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
What is Google JAX? Everything You Need to Know - Geekflare
Dive into Deep Learning — Dive into Deep Learning 1.0.3 documentation
Model Parallelism
Using Cloud TPU Multislice to scale AI workloads
Introducing PyTorch Fully Sharded Data Parallel (FSDP) API
Efficiently Scale LLM Training Across a Large GPU Cluster with
Self-directed online machine learning for topology optimization
Scaling deep learning for materials discovery
Efficiently Scale LLM Training Across a Large GPU Cluster with
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