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How to Think in JAX — JAX documentation

jax.readthedocs.io · 2,991 words · saved by 1 readers

JAX provides a simple and powerful API for writing accelerated numerical code, but working effectively in JAX sometimes requires extra consideration. This document is meant to help build a ground-up understanding of how JAX operates, so that you can use it more effectively. Key Concepts: JAX provides a NumPy-inspired interface for convenience. Through duck-typing, JAX arrays can often be used as drop-in replacements of NumPy arrays. Unlike NumPy arrays, JAX arrays are always immutable. NumPy provides a well-known, powerful API for working with numerical data. For convenience, JAX provides jax.numpy which closely mirrors the numpy API and provides easy entry into JAX. Almost anything that can be done with numpy can be done with jax.numpy: The code blocks are identical aside from replacing np with jnp, and the results are the same. As we can see, JAX arrays can often be used directly in place of NumPy arrays for things like plotting. The arrays themselves are implemented as different Pyt

Quickstart: How to think in JAX # JAX is a library for array-oriented numerical computation ( à la NumPy ), with automatic differentiation and JIT compilation to enable high-performance machine learning research . This document provides a quick overview of essential JAX features, so you can get started with JAX: 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 functions support efficient evaluation

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