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

Vectorize and Parallelize RL Environments with JAX: Q-learning at

MCA, Free Full-Text

Lecture 2: Development Infrastructure & Tooling - The Full Stack

Lecture 6: MLOps Infrastructure & Tooling - The Full Stack

Breaking Up with NumPy: Why JAX is Your New Favorite Tool

Scaling deep learning for materials discovery

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

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

Efficiently Scale LLM Training Across a Large GPU Cluster with

GitHub - che-shr-cat/JAX-in-Action: Notebooks for the JAX in

Hyperparameter optimization: Foundations, algorithms, best

Compiler Technologies in Deep Learning Co-Design: A Survey

7 Parallelizing your computations - Deep Learning with JAX