE', /* required */ ClientSecret: 'STRING_VALUE', /* required */ Issuer: 'STRING_VALUE', /* required */ JwksUri: 'STRING_VALUE', /* required */ LogoutEndpoint: 'STRING_VALUE', /* required */ TokenEndpoint: 'STRING_VALUE', /* required */ UserInfoEndpoint: 'STRING_VALUE', /* required */ AuthenticationRequestExtraParams: { '<AuthenticationRequestExtraParamsKey>': 'STRING_VALUE', /* '<AuthenticationRequestExtraParamsKey>': ... */ }, Scope: 'STRING_VALUE' }, SourceIpConfig: { Cidrs: [ /* required */ 'STRING_VALUE', /* more items */ ] }, WorkforceVpcConfig: { SecurityGroupIds: [ 'STRING_VALUE', /* more items */ ], Subnets: [ 'STRING_VALUE', /* more items */ ], VpcId: 'STRING_VALUE' } }; sagemaker.updateWorkforce(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
WorkforceName
— (String
)The name of the private workforce that you want to update. You can find your workforce name by using the ListWorkforces operation.
SourceIpConfig
— (map
)A list of one to ten worker IP address ranges (CIDRs) that can be used to access tasks assigned to this workforce.
Maximum: Ten CIDR values
Cidrs
— required — (Array<String>
)A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note: The following Length Constraints apply to individual CIDR values in the CIDR value list.
OidcConfig
— (map
)Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.
ClientId
— required — (String
)The OIDC IdP client ID used to configure your private workforce.
ClientSecret
— required — (String
)The OIDC IdP client secret used to configure your private workforce.
Issuer
— required — (String
)The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint
— required — (String
)The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint
— required — (String
)The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint
— required — (String
)The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint
— required — (String
)The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri
— required — (String
)The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
Scope
— (String
)An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.
AuthenticationRequestExtraParams
— (map<String>
)A string to string map of identifiers specific to the custom identity provider (IdP) being used.
WorkforceVpcConfig
— (map
)Use this parameter to update your VPC configuration for a workforce.
VpcId
— (String
)The ID of the VPC that the workforce uses for communication.
SecurityGroupIds
— (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. The security groups must be for the same VPC as specified in the subnet.Subnets
— (Array<String>
)The ID of the subnets in the VPC that you want to connect.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Workforce
— (map
)A single private workforce. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
WorkforceName
— required — (String
)The name of the private workforce.
WorkforceArn
— required — (String
)The Amazon Resource Name (ARN) of the private workforce.
LastUpdatedDate
— (Date
)The most recent date that UpdateWorkforce was used to successfully add one or more IP address ranges (CIDRs) to a private workforce's allow list.
SourceIpConfig
— (map
)A list of one to ten IP address ranges (CIDRs) to be added to the workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
Cidrs
— required — (Array<String>
)A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note: The following Length Constraints apply to individual CIDR values in the CIDR value list.
SubDomain
— (String
)The subdomain for your OIDC Identity Provider.
CognitoConfig
— (map
)The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
UserPool
— required — (String
)A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId
— required — (String
)The client ID for your Amazon Cognito user pool.
OidcConfig
— (map
)The configuration of an OIDC Identity Provider (IdP) private workforce.
ClientId
— (String
)The OIDC IdP client ID used to configure your private workforce.
Issuer
— (String
)The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint
— (String
)The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint
— (String
)The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint
— (String
)The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint
— (String
)The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri
— (String
)The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
Scope
— (String
)An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.
AuthenticationRequestExtraParams
— (map<String>
)A string to string map of identifiers specific to the custom identity provider (IdP) being used.
CreateDate
— (Date
)The date that the workforce is created.
WorkforceVpcConfig
— (map
)The configuration of a VPC workforce.
VpcId
— required — (String
)The ID of the VPC that the workforce uses for communication.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
Subnets
— required — (Array<String>
)The ID of the subnets in the VPC that you want to connect.
VpcEndpointId
— (String
)The IDs for the VPC service endpoints of your VPC workforce when it is created and updated.
Status
— (String
)The status of your workforce.
Possible values include:"Initializing"
"Updating"
"Deleting"
"Failed"
"Active"
FailureReason
— (String
)The reason your workforce failed.
-
(AWS.Response)
—
Returns:
updateWorkteam(params = {}, callback) ⇒ AWS.Request
Updates an existing work team with new member definitions or description.
Service Reference:
Examples:
Calling the updateWorkteam operation
var params = { WorkteamName: 'STRING_VALUE', /* required */ Description: 'STRING_VALUE', MemberDefinitions: [ { CognitoMemberDefinition: { ClientId: 'STRING_VALUE', /* required */ UserGroup: 'STRING_VALUE', /* required */ UserPool: 'STRING_VALUE' /* required */ }, OidcMemberDefinition: { Groups: [ 'STRING_VALUE', /* more items */ ] } }, /* more items */ ], NotificationConfiguration: { NotificationTopicArn: 'STRING_VALUE' }, WorkerAccessConfiguration: { S3Presign: { IamPolicyConstraints: { SourceIp: Enabled | Disabled, VpcSourceIp: Enabled | Disabled } } } }; sagemaker.updateWorkteam(params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
(defaults to: {})
—
WorkteamName
— (String
)The name of the work team to update.
MemberDefinitions
— (Array<map>
)A list of
MemberDefinition
objects that contains objects that identify the workers that make up the work team.Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use
CognitoMemberDefinition
. For workforces created using your own OIDC identity provider (IdP) useOidcMemberDefinition
. You should not provide input for both of these parameters in a single request.For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the
CognitoMemberDefinition
objects that make up the member definition must have the sameClientId
andUserPool
values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools.For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in
OidcMemberDefinition
by listing those groups inGroups
. Be aware that user groups that are already in the work team must also be listed inGroups
when you make this request to remain on the work team. If you do not include these user groups, they will no longer be associated with the work team you update.CognitoMemberDefinition
— (map
)The Amazon Cognito user group that is part of the work team.
UserPool
— required — (String
)An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup
— required — (String
)An identifier for a user group.
ClientId
— required — (String
)An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition
— (map
)A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of
Groups
, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.Groups
— (Array<String>
)A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
Description
— (String
)An updated description for the work team.
NotificationConfiguration
— (map
)Configures SNS topic notifications for available or expiring work items
NotificationTopicArn
— (String
)The ARN for the Amazon SNS topic to which notifications should be published.
WorkerAccessConfiguration
— (map
)Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.
S3Presign
— (map
)Defines any Amazon S3 resource constraints.
IamPolicyConstraints
— (map
)Use this parameter to specify the allowed request source. Possible sources are either
SourceIp
orVpcSourceIp
.SourceIp
— (String
)When
Possible values include:SourceIp
isEnabled
the worker's IP address when a task is rendered in the worker portal is added to the IAM policy as aCondition
used to generate the Amazon S3 presigned URL. This IP address is checked by Amazon S3 and must match in order for the Amazon S3 resource to be rendered in the worker portal."Enabled"
"Disabled"
VpcSourceIp
— (String
)When
Possible values include:VpcSourceIp
isEnabled
the worker's IP address when a task is rendered in private worker portal inside the VPC is added to the IAM policy as aCondition
used to generate the Amazon S3 presigned URL. To render the task successfully Amazon S3 checks that the presigned URL is being accessed over an Amazon S3 VPC Endpoint, and that the worker's IP address matches the IP address in the IAM policy. To learn more about configuring private worker portal, see Use Amazon VPC mode from a private worker portal."Enabled"
"Disabled"
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:Workteam
— (map
)A
Workteam
object that describes the updated work team.WorkteamName
— required — (String
)The name of the work team.
MemberDefinitions
— required — (Array<map>
)A list of
MemberDefinition
objects that contains objects that identify the workers that make up the work team.Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use
CognitoMemberDefinition
. For workforces created using your own OIDC identity provider (IdP) useOidcMemberDefinition
.CognitoMemberDefinition
— (map
)The Amazon Cognito user group that is part of the work team.
UserPool
— required — (String
)An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup
— required — (String
)An identifier for a user group.
ClientId
— required — (String
)An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition
— (map
)A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of
Groups
, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.Groups
— (Array<String>
)A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
WorkteamArn
— required — (String
)The Amazon Resource Name (ARN) that identifies the work team.
WorkforceArn
— (String
)The Amazon Resource Name (ARN) of the workforce.
ProductListingIds
— (Array<String>
)The Amazon Marketplace identifier for a vendor's work team.
Description
— required — (String
)A description of the work team.
SubDomain
— (String
)The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
CreateDate
— (Date
)The date and time that the work team was created (timestamp).
LastUpdatedDate
— (Date
)The date and time that the work team was last updated (timestamp).
NotificationConfiguration
— (map
)Configures SNS notifications of available or expiring work items for work teams.
NotificationTopicArn
— (String
)The ARN for the Amazon SNS topic to which notifications should be published.
WorkerAccessConfiguration
— (map
)Describes any access constraints that have been defined for Amazon S3 resources.
S3Presign
— (map
)Defines any Amazon S3 resource constraints.
IamPolicyConstraints
— (map
)Use this parameter to specify the allowed request source. Possible sources are either
SourceIp
orVpcSourceIp
.SourceIp
— (String
)When
Possible values include:SourceIp
isEnabled
the worker's IP address when a task is rendered in the worker portal is added to the IAM policy as aCondition
used to generate the Amazon S3 presigned URL. This IP address is checked by Amazon S3 and must match in order for the Amazon S3 resource to be rendered in the worker portal."Enabled"
"Disabled"
VpcSourceIp
— (String
)When
Possible values include:VpcSourceIp
isEnabled
the worker's IP address when a task is rendered in private worker portal inside the VPC is added to the IAM policy as aCondition
used to generate the Amazon S3 presigned URL. To render the task successfully Amazon S3 checks that the presigned URL is being accessed over an Amazon S3 VPC Endpoint, and that the worker's IP address matches the IP address in the IAM policy. To learn more about configuring private worker portal, see Use Amazon VPC mode from a private worker portal."Enabled"
"Disabled"
-
(AWS.Response)
—
Returns:
waitFor(state, params = {}, callback) ⇒ AWS.Request
Waits for a given SageMaker resource. The final callback or 'complete' event will be fired only when the resource is either in its final state or the waiter has timed out and stopped polling for the final state.
Examples:
Waiting for the notebookInstanceInService state
var params = { NotebookInstanceName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('notebookInstanceInService', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
state
(String)
—
the resource state to wait for. Available states for this service are listed in "Waiter Resource States" below.
-
params
(map)
(defaults to: {})
—
a list of parameters for the given state. See each waiter resource state for required parameters.
Callback (callback):
-
function(err, data) { ... }
Callback containing error and data information. See the respective resource state for the expected error or data information.
If the waiter times out its requests, it will return a
ResourceNotReady
error.
Returns:
Waiter Resource States:
Waiter Resource Details
sagemaker.waitFor('notebookInstanceInService', params = {}, [callback]) ⇒ AWS.Request
Waits for the
notebookInstanceInService
state by periodically calling the underlying SageMaker.describeNotebookInstance() operation every 30 seconds (at most 60 times).Examples:
Waiting for the notebookInstanceInService state
var params = { NotebookInstanceName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('notebookInstanceInService', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
NotebookInstanceName
— (String
)The name of the notebook instance that you want information about.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NotebookInstanceArn
— (String
)The Amazon Resource Name (ARN) of the notebook instance.
