Audience

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

About JAX

​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. ​

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Integrations

API:
Yes, JAX offers API access
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Ratings/Reviews

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Company Information

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

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Product Details

Platforms Supported
Windows
Mac
Linux
Training
Documentation
Support
Online

JAX Frequently Asked Questions

Q: What kinds of users and organization types does JAX work with?
Q: What languages does JAX support in their product?
Q: What kind of support options does JAX offer?
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Q: Does JAX have an API?
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JAX Product Features