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

Self-directed online machine learning for topology optimization

Applying sequence and parallel graph splits on a data-parallel

Tutorial 6 (JAX): Transformers and Multi-Head Attention — UvA DL

1 Intro to JAX - Deep Learning with JAX

Tutorial 2 (JAX): Introduction to JAX+Flax — UvA DL Notebooks v1.2

7 Parallelizing your computations - Deep Learning with JAX

Learning JAX in 2023: Part 1 — The Ultimate Guide to Accelerating

Deep learning to decompose macromolecules into independent

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

7 Parallelizing your computations - Deep Learning with JAX

JAX: accelerated machine learning research via composable function

Hyperparameter optimization: Foundations, algorithms, best

Running a deep learning workload with JAX on multinode multi-GPU

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

Differentiable sampling of molecular geometries with uncertainty