NotebookInstanceName
— (String
)The name of the SageMaker notebook instance.
NotebookInstanceStatus
— (String
)The status of the notebook instance.
Possible values include:"Pending"
"InService"
"Stopping"
"Stopped"
"Failed"
"Deleting"
"Updating"
FailureReason
— (String
)If status is
Failed
, the reason it failed.Url
— (String
)The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.
InstanceType
— (String
)The type of ML compute instance running on the notebook instance.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
SubnetId
— (String
)The ID of the VPC subnet.
SecurityGroups
— (Array<String>
)The IDs of the VPC security groups.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role associated with the instance.
KmsKeyId
— (String
)The Amazon Web Services KMS key ID SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
NetworkInterfaceId
— (String
)The network interface IDs that SageMaker created at the time of creating the instance.
LastModifiedTime
— (Date
)A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.
CreationTime
— (Date
)A timestamp. Use this parameter to return the time when the notebook instance was created
NotebookInstanceLifecycleConfigName
— (String
)Returns the name of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance
DirectInternetAccess
— (String
)Describes whether SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to SageMaker training and endpoint services.
For more information, see Notebook Instances Are Internet-Enabled by Default.
Possible values include:"Enabled"
"Disabled"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the notebook instance.
AcceleratorTypes
— (Array<String>
)A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
DefaultCodeRepository
— (String
)The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
AdditionalCodeRepositories
— (Array<String>
)An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
RootAccess
— (String
)Whether root access is enabled or disabled for users of the notebook instance.
Note: Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.Possible values include:"Enabled"
"Disabled"
PlatformIdentifier
— (String
)The platform identifier of the notebook instance runtime environment.
InstanceMetadataServiceConfiguration
— (map
)Information on the IMDS configuration of the notebook instance
MinimumInstanceMetadataServiceVersion
— required — (String
)Indicates the minimum IMDS version that the notebook instance supports. When passed as part of
CreateNotebookInstance
, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part ofUpdateNotebookInstance
, there is no default.
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('notebookInstanceStopped', params = {}, [callback]) ⇒ AWS.Request
Waits for the
notebookInstanceStopped
state by periodically calling the underlying SageMaker.describeNotebookInstance() operation every 30 seconds (at most 60 times).Examples:
Waiting for the notebookInstanceStopped state
var params = { NotebookInstanceName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('notebookInstanceStopped', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
NotebookInstanceName
— (String
)The name of the notebook instance that you want information about.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NotebookInstanceArn
— (String
)The Amazon Resource Name (ARN) of the notebook instance.
NotebookInstanceName
— (String
)The name of the SageMaker notebook instance.
NotebookInstanceStatus
— (String
)The status of the notebook instance.
Possible values include:"Pending"
"InService"
"Stopping"
"Stopped"
"Failed"
"Deleting"
"Updating"
FailureReason
— (String
)If status is
Failed
, the reason it failed.Url
— (String
)The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.
InstanceType
— (String
)The type of ML compute instance running on the notebook instance.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
SubnetId
— (String
)The ID of the VPC subnet.
SecurityGroups
— (Array<String>
)The IDs of the VPC security groups.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role associated with the instance.
KmsKeyId
— (String
)The Amazon Web Services KMS key ID SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
NetworkInterfaceId
— (String
)The network interface IDs that SageMaker created at the time of creating the instance.
LastModifiedTime
— (Date
)A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.
CreationTime
— (Date
)A timestamp. Use this parameter to return the time when the notebook instance was created
NotebookInstanceLifecycleConfigName
— (String
)Returns the name of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance
DirectInternetAccess
— (String
)Describes whether SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to SageMaker training and endpoint services.
For more information, see Notebook Instances Are Internet-Enabled by Default.
Possible values include:"Enabled"
"Disabled"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the notebook instance.
AcceleratorTypes
— (Array<String>
)A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
DefaultCodeRepository
— (String
)The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
AdditionalCodeRepositories
— (Array<String>
)An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
RootAccess
— (String
)Whether root access is enabled or disabled for users of the notebook instance.
Note: Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.Possible values include:"Enabled"
"Disabled"
PlatformIdentifier
— (String
)The platform identifier of the notebook instance runtime environment.
InstanceMetadataServiceConfiguration
— (map
)Information on the IMDS configuration of the notebook instance
MinimumInstanceMetadataServiceVersion
— required — (String
)Indicates the minimum IMDS version that the notebook instance supports. When passed as part of
CreateNotebookInstance
, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part ofUpdateNotebookInstance
, there is no default.
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('notebookInstanceDeleted', params = {}, [callback]) ⇒ AWS.Request
Waits for the
notebookInstanceDeleted
state by periodically calling the underlying SageMaker.describeNotebookInstance() operation every 30 seconds (at most 60 times).Examples:
Waiting for the notebookInstanceDeleted state
var params = { NotebookInstanceName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('notebookInstanceDeleted', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
NotebookInstanceName
— (String
)The name of the notebook instance that you want information about.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:NotebookInstanceArn
— (String
)The Amazon Resource Name (ARN) of the notebook instance.
NotebookInstanceName
— (String
)The name of the SageMaker notebook instance.
NotebookInstanceStatus
— (String
)The status of the notebook instance.
Possible values include:"Pending"
"InService"
"Stopping"
"Stopped"
"Failed"
"Deleting"
"Updating"
FailureReason
— (String
)If status is
Failed
, the reason it failed.Url
— (String
)The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.
InstanceType
— (String
)The type of ML compute instance running on the notebook instance.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.8xlarge"
"ml.m5d.12xlarge"
"ml.m5d.16xlarge"
"ml.m5d.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.m6id.large"
"ml.m6id.xlarge"
"ml.m6id.2xlarge"
"ml.m6id.4xlarge"
"ml.m6id.8xlarge"
"ml.m6id.12xlarge"
"ml.m6id.16xlarge"
"ml.m6id.24xlarge"
"ml.m6id.32xlarge"
"ml.c6id.large"
"ml.c6id.xlarge"
"ml.c6id.2xlarge"
"ml.c6id.4xlarge"
"ml.c6id.8xlarge"
"ml.c6id.12xlarge"
"ml.c6id.16xlarge"
"ml.c6id.24xlarge"
"ml.c6id.32xlarge"
"ml.r6id.large"
"ml.r6id.xlarge"
"ml.r6id.2xlarge"
"ml.r6id.4xlarge"
"ml.r6id.8xlarge"
"ml.r6id.12xlarge"
"ml.r6id.16xlarge"
"ml.r6id.24xlarge"
"ml.r6id.32xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
SubnetId
— (String
)The ID of the VPC subnet.
SecurityGroups
— (Array<String>
)The IDs of the VPC security groups.
RoleArn
— (String
)The Amazon Resource Name (ARN) of the IAM role associated with the instance.
KmsKeyId
— (String
)The Amazon Web Services KMS key ID SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
NetworkInterfaceId
— (String
)The network interface IDs that SageMaker created at the time of creating the instance.
LastModifiedTime
— (Date
)A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.
CreationTime
— (Date
)A timestamp. Use this parameter to return the time when the notebook instance was created
NotebookInstanceLifecycleConfigName
— (String
)Returns the name of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance
DirectInternetAccess
— (String
)Describes whether SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to SageMaker training and endpoint services.
For more information, see Notebook Instances Are Internet-Enabled by Default.
Possible values include:"Enabled"
"Disabled"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the notebook instance.
AcceleratorTypes
— (Array<String>
)A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
DefaultCodeRepository
— (String
)The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
AdditionalCodeRepositories
— (Array<String>
)An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
RootAccess
— (String
)Whether root access is enabled or disabled for users of the notebook instance.
Note: Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.Possible values include:"Enabled"
"Disabled"
PlatformIdentifier
— (String
)The platform identifier of the notebook instance runtime environment.
InstanceMetadataServiceConfiguration
— (map
)Information on the IMDS configuration of the notebook instance
MinimumInstanceMetadataServiceVersion
— required — (String
)Indicates the minimum IMDS version that the notebook instance supports. When passed as part of
CreateNotebookInstance
, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part ofUpdateNotebookInstance
, there is no default.
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('trainingJobCompletedOrStopped', params = {}, [callback]) ⇒ AWS.Request
Waits for the
trainingJobCompletedOrStopped
state by periodically calling the underlying SageMaker.describeTrainingJob() operation every 120 seconds (at most 180 times).Examples:
Waiting for the trainingJobCompletedOrStopped state
var params = { TrainingJobName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('trainingJobCompletedOrStopped', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
TrainingJobName
— (String
)The name of the training job.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TrainingJobName
— (String
)Name of the model training job.
TrainingJobArn
— (String
)The Amazon Resource Name (ARN) of the training job.
TuningJobArn
— (String
)The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the transform or training job.
AutoMLJobArn
— (String
)The Amazon Resource Name (ARN) of an AutoML job.
ModelArtifacts
— (map
)Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts
— required — (String
)The path of the S3 object that contains the model artifacts. For example,
s3://bucket-name/keynameprefix/model.tar.gz
.
TrainingJobStatus
— (String
)The status of the training job.
SageMaker provides the following training job statuses:
-
InProgress
- The training is in progress. -
Completed
- The training job has completed. -
Failed
- The training job has failed. To see the reason for the failure, see theFailureReason
field in the response to aDescribeTrainingJobResponse
call. -
Stopping
- The training job is stopping. -
Stopped
- The training job has stopped.
For more detailed information, see
Possible values include:SecondaryStatus
."InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
-
SecondaryStatus
— (String
)Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see
StatusMessage
under SecondaryStatusTransition.SageMaker provides primary statuses and secondary statuses that apply to each of them:
- InProgress
-
-
Starting
- Starting the training job. -
Downloading
- An optional stage for algorithms that supportFile
training input mode. It indicates that data is being downloaded to the ML storage volumes. -
Training
- Training is in progress. -
Interrupted
- The job stopped because the managed spot training instances were interrupted. -
Uploading
- Training is complete and the model artifacts are being uploaded to the S3 location.
-
- Completed
-
-
Completed
- The training job has completed.
-
- Failed
-
-
Failed
- The training job has failed. The reason for the failure is returned in theFailureReason
field ofDescribeTrainingJobResponse
.
-
- Stopped
-
-
MaxRuntimeExceeded
- The job stopped because it exceeded the maximum allowed runtime. -
MaxWaitTimeExceeded
- The job stopped because it exceeded the maximum allowed wait time. -
Stopped
- The training job has stopped.
-
- Stopping
-
-
Stopping
- Stopping the training job.
-
Valid values for
SecondaryStatus
are subject to change.We no longer support the following secondary statuses:
-
LaunchingMLInstances
-
PreparingTraining
-
DownloadingTrainingImage
"Starting"
"LaunchingMLInstances"
"PreparingTrainingStack"
"Downloading"
"DownloadingTrainingImage"
"Training"
"Uploading"
"Stopping"
"Stopped"
"MaxRuntimeExceeded"
"Completed"
"Failed"
"Interrupted"
"MaxWaitTimeExceeded"
"Updating"
"Restarting"
"Pending"
FailureReason
— (String
)If the training job failed, the reason it failed.
HyperParameters
— (map<String>
)Algorithm-specific parameters.
AlgorithmSpecification
— (map
)Information about the algorithm used for training, and algorithm metadata.
