JAX: Python Computing Revolution with JIT & GPU/TPU Support
Discover JAX: Google's open-source library for high-performance Python computing with JIT compilation, auto-diff, and GPU/TPU support.
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Recently, I've been tinkering with Python high-performance computing. Want to make your NumPy code run faster and more flexibly? Have you heard of JAX?
It's not just a simple acceleration library—it masters automatic differentiation, JIT compilation, batch vectorization, and can even deploy to TPU and GPU with one click. Today, let's talk about this "black technology" that will surely give you plenty to gain.
What is JAX
In simple terms, JAX is Google's open-source "high-performance numerical computing + program transformation" tool.
Supports automatic differentiation of native Python/NumPy functions (grad, forward-mode, reverse-mode in any combination)
Based on the XLA compilation backend, one-click acceleration to GPU/TPU (using
jax.jit)Batch vectorization without writing loops (
jax.vmap)Scalable, composable, and a research powerhouse
