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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

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

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

Audience

Component Library solution for DevOps teams

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Pricing

Free
Free Version Supported
Free Trial Not Supported

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

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 Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

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

Company Information

NumPy
numpy.org

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Alternatives

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5.0 out of 5 stars
Gensim

Gensim

Radim Řehůřek

Categories

Categories

Integrations

Flower Supported
3LC Not Supported
AWS EC2 Trn3 Instances Supported
Avanzai Not Supported
Cython Not Supported
Equinox Supported
Gemma 3n Supported
Gensim Not Supported
Grain Supported
JAX Not Supported
Keras Supported
LiteRT Supported
NumPy Supported
PaizaCloud Not Supported
TensorFlow Supported
Thunder Compute Supported
Train in Data Not Supported
Yamak.ai Not Supported
imageio Not Supported
scikit-learn Not Supported

Integrations

Flower Supported
3LC Supported
AWS EC2 Trn3 Instances Not Supported
Avanzai Supported
Cython Supported
Equinox Not Supported
Gemma 3n Not Supported
Gensim Supported
Grain Not Supported
JAX Supported
Keras Not Supported
LiteRT Not Supported
NumPy Not Supported
PaizaCloud Supported
TensorFlow Not Supported
Thunder Compute Not Supported
Train in Data Supported
Yamak.ai Supported
imageio Supported
scikit-learn Supported
Claim JAX and update features and information
Claim JAX and update features and information
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