TrainingImage
— (String
)The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. For more information, see the note in theAlgorithmName
parameter description.AlgorithmName
— (String
)The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note: You must specify either the algorithm name to theAlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter. Note that theAlgorithmName
parameter is mutually exclusive with theTrainingImage
parameter. If you specify a value for theAlgorithmName
parameter, you can't specify a value forTrainingImage
, and vice versa. If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise anull
error.TrainingInputMode
— required — (String
)The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports
Pipe
mode, Amazon SageMaker streams data directly from Amazon S3 to the container.File mode
If an algorithm supports
File
mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports
FastFile
mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.FastFile
mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided."Pipe"
"File"
"FastFile"
MetricDefinitions
— (Array<map>
)A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
Name
— required — (String
)The name of the metric.
Regex
— required — (String
)A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
EnableSageMakerMetricsTimeSeries
— (Boolean
)To generate and save time-series metrics during training, set to
true
. The default isfalse
and time-series metrics aren't generated except in the following cases:-
You use one of the SageMaker built-in algorithms
-
You use one of the following Prebuilt SageMaker Docker Images:
-
Tensorflow (version >= 1.15)
-
MXNet (version >= 1.6)
-
PyTorch (version >= 1.3)
-
-
You specify at least one MetricDefinition
-
ContainerEntrypoint
— (Array<String>
)The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
ContainerArguments
— (Array<String>
)The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
TrainingImageConfig
— (map
)The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode
— required — (String
)The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to
Possible values include:Vpc
."Platform"
"Vpc"
TrainingRepositoryAuthConfig
— (map
)An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn
— required — (String
)The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn
— (String
)The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig
— (Array<map>
)An array of
Channel
objects that describes each data input channel.ChannelName
— required — (String
)The name of the channel.
DataSource
— required — (map
)The location of the channel data.
S3DataSource
— (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.If you choose
Possible values include:AugmentedManifestFile
, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training.AugmentedManifestFile
can only be used if the Channel's input mode isPipe
."ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
-
A manifest might look like this:
s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of
S3Uri
. Note that the prefix must be a valid non-emptyS3Uri
that precludes users from specifying a manifest whose individualS3Uri
is sourced from different S3 buckets.The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following
S3Uri
list:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uri
in this manifest is the input data for the channel for this data source. The object that eachS3Uri
points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
-
S3DataDistributionType
— (String
)If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify
FullyReplicated
.If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify
ShardedByS3Key
. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
Possible values include:ShardedByS3Key
. If the algorithm requires copying training data to the ML storage volume (whenTrainingInputMode
is set toFile
), this copies 1/n of the number of objects."FullyReplicated"
"ShardedByS3Key"
AttributeNames
— (Array<String>
)A list of one or more attribute names to use that are found in a specified augmented manifest file.
InstanceGroupNames
— (Array<String>
)A list of names of instance groups that get data from the S3 data source.
FileSystemDataSource
— (map
)The file system that is associated with a channel.
FileSystemId
— required — (String
)The file system id.
FileSystemAccessMode
— required — (String
)The access mode of the mount of the directory associated with the channel. A directory can be mounted either in
Possible values include:ro
(read-only) orrw
(read-write) mode."rw"
"ro"
FileSystemType
— required — (String
)The file system type.
Possible values include:"EFS"
"FSxLustre"
DirectoryPath
— required — (String
)The full path to the directory to associate with the channel.
ContentType
— (String
)The MIME type of the data.
CompressionType
— (String
)If training data is compressed, the compression type. The default value is
Possible values include:None
.CompressionType
is used only in Pipe input mode. In File mode, leave this field unset or set it to None."None"
"Gzip"
RecordWrapperType
— (String
)Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
Possible values include:"None"
"RecordIO"
InputMode
— (String
)(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode
, SageMaker uses the value set forTrainingInputMode
. Use this parameter to override theTrainingInputMode
setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, useFile
input mode. To stream data directly from Amazon S3 to the container, choosePipe
input mode.To use a model for incremental training, choose
Possible values include:File
input model."Pipe"
"File"
"FastFile"
ShuffleConfig
— (map
)A configuration for a shuffle option for input data in a channel. If you use
S3Prefix
forS3DataType
, this shuffles the results of the S3 key prefix matches. If you useManifestFile
, the order of the S3 object references in theManifestFile
is shuffled. If you useAugmentedManifestFile
, the order of the JSON lines in theAugmentedManifestFile
is shuffled. The shuffling order is determined using theSeed
value.For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with
S3DataDistributionType
ofShardedByS3Key
, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Seed
— required — (Integer
)Determines the shuffling order in
ShuffleConfig
value.
OutputDataConfig
— (map
)The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
// KMS Key Alias
"alias/ExampleAlias"
-
// Amazon Resource Name (ARN) of a KMS Key Alias
"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call
kms:Encrypt
. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One ZoneThe KMS key policy must grant permission to the IAM role that you specify in your
CreateTrainingJob
,CreateTransformJob
, orCreateHyperParameterTuningJob
requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.-
S3OutputPath
— required — (String
)Identifies the S3 path where you want SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.CompressionType
— (String
)The model output compression type. Select
Possible values include:None
to output an uncompressed model, recommended for large model outputs. Defaults to gzip."GZIP"
"NONE"
ResourceConfig
— (map
)Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType
— (String
)The ML compute instance type.
Note: SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (Possible values include:ml.p4de.24xlarge
) to reduce model training time. Theml.p4de.24xlarge
instances are available in the following Amazon Web Services Regions.- US East (N. Virginia) (us-east-1)
- US West (Oregon) (us-west-2)
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— (Integer
)The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as theTrainingInputMode
in the algorithm specification.When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include
ml.p4d
,ml.g4dn
, andml.g5
.When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through
VolumeSizeInGB
in theResourceConfig
API. For example, ML instance families that use EBS volumes includeml.c5
andml.p2
.To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
VolumeKmsKeyId
— (String
)The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be in any of the following formats:-
// KMS Key ID
"1234abcd-12ab-34cd-56ef-1234567890ab"
-
// Amazon Resource Name (ARN) of a KMS Key
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
-
KeepAlivePeriodInSeconds
— (Integer
)The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups
— (Array<map>
)The configuration of a heterogeneous cluster in JSON format.
InstanceType
— required — (String
)Specifies the instance type of the instance group.
Possible values include:"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.p3dn.24xlarge"
"ml.p4d.24xlarge"
"ml.p4de.24xlarge"
"ml.p5.48xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5n.xlarge"
"ml.c5n.2xlarge"
"ml.c5n.4xlarge"
"ml.c5n.9xlarge"
"ml.c5n.18xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.8xlarge"
"ml.c6i.4xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
InstanceCount
— required — (Integer
)Specifies the number of instances of the instance group.
InstanceGroupName
— required — (String
)Specifies the name of the instance group.
WarmPoolStatus
— (map
)The status of the warm pool associated with the training job.
Status
— required — (String
)The status of the warm pool.
-
InUse
: The warm pool is in use for the training job. -
Available
: The warm pool is available to reuse for a matching training job. -
Reused
: The warm pool moved to a matching training job for reuse. -
Terminated
: The warm pool is no longer available. Warm pools are unavailable if they are terminated by a user, terminated for a patch update, or terminated for exceeding the specifiedKeepAlivePeriodInSeconds
.
"Available"
"Terminated"
"Reused"
"InUse"
-
ResourceRetainedBillableTimeInSeconds
— (Integer
)The billable time in seconds used by the warm pool. Billable time refers to the absolute wall-clock time.
Multiply
ResourceRetainedBillableTimeInSeconds
by the number of instances (InstanceCount
) in your training cluster to get the total compute time SageMaker bills you if you run warm pool training. The formula is as follows:ResourceRetainedBillableTimeInSeconds * InstanceCount
.ReusedByJob
— (String
)The name of the matching training job that reused the warm pool.
VpcConfig
— (map
)A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
StoppingCondition
— (map
)Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the
SIGTERM
signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.MaxRuntimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When
RetryStrategy
is specified in the job request,MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a
TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.MaxWaitTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When
RetryStrategy
is specified in the job request,MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt.MaxPendingTimeInSeconds
— (Integer
)The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
CreationTime
— (Date
)A timestamp that indicates when the training job was created.
TrainingStartTime
— (Date
)Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of
TrainingEndTime
. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.TrainingEndTime
— (Date
)Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of
TrainingStartTime
and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.LastModifiedTime
— (Date
)A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions
— (Array<map>
)A history of all of the secondary statuses that the training job has transitioned through.
Status
— required — (String
)Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
- InProgress
-
-
Starting
- Starting the training job. -
Downloading
- An optional stage for algorithms that supportFile
training input mode. It indicates that data is being downloaded to the ML storage volumes. -
Training
- Training is in progress. -
Uploading
- Training is complete and the model artifacts are being uploaded to the S3 location.
-
- Completed
-
-
Completed
- The training job has completed.
-
- Failed
-
-
Failed
- The training job has failed. The reason for the failure is returned in theFailureReason
field ofDescribeTrainingJobResponse
.
-
- Stopped
-
-
MaxRuntimeExceeded
- The job stopped because it exceeded the maximum allowed runtime. -
Stopped
- The training job has stopped.
-
- Stopping
-
-
Stopping
- Stopping the training job.
-
We no longer support the following secondary statuses:
-
LaunchingMLInstances
-
PreparingTrainingStack
-
DownloadingTrainingImage
"Starting"
"LaunchingMLInstances"
"PreparingTrainingStack"
"Downloading"
"DownloadingTrainingImage"
"Training"
"Uploading"
"Stopping"
"Stopped"
"MaxRuntimeExceeded"
"Completed"
"Failed"
"Interrupted"
"MaxWaitTimeExceeded"
"Updating"
"Restarting"
"Pending"
StartTime
— required — (Date
)A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime
— (Date
)A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage
— (String
)A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
- Starting
-
-
Starting the training job.
-
Launching requested ML instances.
-
Insufficient capacity error from EC2 while launching instances, retrying!
-
Launched instance was unhealthy, replacing it!
-
Preparing the instances for training.
-
- Training
-
-
Training image download completed. Training in progress.
-
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view
TrainingJobStatus
andSecondaryStatus
in DescribeTrainingJob, andStatusMessage
together. For example, at the start of a training job, you might see the following:-
TrainingJobStatus
- InProgress -
SecondaryStatus
- Training -
StatusMessage
- Downloading the training image
FinalMetricDataList
— (Array<map>
)A collection of
MetricData
objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.MetricName
— (String
)The name of the metric.
Value
— (Float
)The value of the metric.
Timestamp
— (Date
)The date and time that the algorithm emitted the metric.
EnableNetworkIsolation
— (Boolean
)If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose
True
. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.EnableInterContainerTrafficEncryption
— (Boolean
)To encrypt all communications between ML compute instances in distributed training, choose
True
. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.EnableManagedSpotTraining
— (Boolean
)A Boolean indicating whether managed spot training is enabled (
True
) or not (False
).CheckpointConfig
— (map
)Contains information about the output location for managed spot training checkpoint data.
S3Uri
— required — (String
)Identifies the S3 path where you want SageMaker to store checkpoints. For example,
s3://bucket-name/key-name-prefix
.LocalPath
— (String
)(Optional) The local directory where checkpoints are written. The default directory is
/opt/ml/checkpoints/
.
TrainingTimeInSeconds
— (Integer
)The training time in seconds.
