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pytorch-fsdp

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Master PyTorch FSDP for efficient training.

Software Engineering#sharding#pytorch#distributed training#fsdp#data parallel#large-scale training
AuthorAum08Desai
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill provides expert guidance and practical examples for implementing and optimizing Fully Sharded Data Parallel (FSDP) training with PyTorch, addressing challenges in large-scale model training.

Core Features & Use Cases

  • Parameter Sharding: Understand and implement strategies for sharding model parameters across multiple devices.
  • Mixed Precision & CPU Offloading: Learn how to leverage mixed precision training and CPU offloading to reduce memory footprint.
  • FSDP2: Get up-to-date information on the latest FSDP features and best practices.
  • Use Case: When training a massive language model that exceeds the memory of a single GPU, this Skill helps you configure PyTorch FSDP to distribute the model and data efficiently across your cluster.

Quick Start

Use the pytorch-fsdp skill to learn about the generic join context manager in PyTorch distributed training.

Dependency Matrix

Required Modules

None required

Components

references

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: pytorch-fsdp
Download link: https://github.com/Aum08Desai/hermes-research-agent/archive/main.zip#pytorch-fsdp

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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