About

​JAX is a Python library designed for high-performance numerical computing and machine learning research. It offers a NumPy-like API, facilitating seamless adoption for those familiar with NumPy. Key features of JAX include automatic differentiation, just-in-time compilation, vectorization, and parallelization, all optimized for execution on CPUs, GPUs, and TPUs. These capabilities enable efficient computation for complex mathematical functions and large-scale machine-learning models. JAX also integrates with various libraries within its ecosystem, such as Flax for neural networks and Optax for optimization tasks. Comprehensive documentation, including tutorials and user guides, is available to assist users in leveraging JAX's full potential. ​

About

Use our generator to create a test script in JavaScript or CoffeeScript. Write script to set the request parameters and validate the response. Ping-API will run your test script on global servers in U.S., Japan, Germany and Singapore. Schedule testing to inspect your APIs. We will send the failure test information to you with email, Slack and HipChat. Ping-API allows you to write test script in JavaScript and CoffeeScript to test your APIs. Write script to set request url parameters, headers and body. And write script to validate response headers and body. Script generator. Don't worry about programming. Just set parameter of your API, the generator will give you test script. It is easy! The use case for web developers. Give me a notification when my web is down or the response is unexpected. Ping-API will schedule your tests in every minutes or hours. If the test is failure, we will send the notification to you.

About

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Professional researchers and developers searching for a solution to manage their numerical computing and machine learning operations in Python

Audience

Development teams or inviduals who want an API testing solution

Audience

Researchers in need of an open source machine learning solution to accelerate research prototyping and production deployment

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

$5 per month
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Review this Software

Reviews/Ratings

Overall 5.0 / 5
ease 1.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

JAX
United States
docs.jax.dev/en/latest/

Company Information

Ping-API
ping-api.com

Company Information

PyTorch
Founded: 2016
pytorch.org

Company Information

scikit-learn
United States
scikit-learn.org/stable/

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Replicate

Categories

Categories

Categories

Categories

Integrations

Akira AI
Amazon EC2 Capacity Blocks for ML
Amazon EC2 Trn1 Instances
Amazon EC2 UltraClusters
Bayesforge
Cleanlab
Comet LLM
Flyte
GPUonCLOUD
Horovod
IREN Cloud
MegaETH
NVIDIA AI Foundations
OpenVINO
Runyour AI
Superwise
TensorWave
Unremot
Voxel51
io.net

Integrations

Akira AI
Amazon EC2 Capacity Blocks for ML
Amazon EC2 Trn1 Instances
Amazon EC2 UltraClusters
Bayesforge
Cleanlab
Comet LLM
Flyte
GPUonCLOUD
Horovod
IREN Cloud
MegaETH
NVIDIA AI Foundations
OpenVINO
Runyour AI
Superwise
TensorWave
Unremot
Voxel51
io.net

Integrations

Akira AI
Amazon EC2 Capacity Blocks for ML
Amazon EC2 Trn1 Instances
Amazon EC2 UltraClusters
Bayesforge
Cleanlab
Comet LLM
Flyte
GPUonCLOUD
Horovod
IREN Cloud
MegaETH
NVIDIA AI Foundations
OpenVINO
Runyour AI
Superwise
TensorWave
Unremot
Voxel51
io.net

Integrations

Akira AI
Amazon EC2 Capacity Blocks for ML
Amazon EC2 Trn1 Instances
Amazon EC2 UltraClusters
Bayesforge
Cleanlab
Comet LLM
Flyte
GPUonCLOUD
Horovod
IREN Cloud
MegaETH
NVIDIA AI Foundations
OpenVINO
Runyour AI
Superwise
TensorWave
Unremot
Voxel51
io.net
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