JAX: High performance array computing

High performance array computing

JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.

Familiar API

JAX provides a familiar NumPy-style API for ease of adoption by researchers and engineers.

Transformations

JAX includes composable function transformations for compilation, batching, automatic differentiation, and parallelization.

Run anywhere

The same code executes on multiple backends, including CPU, GPU, & TPU

Installation

Installation

Getting started

Getting Started with JAX

User guides

User guides

If you’re looking to train neural networks, use Flax and start with its tutorials. For an end-to-end transformer library built on JAX, see MaxText.

Ecosystem

JAX itself is narrowly-scoped and focuses on efficient array operations & program transformations. Built around JAX is an evolving ecosystem of machine learning and numerical computing tools; the following is just a small sample of what is out there:

Neural networks

• Flax

• Equinox

• Keras

Optimizers & solvers

• Optax

• Optimistix

• Lineax

• Diffrax

Data loading

• Grain

• TensorFlow Datasets

• Hugging Face Datasets

Miscellaneous tools

• Orbax

• Chex

Probabilistic programming

• Blackjax

• Numpyro

• PyMC

Probabilistic modeling

• TensorFlow Probabilty

• Distrax

Physics & simulation

• JAX MD

• Brax

LLMs

• MaxText

• AXLearn

• Levanter

• EasyLM

Many more JAX-based libraries have been developed; the community-run Awesome JAX page maintains an up-to-date list.