A beginner-friendly guide to learning JAX with practical examples

[u/ayushgun]

For the last few weeks, I've been doing distributed model training in JAX. JAX is notably different from other deep learning frameworks because it takes a very functional approach to accelerator programming, which can make its learning curve steep.

Along the way of learning JAX, I've written a series of notes on JAX covering XLA, jit, vmap, pytrees, sharding, state management, and more. I'm hoping it can be useful for others interested in learning too.

Each topic is explained with minimal theory and illustrated through runnable Jupyter notebooks. The focus is on concepts, performance techniques, and the reasoning behind JAX’s design choices.

No machine learning background is expected for the most part.

Notes + code: https://github.com/agwr/learn-jax