torch-pipeline-parallelism
Guidance for implementing PyTorch pipeline parallelism for distributed model training. This skill should be used when tasks involve implementing pipeline parallelism, distributed training with model partitioning across GPUs/ranks, AFAB (All-Forward-All-Backward) scheduling, or inter-rank tensor communication using torch.distributed.
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Installation for Agentic Skill
View all platforms →skilz install letta-ai/skills/torch-pipeline-parallelismskilz install letta-ai/skills/torch-pipeline-parallelism --agent opencodeskilz install letta-ai/skills/torch-pipeline-parallelism --agent codexskilz install letta-ai/skills/torch-pipeline-parallelism --agent geminiFirst time? Install Skilz: pip install skilz
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Extract and copy to ~/.claude/skills/ then restart Claude Desktop
git clone https://github.com/letta-ai/skillscp -r skills/ai/benchmarks/letta/terminal-bench-2/trajectory-feedback/torch-pipeline-parallelism ~/.claude/skills/Need detailed installation help? Check our platform-specific guides:
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Agentic Skill Details
- Repository
- skills
- Stars
- 13
- Forks
- 1
- Type
- Technical
- Meta-Domain
- data ai
- Primary Domain
- machine learning
- Market Score
- 21
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