JAX

JAX

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Introduction
JAX is a Python library for high-performance array computing and program transformation, designed for numerical computing and machine learning.
Listed on
July 22, 2026
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What is 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. It provides a familiar NumPy-style API and includes composable function transformations for compilation, batching, automatic differentiation, and parallelization. The same code can run on multiple backends, including CPU, GPU, and TPU.

How to use JAX?

  • Install JAX via pip or conda, then import jax and jax.numpy. Use jax.jit for just-in-time compilation, jax.vmap for automatic vectorization, jax.grad for automatic differentiation, and jax.pmap for parallelization. Refer to the official documentation for tutorials and API reference.

Core features of JAX

  • Familiar NumPy-style API
  • Composable function transformations (jit, vmap, grad, pmap)
  • Automatic differentiation (forward and reverse mode)
  • Just-in-time compilation
  • Automatic vectorization
  • Parallelization across multiple devices
  • Support for CPU, GPU, and TPU backends

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