Package-level declarations
Functions
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.
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.
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.
Deletes specific nodes within a SageMaker HyperPod cluster. BatchDeleteClusterNodes accepts a cluster name and a list of node IDs.
This action batch describes a list of versioned model packages
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.
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker AI upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
Creates a configuration for running a SageMaker AI 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.
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.
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
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.
Create cluster policy configuration. This policy is used for task prioritization and fair-share allocation of idle compute. This helps prioritize critical workloads and distributes idle compute across entities.
Creates a Git repository as a resource in your SageMaker AI 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 AI 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.
Starts a model compilation job. After the model has been compiled, Amazon SageMaker AI saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
Create compute allocation definition. This defines how compute is allocated, shared, and borrowed for specified entities. Specifically, how to lend and borrow idle compute and assign a fair-share weight to the specified entities.
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.
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.
Creates a device fleet.
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.
Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
Creates a new stage in an existing edge deployment plan.
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.
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.
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.
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.
Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.
Creates a flow definition.
Create a hub.
Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
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.
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.
Creates a custom SageMaker AI image. A SageMaker AI 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 AI image.
Creates a version of the SageMaker AI image specified by ImageName. The version represents the Amazon ECR container image specified by BaseImage.
Creates an inference component, which is a SageMaker AI 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.
Creates an inference experiment using the configurations specified in the request.
Starts a recommendation job. You can create either an instance recommendation or load test job.
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.
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.
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.
Creates the definition for a model bias job.
Creates an Amazon SageMaker Model Card.
Creates an Amazon SageMaker Model Card export job.
Creates the definition for a model explainability job.
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.
Creates a model group. A model group contains a group of model versions.
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.
Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint.
Creates an SageMaker AI notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
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.
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.
Creates an Amazon SageMaker Partner AI App.
Creates a presigned URL to access an Amazon SageMaker Partner AI App.
Creates a pipeline using a JSON pipeline definition.
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.
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.
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker AI console, when you choose Open next to a notebook instance, SageMaker AI 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.
Creates a processing job.
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.
Creates a private space or a space used for real time collaboration in a domain.
Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.
Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
Creates a new training plan in SageMaker to reserve compute capacity.
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.
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.
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.
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.
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.
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.
Deletes an action.
Removes the specified algorithm from your account.
Used to stop and delete an app.
Deletes an AppImageConfig.
Deletes an artifact. Either ArtifactArn or Source must be specified.
Deletes an association.
Delete a SageMaker HyperPod cluster.
Deletes the cluster policy of the cluster.
Deletes the specified Git repository from your account.
Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker AI. 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.
Deletes the compute allocation from the cluster.
Deletes an context.
Deletes a data quality monitoring job definition.
Deletes a fleet.
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.
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.
Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.
Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.
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.
Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.
Deletes the specified flow definition.
Delete a hub.
Delete the contents of a hub.
Delete a hub content reference in order to remove a model from a private hub.
Use this operation to delete a human task user interface (worker task template).
Deletes a hyperparameter tuning job. The DeleteHyperParameterTuningJob API deletes only the tuning job entry that was created in SageMaker when you called the CreateHyperParameterTuningJob API. It does not delete training jobs, artifacts, or the IAM role that you specified when creating the model.
Deletes a SageMaker AI image and all versions of the image. The container images aren't deleted.
Deletes a version of a SageMaker AI image. The container image the version represents isn't deleted.
Deletes an inference component.
Deletes an inference experiment.
Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
Deletes a model. The DeleteModel API deletes only the model entry that was created in SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.
Deletes an Amazon SageMaker AI model bias job definition.
Deletes an Amazon SageMaker Model Card.
Deletes an Amazon SageMaker AI model explainability job definition.
Deletes a model package.
Deletes the specified model group.
Deletes a model group resource policy.
Deletes the secified model quality monitoring job definition.
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.
Deletes an SageMaker AI notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.
Deletes a notebook instance lifecycle configuration.
Deletes an optimization job.
Deletes a SageMaker Partner AI App.
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.
Delete the specified project.
Used to delete a space.