BillableTimeInSeconds
— (Integer
)The billable time in seconds. Billable time refers to the absolute wall-clock time.
Multiply
BillableTimeInSeconds
by the number of instances (InstanceCount
) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows:BillableTimeInSeconds * InstanceCount
.You can calculate the savings from using managed spot training using the formula
(1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100
. For example, ifBillableTimeInSeconds
is 100 andTrainingTimeInSeconds
is 500, the savings is 80%.DebugHookConfig
— (map
)Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the
DebugHookConfig
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.LocalPath
— (String
)Path to local storage location for metrics and tensors. Defaults to
/opt/ml/output/tensors/
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for metrics and tensors.
HookParameters
— (map<String>
)Configuration information for the Amazon SageMaker Debugger hook parameters.
CollectionConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the
CollectionConfiguration
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.CollectionName
— (String
)The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters
— (map<String>
)Parameter values for the tensor collection. The allowed parameters are
"name"
,"include_regex"
,"reduction_config"
,"save_config"
,"tensor_names"
, and"save_histogram"
.
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
DebugRuleConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a custom rule for debugging a training job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
TensorBoardOutputConfig
— (map
)Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath
— (String
)Path to local storage location for tensorBoard output. Defaults to
/opt/ml/output/tensorboard
.S3OutputPath
— required — (String
)Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses
— (Array<map>
)Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.
RuleConfigurationName
— (String
)The name of the rule configuration.
RuleEvaluationJobArn
— (String
)The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus
— (String
)Status of the rule evaluation.
Possible values include:"InProgress"
"NoIssuesFound"
"IssuesFound"
"Error"
"Stopping"
"Stopped"
StatusDetails
— (String
)Details from the rule evaluation.
LastModifiedTime
— (Date
)Timestamp when the rule evaluation status was last modified.
ProfilerConfig
— (map
)Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath
— (String
)Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds
— (Integer
)A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters
— (map<String>
)Configuration information for capturing framework metrics. Available key strings for different profiling options are
DetailedProfilingConfig
,PythonProfilingConfig
, andDataLoaderProfilingConfig
. The following codes are configuration structures for theProfilingParameters
parameter. To learn more about how to configure theProfilingParameters
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.DisableProfiler
— (Boolean
)Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to
True
.
ProfilerRuleConfigurations
— (Array<map>
)Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
RuleConfigurationName
— required — (String
)The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath
— (String
)Path to local storage location for output of rules. Defaults to
/opt/ml/processing/output/rule/
.S3OutputPath
— (String
)Path to Amazon S3 storage location for rules.
RuleEvaluatorImage
— required — (String
)The Amazon Elastic Container Registry Image for the managed rule evaluation.
InstanceType
— (String
)The instance type to deploy a custom rule for profiling a training job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— (Integer
)The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters
— (map<String>
)Runtime configuration for rule container.
ProfilerRuleEvaluationStatuses
— (Array<map>
)Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.
RuleConfigurationName
— (String
)The name of the rule configuration.
RuleEvaluationJobArn
— (String
)The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus
— (String
)Status of the rule evaluation.
Possible values include:"InProgress"
"NoIssuesFound"
"IssuesFound"
"Error"
"Stopping"
"Stopped"
StatusDetails
— (String
)Details from the rule evaluation.
LastModifiedTime
— (Date
)Timestamp when the rule evaluation status was last modified.
ProfilingStatus
— (String
)Profiling status of a training job.
Possible values include:"Enabled"
"Disabled"
Environment
— (map<String>
)The environment variables to set in the Docker container.
RetryStrategy
— (map
)The number of times to retry the job when the job fails due to an
InternalServerError
.MaximumRetryAttempts
— required — (Integer
)The number of times to retry the job. When the job is retried, it's
SecondaryStatus
is changed toSTARTING
.
RemoteDebugConfig
— (map
)Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
EnableRemoteDebug
— (Boolean
)If set to True, enables remote debugging.
InfraCheckConfig
— (map
)Contains information about the infrastructure health check configuration for the training job.
EnableInfraCheck
— (Boolean
)Enables an infrastructure health check.
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('endpointInService', params = {}, [callback]) ⇒ AWS.Request
Waits for the
endpointInService
state by periodically calling the underlying SageMaker.describeEndpoint() operation every 30 seconds (at most 120 times).Examples:
Waiting for the endpointInService state
var params = { EndpointName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('endpointInService', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
EndpointName
— (String
)The name of the endpoint.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointName
— (String
)Name of the endpoint.
EndpointArn
— (String
)The Amazon Resource Name (ARN) of the endpoint.
EndpointConfigName
— (String
)The name of the endpoint configuration associated with this endpoint.
ProductionVariants
— (Array<map>
)An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.
VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight, as specified in the
UpdateEndpointWeightsAndCapacities
request.CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in the
UpdateEndpointWeightsAndCapacities
request.VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for the endpoint update.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
DataCaptureConfig
— (map
)The currently active data capture configuration used by your Endpoint.
EnableCapture
— required — (Boolean
)Whether data capture is enabled or disabled.
CaptureStatus
— required — (String
)Whether data capture is currently functional.
Possible values include:"Started"
"Stopped"
CurrentSamplingPercentage
— required — (Integer
)The percentage of requests being captured by your Endpoint.
DestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
KmsKeyId
— required — (String
)The KMS key being used to encrypt the data in Amazon S3.
EndpointStatus
— (String
)The status of the endpoint.
-
OutOfService
: Endpoint is not available to take incoming requests. -
Creating
: CreateEndpoint is executing. -
Updating
: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing. -
SystemUpdating
: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count. -
RollingBack
: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to anInService
status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly. -
InService
: Endpoint is available to process incoming requests. -
Deleting
: DeleteEndpoint is executing. -
Failed
: Endpoint could not be created, updated, or re-scaled. Use theFailureReason
value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint. -
UpdateRollbackFailed
: Both the rolling deployment and auto-rollback failed. Your endpoint is in service with a mix of the old and new endpoint configurations. For information about how to remedy this issue and restore the endpoint's status toInService
, see Rolling Deployments.
"OutOfService"
"Creating"
"Updating"
"SystemUpdating"
"RollingBack"
"InService"
"Deleting"
"Failed"
"UpdateRollbackFailed"
-
FailureReason
— (String
)If the status of the endpoint is
Failed
, the reason why it failed.CreationTime
— (Date
)A timestamp that shows when the endpoint was created.
LastModifiedTime
— (Date
)A timestamp that shows when the endpoint was last modified.
LastDeploymentConfig
— (map
)The most recent deployment configuration for the endpoint.
BlueGreenUpdatePolicy
— (map
)Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
TrafficRoutingConfiguration
— required — (map
)Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.
Type
— required — (String
)Traffic routing strategy type.
-
ALL_AT_ONCE
: Endpoint traffic shifts to the new fleet in a single step. -
CANARY
: Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic. -
LINEAR
: Endpoint traffic shifts to the new fleet in n steps of a configurable size.
"ALL_AT_ONCE"
"CANARY"
"LINEAR"
-
WaitIntervalInSeconds
— required — (Integer
)The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.
CanarySize
— (map
)Batch size for the first step to turn on traffic on the new endpoint fleet.
Value
must be less than or equal to 50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
LinearStepSize
— (map
)Batch size for each step to turn on traffic on the new endpoint fleet.
Value
must be 10-50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
TerminationWaitInSeconds
— (Integer
)Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.
MaximumExecutionTimeoutInSeconds
— (Integer
)Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in
TerminationWaitInSeconds
andWaitIntervalInSeconds
.
RollingUpdatePolicy
— (map
)Specifies a rolling deployment strategy for updating a SageMaker endpoint.
MaximumBatchSize
— required — (map
)Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.
Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
WaitIntervalInSeconds
— required — (Integer
)The length of the baking period, during which SageMaker monitors alarms for each batch on the new fleet.
MaximumExecutionTimeoutInSeconds
— (Integer
)The time limit for the total deployment. Exceeding this limit causes a timeout.
RollbackMaximumBatchSize
— (map
)Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.
Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
AutoRollbackConfiguration
— (map
)Automatic rollback configuration for handling endpoint deployment failures and recovery.
Alarms
— (Array<map>
)List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.
AlarmName
— (String
)The name of a CloudWatch alarm in your account.
AsyncInferenceConfig
— (map
)Returns the description of an endpoint configuration created using the
CreateEndpointConfig
API.ClientConfig
— (map
)Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
MaxConcurrentInvocationsPerInstance
— (Integer
)The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
OutputConfig
— required — (map
)Specifies the configuration for asynchronous inference invocation outputs.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
S3OutputPath
— (String
)The Amazon S3 location to upload inference responses to.
NotificationConfig
— (map
)Specifies the configuration for notifications of inference results for asynchronous inference.
SuccessTopic
— (String
)Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
ErrorTopic
— (String
)Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
IncludeInferenceResponseIn
— (Array<String>
)The Amazon SNS topics where you want the inference response to be included.
Note: The inference response is included only if the response size is less than or equal to 128 KB.
S3FailurePath
— (String
)The Amazon S3 location to upload failure inference responses to.
PendingDeploymentSummary
— (map
)Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.
EndpointConfigName
— required — (String
)The name of the endpoint configuration used in the deployment.
ProductionVariants
— (Array<map>
)An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint for the in-progress deployment.
VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
InstanceType
— (String
)The type of instances associated with the variant.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
StartTime
— (Date
)The start time of the deployment.
ShadowProductionVariants
— (Array<map>
)An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint in shadow mode with production traffic replicated from the model specified on
ProductionVariants
for the in-progress deployment.VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
InstanceType
— (String
)The type of instances associated with the variant.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
ExplainerConfig
— (map
)The configuration parameters for an explainer.
ClarifyExplainerConfig
— (map
)A member of
ExplainerConfig
that contains configuration parameters for the SageMaker Clarify explainer.EnableExplanations
— (String
)A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See
EnableExplanations
for additional information.InferenceConfig
— (map
)The inference configuration parameter for the model container.
FeaturesAttribute
— (String
)Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if
FeaturesAttribute
is the JMESPath expression'myfeatures'
, it extracts a list of features[1,2,3]
from request data'{"myfeatures":[1,2,3]}'
.ContentTemplate
— (String
)A template string used to format a JSON record into an acceptable model container input. For example, a
ContentTemplate
string'{"myfeatures":$features}'
will format a list of features[1,2,3]
into the record string'{"myfeatures":[1,2,3]}'
. Required only when the model container input is in JSON Lines format.MaxRecordCount
— (Integer
)The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If
MaxRecordCount
is1
, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.MaxPayloadInMB
— (Integer
)The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
6
MB.ProbabilityIndex
— (Integer
)A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.
Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability:
'1,0.6'
, setProbabilityIndex
to1
to select the probability value0.6
.Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, setProbabilityIndex
to1
to select the probability values[0.1,0.6,0.3]
.LabelIndex
— (Integer
)A zero-based index used to extract a label header or list of label headers from model container output in CSV format.
Example for a multiclass model: If the model container output consists of label headers followed by probabilities:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, setLabelIndex
to0
to select the label headers['cat','dog','fish']
.ProbabilityAttribute
— (String
)A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.
Example: If the model container output of a single request is
'{"predicted_label":1,"probability":0.6}'
, then setProbabilityAttribute
to'probability'
.LabelAttribute
— (String
)A JMESPath expression used to locate the list of label headers in the model container output.
