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![logo](https://raw.githubusercontent.com/google/jax/master/images/jax_logo_250px.png)
[JAX](http://go/jax) is [Autograd](https://github.com/hips/autograd) and
JAX is [Autograd](https://github.com/hips/autograd) and
[XLA](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/g3doc/overview.md),
brought together for high-performance machine learning research.
With its updated version of [Autograd](https://github.com/hips/autograd), JAX
can automatically differentiate native Python and NumPy code. It can
differentiate through a large subset of Python’s features, including loops,
ifs, recursion, and closures, and it can even take derivatives of derivatives
of derivatives. It supports reverse-mode differentiation (a.k.a.
backpropagation) as well as forward-mode differentiation, and the two can be
composed arbitrarily to any order.
With its updated version of Autograd, JAX can automatically differentiate native
Python and NumPy functions. It can differentiate through loops, branches,
recursion, and closures, and it can take derivatives of derivatives of
derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation)
as well as forward-mode differentiation, and the two can be composed arbitrarily
to any order.
What’s new is that JAX uses
[XLA](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/g3doc/overview.md)
to compile and run your NumPy code on accelerators, like GPUs and TPUs.
Compilation happens under the hood by default, with library calls getting
just-in-time compiled and executed. But JAX even lets you just-in-time compile
your own Python functions into XLA-optimized kernels using a one-function API.
Compilation and automatic differentiation can be composed arbitrarily, so you
can express sophisticated algorithms and get maximal performance without having
to leave Python.
to compile and run your NumPy functions on GPUs and TPUs. Compilation happens
under the hood by default, with library calls getting just-in-time compiled and
executed. But JAX also lets you just-in-time compile your own Python functions
into XLA-optimized kernels using a one-function API,
[`jit`](#compilation-with-jit). Compilation and automatic differentiation can be
composed arbitrarily, so you can express sophisticated algorithms and get
maximal performance without leaving Python.
This is a research project, not an official Google product. Expect bugs and
sharp edges. Please help by trying it out, [reporting