ReddRadar
Agent skill
distributed-training
Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
Install this agent skill to your Project
npx add-skill https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/distributed-training
Metadata
Additional technical details for this skill
- author
- researchclaw
- version
- 1.0
- category
- tooling
- priority
- 7
- references
- PyTorch DDP Tutorial, pytorch.org; Goyal et al., Accurate Large Minibatch SGD, 2017
- trigger keywords
- distributed,multi-gpu,parallel,ddp,scale
- applicable stages
- 10,12
SKILL.md
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
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