Example: If the model container output of a batch request is
'{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}'
, then setLabelAttribute
to'labels'
to extract the list of label headers["cat","dog","fish"]
LabelHeaders
— (Array<String>
)For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the
InvokeEndpoint
API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.FeatureHeaders
— (Array<String>
)The names of the features. If provided, these are included in the endpoint response payload to help readability of the
InvokeEndpoint
output. See the Response section under Invoke the endpoint in the Developer Guide for more information.FeatureTypes
— (Array<String>
)A list of data types of the features (optional). Applicable only to NLP explainability. If provided,
FeatureTypes
must have at least one'text'
string (for example,['text']
). IfFeatureTypes
is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.
ShapConfig
— required — (map
)The configuration for SHAP analysis.
ShapBaselineConfig
— required — (map
)The configuration for the SHAP baseline of the Kernal SHAP algorithm.
MimeType
— (String
)The MIME type of the baseline data. Choose from
'text/csv'
or'application/jsonlines'
. Defaults to'text/csv'
.ShapBaseline
— (String
)The inline SHAP baseline data in string format.
ShapBaseline
can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by theGranularity
of theTextConfig
parameter. The size limit forShapBasline
is 4 KB. Use theShapBaselineUri
parameter if you want to provide more than 4 KB of baseline data.ShapBaselineUri
— (String
)The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the
ShapBaselineUri
should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.
NumberOfSamples
— (Integer
)The number of samples to be used for analysis by the Kernal SHAP algorithm.
Note: The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.UseLogit
— (Boolean
)A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.
Seed
— (Integer
)The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.
TextConfig
— (map
)A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.
Language
— required — (String
)Specifies the language of the text features in ISO 639-1 or ISO 639-3 code of a supported language.
Note: For a mix of multiple languages, use codePossible values include:'xx'
."af"
"sq"
"ar"
"hy"
"eu"
"bn"
"bg"
"ca"
"zh"
"hr"
"cs"
"da"
"nl"
"en"
"et"
"fi"
"fr"
"de"
"el"
"gu"
"he"
"hi"
"hu"
"is"
"id"
"ga"
"it"
"kn"
"ky"
"lv"
"lt"
"lb"
"mk"
"ml"
"mr"
"ne"
"nb"
"fa"
"pl"
"pt"
"ro"
"ru"
"sa"
"sr"
"tn"
"si"
"sk"
"sl"
"es"
"sv"
"tl"
"ta"
"tt"
"te"
"tr"
"uk"
"ur"
"yo"
"lij"
"xx"
Granularity
— required — (String
)The unit of granularity for the analysis of text features. For example, if the unit is
Possible values include:'token'
, then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature."token"
"sentence"
"paragraph"
ShadowProductionVariants
— (Array<map>
)An array of ProductionVariantSummary objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on
ProductionVariants
.VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight, as specified in the
UpdateEndpointWeightsAndCapacities
request.CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in the
UpdateEndpointWeightsAndCapacities
request.VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for the endpoint update.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('endpointDeleted', params = {}, [callback]) ⇒ AWS.Request
Waits for the
endpointDeleted
state by periodically calling the underlying SageMaker.describeEndpoint() operation every 30 seconds (at most 60 times).Examples:
Waiting for the endpointDeleted state
var params = { EndpointName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('endpointDeleted', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
EndpointName
— (String
)The name of the endpoint.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:EndpointName
— (String
)Name of the endpoint.
EndpointArn
— (String
)The Amazon Resource Name (ARN) of the endpoint.
EndpointConfigName
— (String
)The name of the endpoint configuration associated with this endpoint.
ProductionVariants
— (Array<map>
)An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.
VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight, as specified in the
UpdateEndpointWeightsAndCapacities
request.CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in the
UpdateEndpointWeightsAndCapacities
request.VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for the endpoint update.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
DataCaptureConfig
— (map
)The currently active data capture configuration used by your Endpoint.
EnableCapture
— required — (Boolean
)Whether data capture is enabled or disabled.
CaptureStatus
— required — (String
)Whether data capture is currently functional.
Possible values include:"Started"
"Stopped"
CurrentSamplingPercentage
— required — (Integer
)The percentage of requests being captured by your Endpoint.
DestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
KmsKeyId
— required — (String
)The KMS key being used to encrypt the data in Amazon S3.
EndpointStatus
— (String
)The status of the endpoint.
-
OutOfService
: Endpoint is not available to take incoming requests. -
Creating
: CreateEndpoint is executing. -
Updating
: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing. -
SystemUpdating
: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count. -
RollingBack
: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to anInService
status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly. -
InService
: Endpoint is available to process incoming requests. -
Deleting
: DeleteEndpoint is executing. -
Failed
: Endpoint could not be created, updated, or re-scaled. Use theFailureReason
value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint. -
UpdateRollbackFailed
: Both the rolling deployment and auto-rollback failed. Your endpoint is in service with a mix of the old and new endpoint configurations. For information about how to remedy this issue and restore the endpoint's status toInService
, see Rolling Deployments.
"OutOfService"
"Creating"
"Updating"
"SystemUpdating"
"RollingBack"
"InService"
"Deleting"
"Failed"
"UpdateRollbackFailed"
-
FailureReason
— (String
)If the status of the endpoint is
Failed
, the reason why it failed.CreationTime
— (Date
)A timestamp that shows when the endpoint was created.
LastModifiedTime
— (Date
)A timestamp that shows when the endpoint was last modified.
LastDeploymentConfig
— (map
)The most recent deployment configuration for the endpoint.
BlueGreenUpdatePolicy
— (map
)Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
TrafficRoutingConfiguration
— required — (map
)Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.
Type
— required — (String
)Traffic routing strategy type.
-
ALL_AT_ONCE
: Endpoint traffic shifts to the new fleet in a single step. -
CANARY
: Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic. -
LINEAR
: Endpoint traffic shifts to the new fleet in n steps of a configurable size.
"ALL_AT_ONCE"
"CANARY"
"LINEAR"
-
WaitIntervalInSeconds
— required — (Integer
)The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.
CanarySize
— (map
)Batch size for the first step to turn on traffic on the new endpoint fleet.
Value
must be less than or equal to 50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
LinearStepSize
— (map
)Batch size for each step to turn on traffic on the new endpoint fleet.
Value
must be 10-50% of the variant's total instance count.Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
TerminationWaitInSeconds
— (Integer
)Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.
MaximumExecutionTimeoutInSeconds
— (Integer
)Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in
TerminationWaitInSeconds
andWaitIntervalInSeconds
.
RollingUpdatePolicy
— (map
)Specifies a rolling deployment strategy for updating a SageMaker endpoint.
MaximumBatchSize
— required — (map
)Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.
Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
WaitIntervalInSeconds
— required — (Integer
)The length of the baking period, during which SageMaker monitors alarms for each batch on the new fleet.
MaximumExecutionTimeoutInSeconds
— (Integer
)The time limit for the total deployment. Exceeding this limit causes a timeout.
RollbackMaximumBatchSize
— (map
)Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.
Type
— required — (String
)Specifies the endpoint capacity type.
-
INSTANCE_COUNT
: The endpoint activates based on the number of instances. -
CAPACITY_PERCENT
: The endpoint activates based on the specified percentage of capacity.
"INSTANCE_COUNT"
"CAPACITY_PERCENT"
-
Value
— required — (Integer
)Defines the capacity size, either as a number of instances or a capacity percentage.
AutoRollbackConfiguration
— (map
)Automatic rollback configuration for handling endpoint deployment failures and recovery.
Alarms
— (Array<map>
)List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.
AlarmName
— (String
)The name of a CloudWatch alarm in your account.
AsyncInferenceConfig
— (map
)Returns the description of an endpoint configuration created using the
CreateEndpointConfig
API.ClientConfig
— (map
)Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
MaxConcurrentInvocationsPerInstance
— (Integer
)The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
OutputConfig
— required — (map
)Specifies the configuration for asynchronous inference invocation outputs.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
S3OutputPath
— (String
)The Amazon S3 location to upload inference responses to.
NotificationConfig
— (map
)Specifies the configuration for notifications of inference results for asynchronous inference.
SuccessTopic
— (String
)Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
ErrorTopic
— (String
)Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
IncludeInferenceResponseIn
— (Array<String>
)The Amazon SNS topics where you want the inference response to be included.
Note: The inference response is included only if the response size is less than or equal to 128 KB.
S3FailurePath
— (String
)The Amazon S3 location to upload failure inference responses to.
PendingDeploymentSummary
— (map
)Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.
EndpointConfigName
— required — (String
)The name of the endpoint configuration used in the deployment.
ProductionVariants
— (Array<map>
)An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint for the in-progress deployment.
VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
InstanceType
— (String
)The type of instances associated with the variant.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
StartTime
— (Date
)The start time of the deployment.
ShadowProductionVariants
— (Array<map>
)An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint in shadow mode with production traffic replicated from the model specified on
ProductionVariants
for the in-progress deployment.VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
InstanceType
— (String
)The type of instances associated with the variant.
Possible values include:"ml.t2.medium"
"ml.t2.large"
"ml.t2.xlarge"
"ml.t2.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m5d.large"
"ml.m5d.xlarge"
"ml.m5d.2xlarge"
"ml.m5d.4xlarge"
"ml.m5d.12xlarge"
"ml.m5d.24xlarge"
"ml.c4.large"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.large"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.c5d.large"
"ml.c5d.xlarge"
"ml.c5d.2xlarge"
"ml.c5d.4xlarge"
"ml.c5d.9xlarge"
"ml.c5d.18xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.12xlarge"
"ml.r5.24xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.12xlarge"
"ml.r5d.24xlarge"
"ml.inf1.xlarge"
"ml.inf1.2xlarge"
"ml.inf1.6xlarge"
"ml.inf1.24xlarge"
"ml.dl1.24xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.g6.xlarge"
"ml.g6.2xlarge"
"ml.g6.4xlarge"
"ml.g6.8xlarge"
"ml.g6.12xlarge"
"ml.g6.16xlarge"
"ml.g6.24xlarge"
"ml.g6.48xlarge"
"ml.p4d.24xlarge"
"ml.c7g.large"
"ml.c7g.xlarge"
"ml.c7g.2xlarge"
"ml.c7g.4xlarge"
"ml.c7g.8xlarge"
"ml.c7g.12xlarge"
"ml.c7g.16xlarge"
"ml.m6g.large"
"ml.m6g.xlarge"
"ml.m6g.2xlarge"
"ml.m6g.4xlarge"
"ml.m6g.8xlarge"
"ml.m6g.12xlarge"
"ml.m6g.16xlarge"
"ml.m6gd.large"
"ml.m6gd.xlarge"
"ml.m6gd.2xlarge"
"ml.m6gd.4xlarge"
"ml.m6gd.8xlarge"
"ml.m6gd.12xlarge"
"ml.m6gd.16xlarge"
"ml.c6g.large"
"ml.c6g.xlarge"
"ml.c6g.2xlarge"
"ml.c6g.4xlarge"
"ml.c6g.8xlarge"
"ml.c6g.12xlarge"
"ml.c6g.16xlarge"
"ml.c6gd.large"
"ml.c6gd.xlarge"
"ml.c6gd.2xlarge"
"ml.c6gd.4xlarge"
"ml.c6gd.8xlarge"
"ml.c6gd.12xlarge"
"ml.c6gd.16xlarge"
"ml.c6gn.large"
"ml.c6gn.xlarge"
"ml.c6gn.2xlarge"
"ml.c6gn.4xlarge"
"ml.c6gn.8xlarge"
"ml.c6gn.12xlarge"
"ml.c6gn.16xlarge"
"ml.r6g.large"
"ml.r6g.xlarge"
"ml.r6g.2xlarge"
"ml.r6g.4xlarge"
"ml.r6g.8xlarge"
"ml.r6g.12xlarge"
"ml.r6g.16xlarge"
"ml.r6gd.large"
"ml.r6gd.xlarge"
"ml.r6gd.2xlarge"
"ml.r6gd.4xlarge"
"ml.r6gd.8xlarge"
"ml.r6gd.12xlarge"
"ml.r6gd.16xlarge"
"ml.p4de.24xlarge"
"ml.trn1.2xlarge"
"ml.trn1.32xlarge"
"ml.trn1n.32xlarge"
"ml.inf2.xlarge"
"ml.inf2.8xlarge"
"ml.inf2.24xlarge"
"ml.inf2.48xlarge"
"ml.p5.48xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
AcceleratorType
— (String
)The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
Possible values include:"ml.eia1.medium"
"ml.eia1.large"
"ml.eia1.xlarge"
"ml.eia2.medium"
"ml.eia2.large"
"ml.eia2.xlarge"
VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
ExplainerConfig
— (map
)The configuration parameters for an explainer.
