8 Advanced parallelization - Deep Learning with JAX

Using easy-to-revise parallelism with xmap() · Compiling and automatically partitioning functions with pjit() · Using tensor sharding to achieve parallelization with XLA · Running code in multi-host configurations

Model Parallelism

Frontiers Tensor Processing Primitives: A Programming

How to Think in JAX — JAX documentation

Grigory Sapunov on LinkedIn: Deep Learning with JAX

What is Google JAX? Everything You Need to Know - Geekflare

Learn JAX in 2023: Part 2 - grad, jit, vmap, and pmap

Lecture 6: MLOps Infrastructure & Tooling - The Full Stack

Why You Should (or Shouldn't) be Using Google's JAX in 2023

Lecture 2: Development Infrastructure & Tooling - The Full Stack

Introducing Neuropod, Uber ATG's Open Source Deep Learning