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