ClarifyExplainerConfig
— (map
)A member of
ExplainerConfig
that contains configuration parameters for the SageMaker Clarify explainer.EnableExplanations
— (String
)A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See
EnableExplanations
for additional information.InferenceConfig
— (map
)The inference configuration parameter for the model container.
FeaturesAttribute
— (String
)Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if
FeaturesAttribute
is the JMESPath expression'myfeatures'
, it extracts a list of features[1,2,3]
from request data'{"myfeatures":[1,2,3]}'
.ContentTemplate
— (String
)A template string used to format a JSON record into an acceptable model container input. For example, a
ContentTemplate
string'{"myfeatures":$features}'
will format a list of features[1,2,3]
into the record string'{"myfeatures":[1,2,3]}'
. Required only when the model container input is in JSON Lines format.MaxRecordCount
— (Integer
)The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If
MaxRecordCount
is1
, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.MaxPayloadInMB
— (Integer
)The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to
6
MB.ProbabilityIndex
— (Integer
)A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.
Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability:
'1,0.6'
, setProbabilityIndex
to1
to select the probability value0.6
.Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, setProbabilityIndex
to1
to select the probability values[0.1,0.6,0.3]
.LabelIndex
— (Integer
)A zero-based index used to extract a label header or list of label headers from model container output in CSV format.
Example for a multiclass model: If the model container output consists of label headers followed by probabilities:
'"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"'
, setLabelIndex
to0
to select the label headers['cat','dog','fish']
.ProbabilityAttribute
— (String
)A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.
Example: If the model container output of a single request is
'{"predicted_label":1,"probability":0.6}'
, then setProbabilityAttribute
to'probability'
.LabelAttribute
— (String
)A JMESPath expression used to locate the list of label headers in the model container output.
Example: If the model container output of a batch request is
'{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}'
, then setLabelAttribute
to'labels'
to extract the list of label headers["cat","dog","fish"]
LabelHeaders
— (Array<String>
)For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the
InvokeEndpoint
API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.FeatureHeaders
— (Array<String>
)The names of the features. If provided, these are included in the endpoint response payload to help readability of the
InvokeEndpoint
output. See the Response section under Invoke the endpoint in the Developer Guide for more information.FeatureTypes
— (Array<String>
)A list of data types of the features (optional). Applicable only to NLP explainability. If provided,
FeatureTypes
must have at least one'text'
string (for example,['text']
). IfFeatureTypes
is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.
ShapConfig
— required — (map
)The configuration for SHAP analysis.
ShapBaselineConfig
— required — (map
)The configuration for the SHAP baseline of the Kernal SHAP algorithm.
MimeType
— (String
)The MIME type of the baseline data. Choose from
'text/csv'
or'application/jsonlines'
. Defaults to'text/csv'
.ShapBaseline
— (String
)The inline SHAP baseline data in string format.
ShapBaseline
can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by theGranularity
of theTextConfig
parameter. The size limit forShapBasline
is 4 KB. Use theShapBaselineUri
parameter if you want to provide more than 4 KB of baseline data.ShapBaselineUri
— (String
)The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the
ShapBaselineUri
should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.
NumberOfSamples
— (Integer
)The number of samples to be used for analysis by the Kernal SHAP algorithm.
Note: The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.UseLogit
— (Boolean
)A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.
Seed
— (Integer
)The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.
TextConfig
— (map
)A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.
Language
— required — (String
)Specifies the language of the text features in ISO 639-1 or ISO 639-3 code of a supported language.
Note: For a mix of multiple languages, use codePossible values include:'xx'
."af"
"sq"
"ar"
"hy"
"eu"
"bn"
"bg"
"ca"
"zh"
"hr"
"cs"
"da"
"nl"
"en"
"et"
"fi"
"fr"
"de"
"el"
"gu"
"he"
"hi"
"hu"
"is"
"id"
"ga"
"it"
"kn"
"ky"
"lv"
"lt"
"lb"
"mk"
"ml"
"mr"
"ne"
"nb"
"fa"
"pl"
"pt"
"ro"
"ru"
"sa"
"sr"
"tn"
"si"
"sk"
"sl"
"es"
"sv"
"tl"
"ta"
"tt"
"te"
"tr"
"uk"
"ur"
"yo"
"lij"
"xx"
Granularity
— required — (String
)The unit of granularity for the analysis of text features. For example, if the unit is
Possible values include:'token'
, then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature."token"
"sentence"
"paragraph"
ShadowProductionVariants
— (Array<map>
)An array of ProductionVariantSummary objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on
ProductionVariants
.VariantName
— required — (String
)The name of the variant.
DeployedImages
— (Array<map>
)An array of
DeployedImage
objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of thisProductionVariant
.SpecifiedImage
— (String
)The image path you specified when you created the model.
ResolvedImage
— (String
)The specific digest path of the image hosted in this
ProductionVariant
.ResolutionTime
— (Date
)The date and time when the image path for the model resolved to the
ResolvedImage
CurrentWeight
— (Float
)The weight associated with the variant.
DesiredWeight
— (Float
)The requested weight, as specified in the
UpdateEndpointWeightsAndCapacities
request.CurrentInstanceCount
— (Integer
)The number of instances associated with the variant.
DesiredInstanceCount
— (Integer
)The number of instances requested in the
UpdateEndpointWeightsAndCapacities
request.VariantStatus
— (Array<map>
)The endpoint variant status which describes the current deployment stage status or operational status.
Status
— required — (String
)The endpoint variant status which describes the current deployment stage status or operational status.
-
Creating
: Creating inference resources for the production variant. -
Deleting
: Terminating inference resources for the production variant. -
Updating
: Updating capacity for the production variant. -
ActivatingTraffic
: Turning on traffic for the production variant. -
Baking
: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
"Creating"
"Updating"
"Deleting"
"ActivatingTraffic"
"Baking"
-
StatusMessage
— (String
)A message that describes the status of the production variant.
StartTime
— (Date
)The start time of the current status change.
CurrentServerlessConfig
— (map
)The serverless configuration for the endpoint.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
DesiredServerlessConfig
— (map
)The serverless configuration requested for the endpoint update.
MemorySizeInMB
— required — (Integer
)The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency
— required — (Integer
)The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency
— (Integer
)The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to
MaxConcurrency
.Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs.
ManagedInstanceScaling
— (map
)Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status
— (String
)Indicates whether managed instance scaling is enabled.
Possible values include:"ENABLED"
"DISABLED"
MinInstanceCount
— (Integer
)The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount
— (Integer
)The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig
— (map
)Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy
— required — (String
)Sets how the endpoint routes incoming traffic:
-
LEAST_OUTSTANDING_REQUESTS
: The endpoint routes requests to the specific instances that have more capacity to process them. -
RANDOM
: The endpoint routes each request to a randomly chosen instance.
"LEAST_OUTSTANDING_REQUESTS"
"RANDOM"
-
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('transformJobCompletedOrStopped', params = {}, [callback]) ⇒ AWS.Request
Waits for the
transformJobCompletedOrStopped
state by periodically calling the underlying SageMaker.describeTransformJob() operation every 60 seconds (at most 60 times).Examples:
Waiting for the transformJobCompletedOrStopped state
var params = { TransformJobName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('transformJobCompletedOrStopped', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
TransformJobName
— (String
)The name of the transform job that you want to view details of.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:TransformJobName
— (String
)The name of the transform job.
TransformJobArn
— (String
)The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus
— (String
)The status of the transform job. If the transform job failed, the reason is returned in the
Possible values include:FailureReason
field."InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
FailureReason
— (String
)If the transform job failed,
FailureReason
describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch.ModelName
— (String
)The name of the model used in the transform job.
MaxConcurrentTransforms
— (Integer
)The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.
ModelClientConfig
— (map
)The timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds
— (Integer
)The timeout value in seconds for an invocation request. The default value is 600.
InvocationsMaxRetries
— (Integer
)The maximum number of retries when invocation requests are failing. The default value is 3.
MaxPayloadInMB
— (Integer
)The maximum payload size, in MB, used in the transform job.
BatchStrategy
— (String
)Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
To enable the batch strategy, you must set
Possible values include:SplitType
toLine
,RecordIO
, orTFRecord
."MultiRecord"
"SingleRecord"
Environment
— (map<String>
)The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput
— (map
)Describes the dataset to be transformed and the Amazon S3 location where it is stored.
DataSource
— required — (map
)Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource
— required — (map
)The S3 location of the data source that is associated with a channel.
S3DataType
— required — (String
)If you choose
S3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.If you choose
ManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.The following values are compatible:
ManifestFile
,S3Prefix
The following value is not compatible:
Possible values include:AugmentedManifestFile
"ManifestFile"
"S3Prefix"
"AugmentedManifestFile"
S3Uri
— required — (String
)Depending on the value specified for the
S3DataType
, identifies either a key name prefix or a manifest. For example:-
A key name prefix might look like this:
s3://bucketname/exampleprefix/
. -
A manifest might look like this:
s3://bucketname/example.manifest
The manifest is an S3 object which is a JSON file with the following format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
The preceding JSON matches the following
S3Uris
:s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of
S3Uris
in this manifest constitutes the input data for the channel for this datasource. The object that eachS3Uris
points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
-
ContentType
— (String
)The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType
— (String
)If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is
Possible values include:None
."None"
"Gzip"
SplitType
— (String
)The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for
SplitType
isNone
, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter toLine
to split records on a newline character boundary.SplitType
also supports a number of record-oriented binary data formats. Currently, the supported record formats are:-
RecordIO
-
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the
BatchStrategy
andMaxPayloadInMB
parameters. When the value ofBatchStrategy
isMultiRecord
, Amazon SageMaker sends the maximum number of records in each request, up to theMaxPayloadInMB
limit. If the value ofBatchStrategy
isSingleRecord
, Amazon SageMaker sends individual records in each request.Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value ofPossible values include:BatchStrategy
is set toSingleRecord
. Padding is not removed if the value ofBatchStrategy
is set toMultiRecord
. For more information aboutRecordIO
, see Create a Dataset Using RecordIO in the MXNet documentation. For more information aboutTFRecord
, see Consuming TFRecord data in the TensorFlow documentation."None"
"Line"
"RecordIO"
"TFRecord"
-
TransformOutput
— (map
)Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath
— required — (String
)The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example,
s3://bucket-name/key-name-prefix
.For every S3 object used as input for the transform job, batch transform stores the transformed data with an .
out
suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored ats3://bucket-name/input-name-prefix/dataset01/data.csv
, batch transform stores the transformed data ats3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out
. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out
file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.Accept
— (String
)The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith
— (String
)Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify
Possible values include:None
. To add a newline character at the end of every transformed record, specifyLine
."None"
"Line"
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
KmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
-
DataCaptureConfig
— (map
)Configuration to control how SageMaker captures inference data.
