pytorch-fsdp

name pytorch-fsdp description Fully sharded data-parallel training for large models. version 1.0.0 author Orchestra Research license MIT dependencies ["torch>=2.0","transformers"] platforms ["linux","macos"] metadata {"hermes":{"tags":["Distributed Training","PyTorch","FSDP","Data Parallel","Sharding","Mixed Precision","CPU Offloading","FSDP2","Large-Scale Training"]}} Pytorch-Fsdp Skill Assistance with pytorch-fsdp development, generated from official documentation. When to Use This Skill This skill should be triggered when: Working with pytorch-fsdp Asking about pytorch-fsdp features or APIs Implementing pytorch-fsdp solutions Debugging pytorch-fsdp code Learning pytorch-fsdp best practices Quick Reference The full common-patterns catalog (~157k chars of runnable FSDP snippets) lives in references/common-patterns.md — load it with read_file when you need wrapping, sharding-strategy, checkpoint, or mixed-precision examples. Start there rather than reconstructing FSDP incantations from memory. Reference Files This skill includes comprehensive documentation in references/ : other.md - Other documentation Use view to read specific reference files when detailed information is needed. Working with This Skill For Beginners Start with the getting_started or tutorials reference files for foundational concepts. For Specific Features Use the appropriate category reference file (api, guides, etc.) for detailed information. For Code Examples The quick reference section above contains common patterns extracted from the official docs. Resources references/ Organized documentation extracted from official sources. These files contain: Detailed explanations Code examples with language annotations Links to original documentation Table of contents for quick navigation scripts/ Add helper scripts here for common automation tasks. assets/ Add templates, boilerplate, or example projects here. Notes This skill was automatically generated from official documentation Reference files preserve the structure and examples from source docs Code examples include language detection for better syntax highlighting Quick reference patterns are extracted from common usage examples in the docs Updating To refresh this skill with updated documentation: Re-run the scraper with the same configuration The skill will be rebuilt with the latest information