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torch.distributed.fsdp
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torch.distributed.fsdp

Creator
Creator
Seonglae Cho
Created
Created
2024 Feb 7 9:48
Editor
Editor
Seonglae Cho
Edited
Edited
2024 Nov 29 21:34
Refs
Refs
FSDP
wrap policy란, FSDP에서 모델을 어떻게 나눌 것인가에 대한 policy
PyTorch에서는 기본적으로 transformer 모델에 대한 policy를 지원
 
 
 

Random port with firewall issue

torch.distributed server back-connects to clients on random ports leading to firewall problems
Updated 2024 Jul 19 15:25
[RFC] Change --standalone to bind to a random port
Updated 2023 Aug 26 4:20
Getting Started with Fully Sharded Data Parallel(FSDP) — PyTorch Tutorials 2.2.1+cu121 documentation
Training AI models at a large scale is a challenging task that requires a lot of compute power and resources. It also comes with considerable engineering complexity to handle the training of these very large models. PyTorch FSDP, released in PyTorch 1.11 makes this easier.
Getting Started with Fully Sharded Data Parallel(FSDP) — PyTorch Tutorials 2.2.1+cu121 documentation
https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html
fsdp2
Supercharging Training using float8 and FSDP2
IBM: Tuan Hoang Trong, Alexei Karve, Yan Koyfman, Linsong Chu, Divya Kumari, Shweta Salaria, Robert Walkup, Praneet Adusumilli, Nirmit Desai, Raghu Ganti, Seetharami Seelam Meta: Less Wright, Wei Feng, Vasiliy Kuznetsov, Driss Guesseous
Supercharging Training using float8 and FSDP2
https://pytorch.org/blog/training-using-float8-fsdp2/
Supercharging Training using float8 and FSDP2
 
 
 

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torch.distributed.fsdp
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