DestinationS3Uri
— required — (String
)The Amazon S3 location being used to capture the data.
KmsKeyId
— (String
)The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
GenerateInferenceId
— (Boolean
)Flag that indicates whether to append inference id to the output.
TransformResources
— (map
)Describes the resources, including ML instance types and ML instance count, to use for the transform job.
InstanceType
— required — (String
)The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or
Possible values include:ml.m5.large
instance types."ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.m6i.large"
"ml.m6i.xlarge"
"ml.m6i.2xlarge"
"ml.m6i.4xlarge"
"ml.m6i.8xlarge"
"ml.m6i.12xlarge"
"ml.m6i.16xlarge"
"ml.m6i.24xlarge"
"ml.m6i.32xlarge"
"ml.c6i.large"
"ml.c6i.xlarge"
"ml.c6i.2xlarge"
"ml.c6i.4xlarge"
"ml.c6i.8xlarge"
"ml.c6i.12xlarge"
"ml.c6i.16xlarge"
"ml.c6i.24xlarge"
"ml.c6i.32xlarge"
"ml.r6i.large"
"ml.r6i.xlarge"
"ml.r6i.2xlarge"
"ml.r6i.4xlarge"
"ml.r6i.8xlarge"
"ml.r6i.12xlarge"
"ml.r6i.16xlarge"
"ml.r6i.24xlarge"
"ml.r6i.32xlarge"
"ml.m7i.large"
"ml.m7i.xlarge"
"ml.m7i.2xlarge"
"ml.m7i.4xlarge"
"ml.m7i.8xlarge"
"ml.m7i.12xlarge"
"ml.m7i.16xlarge"
"ml.m7i.24xlarge"
"ml.m7i.48xlarge"
"ml.c7i.large"
"ml.c7i.xlarge"
"ml.c7i.2xlarge"
"ml.c7i.4xlarge"
"ml.c7i.8xlarge"
"ml.c7i.12xlarge"
"ml.c7i.16xlarge"
"ml.c7i.24xlarge"
"ml.c7i.48xlarge"
"ml.r7i.large"
"ml.r7i.xlarge"
"ml.r7i.2xlarge"
"ml.r7i.4xlarge"
"ml.r7i.8xlarge"
"ml.r7i.12xlarge"
"ml.r7i.16xlarge"
"ml.r7i.24xlarge"
"ml.r7i.48xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.12xlarge"
"ml.g5.16xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the transform job. The default value is
1
, and the maximum is100
. For distributed transform jobs, specify a value greater than1
.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.The
VolumeKmsKeyId
can be any of the following formats:-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
CreationTime
— (Date
)A timestamp that shows when the transform Job was created.
TransformStartTime
— (Date
)Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of
TransformEndTime
.TransformEndTime
— (Date
)Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of
TransformStartTime
.LabelingJobArn
— (String
)The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
AutoMLJobArn
— (String
)The Amazon Resource Name (ARN) of the AutoML transform job.
DataProcessing
— (map
)The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
InputFilter
— (String
)A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
InputFilter
parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value$
.Examples:
"$"
,"$[1:]"
,"$.features"
OutputFilter
— (String
)A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value,
$
. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.Examples:
"$"
,"$[0,5:]"
,"$['id','SageMakerOutput']"
JoinSource
— (String
)Specifies the source of the data to join with the transformed data. The valid values are
None
andInput
. The default value isNone
, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, setJoinSource
toInput
. You can specifyOutputFilter
as an additional filter to select a portion of the joined dataset and store it in the output file.For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called
SageMakerOutput
. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under theSageMakerInput
key and the results are stored inSageMakerOutput
.For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
Possible values include:"Input"
"None"
ExperimentConfig
— (map
)Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('processingJobCompletedOrStopped', params = {}, [callback]) ⇒ AWS.Request
Waits for the
processingJobCompletedOrStopped
state by periodically calling the underlying SageMaker.describeProcessingJob() operation every 60 seconds (at most 60 times).Examples:
Waiting for the processingJobCompletedOrStopped state
var params = { ProcessingJobName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('processingJobCompletedOrStopped', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
ProcessingJobName
— (String
)The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:ProcessingInputs
— (Array<map>
)The inputs for a processing job.
InputName
— required — (String
)The name for the processing job input.
AppManaged
— (Boolean
)When
True
, input operations such as data download are managed natively by the processing job application. WhenFalse
(default), input operations are managed by Amazon SageMaker.S3Input
— (map
)Configuration for downloading input data from Amazon S3 into the processing container.
S3Uri
— required — (String
)The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
LocalPath
— (String
)The local path in your container where you want Amazon SageMaker to write input data to.
LocalPath
is an absolute path to the input data and must begin with/opt/ml/processing/
.LocalPath
is a required parameter whenAppManaged
isFalse
(default).S3DataType
— required — (String
)Whether you use an
Possible values include:S3Prefix
or aManifestFile
for the data type. If you chooseS3Prefix
,S3Uri
identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you chooseManifestFile
,S3Uri
identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job."ManifestFile"
"S3Prefix"
S3InputMode
— (String
)Whether to use
Possible values include:File
orPipe
input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. InPipe
mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume."Pipe"
"File"
S3DataDistributionType
— (String
)Whether to distribute the data from Amazon S3 to all processing instances with
Possible values include:FullyReplicated
, or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance."FullyReplicated"
"ShardedByS3Key"
S3CompressionType
— (String
)Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container.
Possible values include:Gzip
can only be used whenPipe
mode is specified as theS3InputMode
. InPipe
mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume."None"
"Gzip"
DatasetDefinition
— (map
)Configuration for a Dataset Definition input.
AthenaDatasetDefinition
— (map
)Configuration for Athena Dataset Definition input.
Catalog
— required — (String
)The name of the data catalog used in Athena query execution.
Database
— required — (String
)The name of the database used in the Athena query execution.
QueryString
— required — (String
)The SQL query statements, to be executed.
WorkGroup
— (String
)The name of the workgroup in which the Athena query is being started.
OutputS3Uri
— required — (String
)The location in Amazon S3 where Athena query results are stored.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat
— required — (String
)The data storage format for Athena query results.
Possible values include:"PARQUET"
"ORC"
"AVRO"
"JSON"
"TEXTFILE"
OutputCompression
— (String
)The compression used for Athena query results.
Possible values include:"GZIP"
"SNAPPY"
"ZLIB"
RedshiftDatasetDefinition
— (map
)Configuration for Redshift Dataset Definition input.
ClusterId
— required — (String
)The Redshift cluster Identifier.
Database
— required — (String
)The name of the Redshift database used in Redshift query execution.
DbUser
— required — (String
)The database user name used in Redshift query execution.
QueryString
— required — (String
)The SQL query statements to be executed.
ClusterRoleArn
— required — (String
)The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri
— required — (String
)The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat
— required — (String
)The data storage format for Redshift query results.
Possible values include:"PARQUET"
"CSV"
OutputCompression
— (String
)The compression used for Redshift query results.
Possible values include:"None"
"GZIP"
"BZIP2"
"ZSTD"
"SNAPPY"
LocalPath
— (String
)The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job.
LocalPath
is an absolute path to the input data. This is a required parameter whenAppManaged
isFalse
(default).DataDistributionType
— (String
)Whether the generated dataset is
Possible values include:FullyReplicated
orShardedByS3Key
(default)."FullyReplicated"
"ShardedByS3Key"
InputMode
— (String
)Whether to use
Possible values include:File
orPipe
input mode. InFile
(default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. InPipe
mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume."Pipe"
"File"
ProcessingOutputConfig
— (map
)Output configuration for the processing job.
Outputs
— required — (Array<map>
)An array of outputs configuring the data to upload from the processing container.
OutputName
— required — (String
)The name for the processing job output.
S3Output
— (map
)Configuration for processing job outputs in Amazon S3.
S3Uri
— required — (String
)A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath
— (String
)The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3.
LocalPath
is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.S3UploadMode
— required — (String
)Whether to upload the results of the processing job continuously or after the job completes.
Possible values include:"Continuous"
"EndOfJob"
FeatureStoreOutput
— (map
)Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when
AppManaged
is specified.FeatureGroupName
— required — (String
)The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
AppManaged
— (Boolean
)When
True
, output operations such as data upload are managed natively by the processing job application. WhenFalse
(default), output operations are managed by Amazon SageMaker.
KmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output.
KmsKeyId
can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. TheKmsKeyId
is applied to all outputs.
ProcessingJobName
— (String
)The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ProcessingResources
— (map
)Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig
— required — (map
)The configuration for the resources in a cluster used to run the processing job.
InstanceCount
— required — (Integer
)The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType
— required — (String
)The ML compute instance type for the processing job.
Possible values include:"ml.t3.medium"
"ml.t3.large"
"ml.t3.xlarge"
"ml.t3.2xlarge"
"ml.m4.xlarge"
"ml.m4.2xlarge"
"ml.m4.4xlarge"
"ml.m4.10xlarge"
"ml.m4.16xlarge"
"ml.c4.xlarge"
"ml.c4.2xlarge"
"ml.c4.4xlarge"
"ml.c4.8xlarge"
"ml.p2.xlarge"
"ml.p2.8xlarge"
"ml.p2.16xlarge"
"ml.p3.2xlarge"
"ml.p3.8xlarge"
"ml.p3.16xlarge"
"ml.c5.xlarge"
"ml.c5.2xlarge"
"ml.c5.4xlarge"
"ml.c5.9xlarge"
"ml.c5.18xlarge"
"ml.m5.large"
"ml.m5.xlarge"
"ml.m5.2xlarge"
"ml.m5.4xlarge"
"ml.m5.12xlarge"
"ml.m5.24xlarge"
"ml.r5.large"
"ml.r5.xlarge"
"ml.r5.2xlarge"
"ml.r5.4xlarge"
"ml.r5.8xlarge"
"ml.r5.12xlarge"
"ml.r5.16xlarge"
"ml.r5.24xlarge"
"ml.g4dn.xlarge"
"ml.g4dn.2xlarge"
"ml.g4dn.4xlarge"
"ml.g4dn.8xlarge"
"ml.g4dn.12xlarge"
"ml.g4dn.16xlarge"
"ml.g5.xlarge"
"ml.g5.2xlarge"
"ml.g5.4xlarge"
"ml.g5.8xlarge"
"ml.g5.16xlarge"
"ml.g5.12xlarge"
"ml.g5.24xlarge"
"ml.g5.48xlarge"
"ml.r5d.large"
"ml.r5d.xlarge"
"ml.r5d.2xlarge"
"ml.r5d.4xlarge"
"ml.r5d.8xlarge"
"ml.r5d.12xlarge"
"ml.r5d.16xlarge"
"ml.r5d.24xlarge"
VolumeSizeInGB
— required — (Integer
)The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request aVolumeSizeInGB
greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.VolumeKmsKeyId
— (String
)The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request aVolumeKmsKeyId
when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.