Deletes the Amazon SageMaker AI 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.
Deletes the specified tags from an SageMaker resource.
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.
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.
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
Use this operation to delete a workforce.
Deletes an existing work team. This operation can't be undone.
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
Describes an action.
Returns a description of the specified algorithm that is in your account.
Describes the app.
Describes an AppImageConfig.
Describes an artifact.
Returns information about an AutoML job created by calling CreateAutoMLJob.
Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
Retrieves information of a SageMaker HyperPod cluster.
Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
Description of the cluster policy. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities.
Gets details about the specified Git repository.
Returns information about a model compilation job.
Description of the compute allocation definition.
Describes a context.
Gets the details of a data quality monitoring job definition.
Describes the device.
A description of the fleet the device belongs to.
The description of the domain.
Describes an edge deployment plan with deployment status per stage.
A description of edge packaging jobs.
Returns the description of an endpoint.
Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
Provides a list of an experiment's properties.
Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.
Shows the metadata for a feature within a feature group.
Returns information about the specified flow definition.
Describes a hub.
Describe the content of a hub.
Returns information about the requested human task user interface (worker task template).
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.
Describes a SageMaker AI image.
Describes a version of a SageMaker AI image.
Returns information about an inference component.
Returns details about an inference experiment.
Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
Gets information about a labeling job.
Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
Returns information about an MLflow Tracking Server.
Describes a model that you created using the CreateModel API.
Returns a description of a model bias job definition.
Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
Describes an Amazon SageMaker Model Card export job.
Returns a description of a model explainability job definition.
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
Gets a description for the specified model group.
Returns a description of a model quality job definition.
Describes the schedule for a monitoring job.
Returns information about a notebook instance.
Returns a description of a notebook instance lifecycle configuration.
Provides the properties of the specified optimization job.
Gets information about a SageMaker Partner AI App.
Describes the details of a pipeline.
Describes the details of an execution's pipeline definition.
Describes the details of a pipeline execution.
Returns a description of a processing job.
Describes the details of a project.
Describes the space.
Describes the Amazon SageMaker AI Studio Lifecycle Configuration.
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.
Returns information about a training job.
Retrieves detailed information about a specific training plan.
Returns information about a transform job.
Provides a list of a trial's properties.
Provides a list of a trials component's properties.
Describes a user profile. For more information, see CreateUserProfile.
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.
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).
Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
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.
Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
Describes a fleet.
The resource policy for the lineage group.
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..
Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.
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 for HyperParameters, Tags, and Metrics.
Import hub content.
Lists the actions in your account and their properties.
Lists the machine learning algorithms that have been created.
Lists the aliases of a specified image or image version.
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.
Lists apps.
Lists the artifacts in your account and their properties.
Lists the associations in your account and their properties.
Request a list of jobs.
List the candidates created for the job.
Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
Retrieves the list of SageMaker HyperPod clusters.
List the cluster policy configurations.
Gets a list of the Git repositories in your account.
Lists model compilation jobs that satisfy various filters.
List the resource allocation definitions.
Lists the contexts in your account and their properties.
Lists the data quality job definitions in your account.
Returns a list of devices in the fleet.
A list of devices.
Lists the domains.
Lists all edge deployment plans.
Returns a list of edge packaging jobs.
Lists endpoint configurations.
Lists endpoints.
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.
List FeatureGroups based on given filter and order.
Returns information about the flow definitions in your account.
List the contents of a hub.
List hub content versions.
List all existing hubs.
Returns information about the human task user interfaces in your account.
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
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.
Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
Lists the inference components in your account and their properties.
Returns the list of all inference experiments.
Lists recommendation jobs that satisfy various filters.
Returns a list of the subtasks for an Inference Recommender job.
Gets a list of labeling jobs.
Gets a list of labeling jobs assigned to a specified work team.
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.
Lists all MLflow Tracking Servers.
Lists model bias jobs definitions that satisfy various filters.
List the export jobs for the Amazon SageMaker Model Card.
List existing model cards.
List existing versions of an Amazon SageMaker Model Card.
Lists model explainability job definitions that satisfy various filters.
