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This document provides a quick overview of essential JAX features, so you can get started with JAX quickly: JAX provides a unified NumPy-like interface to computations that run on CPU, GPU, or TPU, in local or distributed settings. JAX features built-in Just-In-Time (JIT) compilation via Open XLA, an open-source machine learning compiler ecosystem.
JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. With its updated version of Autograd , JAX can automatically differentiate native Python and NumPy functions.
Google JAX is a machine learning framework for transforming numerical functions to be used in Python. [2] [3] [4] It is described as bringing together a modified version of autograd [5] (automatic obtaining of the gradient function through differentiation of a function) and TensorFlow 's XLA (Accelerated Linear Algebra).
Using JAX requires installing two packages: jax, which is pure Python and cross-platform, and jaxlib which contains compiled binaries, and requires different builds for different operating systems and accelerators. TL;DR For most users, a typical JAX installation may look something like this: CPU-only (Linux/macOS/Windows) pip install -U jax.
JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. If you’re looking to train neural networks, use Flax and start with its documentation.
Jun 18, 2024 · JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. With its updated version of Autograd , JAX can automatically differentiate native Python and NumPy functions.
JAX is written in pure Python, but it depends on XLA, which needs to be installed as the jaxlib package. Use the following instructions to install a binary package with pip or conda, to use a Docker container, or to build JAX from source. Supported platforms #
JAX - The Sharp Bits. jit changes the exact numerics of outputs # Sometimes users are surprised by the fact that wrapping a function with jit() can change the function’s outputs. For example: >>> from jax import jit >>> import jax.numpy as jnp >>> def f(x): ... return jnp.log(jnp.sqrt(x)) >>> x = jnp.pi >>> print(f(x)) 0.572365.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more - Releases · google/jax.
Feb 20, 2023 · As deep learning practitioners, it can be tough to keep up with all the new developments. New academic papers and models are always coming out; there’s a new framework to learn every few years. Recently, many people have been talking about JAX, a new numerical computing library that can make your code run faster.