StoppingCondition
— (map
)The time limit for how long the processing job is allowed to run.
MaxRuntimeInSeconds
— required — (Integer
)Specifies the maximum runtime in seconds.
AppSpecification
— (map
)Configures the processing job to run a specified container image.
ImageUri
— required — (String
)The container image to be run by the processing job.
ContainerEntrypoint
— (Array<String>
)The entrypoint for a container used to run a processing job.
ContainerArguments
— (Array<String>
)The arguments for a container used to run a processing job.
Environment
— (map<String>
)The environment variables set in the Docker container.
NetworkConfig
— (map
)Networking options for a processing job.
EnableInterContainerTrafficEncryption
— (Boolean
)Whether to encrypt all communications between distributed processing jobs. Choose
True
to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.EnableNetworkIsolation
— (Boolean
)Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig
— (map
)Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
SecurityGroupIds
— required — (Array<String>
)The VPC security group IDs, in the form
sg-xxxxxxxx
. Specify the security groups for the VPC that is specified in theSubnets
field.Subnets
— required — (Array<String>
)The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
RoleArn
— (String
)The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
ExperimentConfig
— (map
)The configuration information used to create an experiment.
ExperimentName
— (String
)The name of an existing experiment to associate with the trial component.
TrialName
— (String
)The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName
— (String
)The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName
— (String
)The name of the experiment run to associate with the trial component.
ProcessingJobArn
— (String
)The Amazon Resource Name (ARN) of the processing job.
ProcessingJobStatus
— (String
)Provides the status of a processing job.
Possible values include:"InProgress"
"Completed"
"Failed"
"Stopping"
"Stopped"
ExitMessage
— (String
)An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
FailureReason
— (String
)A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ProcessingEndTime
— (Date
)The time at which the processing job completed.
ProcessingStartTime
— (Date
)The time at which the processing job started.
LastModifiedTime
— (Date
)The time at which the processing job was last modified.
CreationTime
— (Date
)The time at which the processing job was created.
MonitoringScheduleArn
— (String
)The ARN of a monitoring schedule for an endpoint associated with this processing job.
AutoMLJobArn
— (String
)The ARN of an AutoML job associated with this processing job.
TrainingJobArn
— (String
)The ARN of a training job associated with this processing job.
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('imageCreated', params = {}, [callback]) ⇒ AWS.Request
Waits for the
imageCreated
state by periodically calling the underlying SageMaker.describeImage() operation every 60 seconds (at most 60 times).Examples:
Waiting for the imageCreated state
var params = { ImageName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('imageCreated', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
ImageName
— (String
)The name of the image to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CreationTime
— (Date
)When the image was created.
Description
— (String
)The description of the image.
DisplayName
— (String
)The name of the image as displayed.
FailureReason
— (String
)When a create, update, or delete operation fails, the reason for the failure.
ImageArn
— (String
)The ARN of the image.
ImageName
— (String
)The name of the image.
ImageStatus
— (String
)The status of the image.
Possible values include:"CREATING"
"CREATED"
"CREATE_FAILED"
"UPDATING"
"UPDATE_FAILED"
"DELETING"
"DELETE_FAILED"
LastModifiedTime
— (Date
)When the image was last modified.
RoleArn
— (String
)The ARN of the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('imageUpdated', params = {}, [callback]) ⇒ AWS.Request
Waits for the
imageUpdated
state by periodically calling the underlying SageMaker.describeImage() operation every 60 seconds (at most 60 times).Examples:
Waiting for the imageUpdated state
var params = { ImageName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('imageUpdated', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
ImageName
— (String
)The name of the image to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CreationTime
— (Date
)When the image was created.
Description
— (String
)The description of the image.
DisplayName
— (String
)The name of the image as displayed.
FailureReason
— (String
)When a create, update, or delete operation fails, the reason for the failure.
ImageArn
— (String
)The ARN of the image.
ImageName
— (String
)The name of the image.
ImageStatus
— (String
)The status of the image.
Possible values include:"CREATING"
"CREATED"
"CREATE_FAILED"
"UPDATING"
"UPDATE_FAILED"
"DELETING"
"DELETE_FAILED"
LastModifiedTime
— (Date
)When the image was last modified.
RoleArn
— (String
)The ARN of the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('imageDeleted', params = {}, [callback]) ⇒ AWS.Request
Waits for the
imageDeleted
state by periodically calling the underlying SageMaker.describeImage() operation every 60 seconds (at most 60 times).Examples:
Waiting for the imageDeleted state
var params = { ImageName: 'STRING_VALUE' /* required */ }; sagemaker.waitFor('imageDeleted', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
ImageName
— (String
)The name of the image to describe.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:CreationTime
— (Date
)When the image was created.
Description
— (String
)The description of the image.
DisplayName
— (String
)The name of the image as displayed.
FailureReason
— (String
)When a create, update, or delete operation fails, the reason for the failure.
ImageArn
— (String
)The ARN of the image.
ImageName
— (String
)The name of the image.
ImageStatus
— (String
)The status of the image.
Possible values include:"CREATING"
"CREATED"
"CREATE_FAILED"
"UPDATING"
"UPDATE_FAILED"
"DELETING"
"DELETE_FAILED"
LastModifiedTime
— (Date
)When the image was last modified.
RoleArn
— (String
)The ARN of the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('imageVersionCreated', params = {}, [callback]) ⇒ AWS.Request
Waits for the
imageVersionCreated
state by periodically calling the underlying SageMaker.describeImageVersion() operation every 60 seconds (at most 60 times).Examples:
Waiting for the imageVersionCreated state
var params = { ImageName: 'STRING_VALUE', /* required */ }; sagemaker.waitFor('imageVersionCreated', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
ImageName
— (String
)The name of the image.
Version
— (Integer
)The version of the image. If not specified, the latest version is described.
Alias
— (String
)The alias of the image version.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:BaseImage
— (String
)The registry path of the container image on which this image version is based.
ContainerImage
— (String
)The registry path of the container image that contains this image version.
CreationTime
— (Date
)When the version was created.
FailureReason
— (String
)When a create or delete operation fails, the reason for the failure.
ImageArn
— (String
)The ARN of the image the version is based on.
ImageVersionArn
— (String
)The ARN of the version.
ImageVersionStatus
— (String
)The status of the version.
Possible values include:"CREATING"
"CREATED"
"CREATE_FAILED"
"DELETING"
"DELETE_FAILED"
LastModifiedTime
— (Date
)When the version was last modified.
Version
— (Integer
)The version number.
VendorGuidance
— (String
)The stability of the image version specified by the maintainer.
-
NOT_PROVIDED
: The maintainers did not provide a status for image version stability. -
STABLE
: The image version is stable. -
TO_BE_ARCHIVED
: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months. -
ARCHIVED
: The image version is archived. Archived image versions are not searchable and are no longer actively supported.
"NOT_PROVIDED"
"STABLE"
"TO_BE_ARCHIVED"
"ARCHIVED"
-
JobType
— (String
)Indicates SageMaker job type compatibility.
-
TRAINING
: The image version is compatible with SageMaker training jobs. -
INFERENCE
: The image version is compatible with SageMaker inference jobs. -
NOTEBOOK_KERNEL
: The image version is compatible with SageMaker notebook kernels.
"TRAINING"
"INFERENCE"
"NOTEBOOK_KERNEL"
-
MLFramework
— (String
)The machine learning framework vended in the image version.
ProgrammingLang
— (String
)The supported programming language and its version.
Processor
— (String
)Indicates CPU or GPU compatibility.
-
CPU
: The image version is compatible with CPU. -
GPU
: The image version is compatible with GPU.
"CPU"
"GPU"
-
Horovod
— (Boolean
)Indicates Horovod compatibility.
ReleaseNotes
— (String
)The maintainer description of the image version.
-
(AWS.Response)
—
Returns:
See Also:
sagemaker.waitFor('imageVersionDeleted', params = {}, [callback]) ⇒ AWS.Request
Waits for the
imageVersionDeleted
state by periodically calling the underlying SageMaker.describeImageVersion() operation every 60 seconds (at most 60 times).Examples:
Waiting for the imageVersionDeleted state
var params = { ImageName: 'STRING_VALUE', /* required */ }; sagemaker.waitFor('imageVersionDeleted', params, function(err, data) { if (err) console.log(err, err.stack); // an error occurred else console.log(data); // successful response });
Parameters:
-
params
(Object)
—
ImageName
— (String
)The name of the image.
Version
— (Integer
)The version of the image. If not specified, the latest version is described.
Alias
— (String
)The alias of the image version.
Callback (callback):
-
function(err, data) { ... }
Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.
Context (this):
-
(AWS.Response)
—
the response object containing error, data properties, and the original request object.
Parameters:
-
err
(Error)
—
the error object returned from the request. Set to
null
if the request is successful. -
data
(Object)
—
the de-serialized data returned from the request. Set to
null
if a request error occurs. Thedata
object has the following properties:BaseImage
— (String
)The registry path of the container image on which this image version is based.
ContainerImage
— (String
)The registry path of the container image that contains this image version.
CreationTime
— (Date
)When the version was created.
FailureReason
— (String
)When a create or delete operation fails, the reason for the failure.
ImageArn
— (String
)The ARN of the image the version is based on.
ImageVersionArn
— (String
)The ARN of the version.
ImageVersionStatus
— (String
)The status of the version.
Possible values include:"CREATING"
"CREATED"
"CREATE_FAILED"
"DELETING"
"DELETE_FAILED"
LastModifiedTime
— (Date
)When the version was last modified.
Version
— (Integer
)The version number.
VendorGuidance
— (String
)The stability of the image version specified by the maintainer.
-
NOT_PROVIDED
: The maintainers did not provide a status for image version stability. -
STABLE
: The image version is stable. -
TO_BE_ARCHIVED
: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months. -
ARCHIVED
: The image version is archived. Archived image versions are not searchable and are no longer actively supported.
"NOT_PROVIDED"
"STABLE"
"TO_BE_ARCHIVED"
"ARCHIVED"
-
JobType
— (String
)Indicates SageMaker job type compatibility.
-
TRAINING
: The image version is compatible with SageMaker training jobs. -
INFERENCE
: The image version is compatible with SageMaker inference jobs. -
NOTEBOOK_KERNEL
: The image version is compatible with SageMaker notebook kernels.
"TRAINING"
"INFERENCE"
"NOTEBOOK_KERNEL"
-
MLFramework
— (String
)The machine learning framework vended in the image version.
ProgrammingLang
— (String
)The supported programming language and its version.
Processor
— (String
)Indicates CPU or GPU compatibility.
-
CPU
: The image version is compatible with CPU. -
GPU
: The image version is compatible with GPU.
"CPU"
"GPU"
-
Horovod
— (Boolean
)Indicates Horovod compatibility.
ReleaseNotes
— (String
)The maintainer description of the image version.
-
(AWS.Response)
—
Returns:
See Also:
Generated on Wed Nov 6 18:51:49 2024 by yard 0.9.36 (ruby-2.5.1). - deleteMlflowTrackingServer(params = {}, callback) ⇒ AWS.Request