8 Advanced parallelization - Deep Learning with JAX
Por um escritor misterioso
Descrição
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
Compiler Technologies in Deep Learning Co-Design: A Survey
Learn JAX in 2023: Part 2 - grad, jit, vmap, and pmap
High-Performance LLM Training at 1000 GPU Scale With Alpa & Ray
Intro to JAX for Machine Learning, by Khang Pham
What is Google JAX? Everything You Need to Know - Geekflare
Lecture 6: MLOps Infrastructure & Tooling - The Full Stack
What is Google JAX? Everything You Need to Know - Geekflare
Why You Should (or Shouldn't) be Using Google's JAX in 2023
Lecture 2: Development Infrastructure & Tooling - The Full Stack
Tutorial 6 (JAX): Transformers and Multi-Head Attention — UvA DL
Introducing PyTorch Fully Sharded Data Parallel (FSDP) API
Frontiers Tensor Processing Primitives: A Programming
Machine Learning Glossary
Vectorize and Parallelize RL Environments with JAX: Q-learning at
Efficiently Scale LLM Training Across a Large GPU Cluster with