Distributed training
Skill thada2402/AutoResearchClaw/researchclaw/skills/builtin/tooling/distributed-training
Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.From its SKILL.md
npx -y skills add thada2402/AutoResearchClaw --skill distributed-trainingAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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Distributed Training Best Practice
- Use DistributedDataParallel (DDP) over DataParallel for multi-GPU
- Initialize process group: dist.init_process_group(backend='nccl')
- Use DistributedSampler for data sharding
- Synchronize batch norm: nn.SyncBatchNorm.convert_sync_batchnorm()
- Only save checkpoint on rank 0
- Scale learning rate linearly with world size
- Use gradient accumulation for effectively larger batch sizes
What ships with it
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