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
Gets a list of the model groups in your Amazon Web Services account.
Lists the model packages that have been created.
Gets a list of model quality monitoring job definitions in your account.
Lists models created with the CreateModel API.
Gets a list of past alerts in a model monitoring schedule.
Gets the alerts for a single monitoring schedule.
Returns list of all monitoring job executions.
Returns list of all monitoring schedules.
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
Returns a list of the SageMaker AI notebook instances in the requester's account in an Amazon Web Services Region.
Lists the optimization jobs in your account and their properties.
Lists all of the SageMaker Partner AI Apps in an account.
Gets a list of the pipeline executions.
Gets a list of PipeLineExecutionStep objects.
Gets a list of parameters for a pipeline execution.
Gets a list of pipelines.
Lists processing jobs that satisfy various filters.
Gets a list of the projects in an Amazon Web Services account.
Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of ResourceCatalogs viewable is 1000.
Lists spaces.
Lists devices allocated to the stage, containing detailed device information and deployment status.
Lists the Amazon SageMaker AI Studio Lifecycle Configurations in your Amazon Web Services Account.
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.
Returns the tags for the specified SageMaker resource.
Lists training jobs.
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
Retrieves a list of training plans for the current account.
Lists transform jobs.
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:
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.
Lists user profiles.
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.
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.
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..
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.
Register devices.
Renders the UI template so that you can preview the worker's experience.
Retry the execution of the pipeline.
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.
Searches for available training plan offerings based on specified criteria.
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).
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).
Starts a stage in an edge deployment plan.
Starts an inference experiment.
Programmatically start an MLflow Tracking Server.
Starts a previously stopped monitoring schedule.
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker AI sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.
Starts a pipeline execution.
A method for forcing a running job to shut down.
Stops a model compilation job.
Stops a stage in an edge deployment plan.
Request to stop an edge packaging job.
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
Stops an inference experiment.
Stops an Inference Recommender job.
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.
Programmatically stop an MLflow Tracking Server.
Stops a previously started monitoring schedule.
Terminates the ML compute instance. Before terminating the instance, SageMaker AI disconnects the ML storage volume from it. SageMaker AI preserves the ML storage volume. SageMaker AI stops charging you for the ML compute instance when you call StopNotebookInstance.
Ends a running inference optimization job.
Stops a pipeline execution.
Stops a processing job.
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.
Stops a batch transform job.
Updates an action.
Updates the properties of an AppImageConfig.
Updates an artifact.
Updates a SageMaker HyperPod cluster.
Update the cluster policy configuration.
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.
Updates the specified Git repository with the specified values.
Update the compute allocation definition.
Updates a context.
Updates a fleet of devices.
Updates one or more devices in a fleet.
Updates the default settings for new user profiles in the domain.
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 previous EndpointConfig (there is no availability loss). For more information about how to control the update and traffic shifting process, see Update models in production.
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 to InService. To check the status of an endpoint, use the DescribeEndpoint API.
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
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.
Updates the description and parameters of the feature group.
Update a hub.
Updates SageMaker hub content (either a Model or Notebook resource).
Updates the contents of a SageMaker hub for a ModelReference resource. A ModelReference allows you to access public SageMaker JumpStart models from within your private hub.
Updates the properties of a SageMaker AI image. To change the image's tags, use the AddTags and DeleteTags APIs.
Updates the properties of a SageMaker AI image version.
Updates an inference component.
Runtime settings for a model that is deployed with an inference component.
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.
Updates properties of an existing MLflow Tracking Server.
Update an Amazon SageMaker Model Card.
Updates a versioned model.
Update the parameters of a model monitor alert.
Updates a previously created schedule.
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.
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
Updates all of the SageMaker Partner AI Apps in an account.
Updates a pipeline.
Updates a pipeline execution.
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.
Updates the settings of a space.
Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
Updates the display name of a trial.
Updates one or more properties of a trial component.
Updates a user profile.
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.
Updates an existing work team with new member definitions or description.
Create a copy of the client with one or more configuration values overridden. This method allows the caller to perform scoped config overrides for one or more client operations.