pytorch-training
Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
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tessl review fix ./researchclaw/skills/builtin/tooling/pytorch-training/SKILL.mdPyTorch Training Best Practice
- Use torch.manual_seed() for reproducibility (set for torch, numpy, random)
- Use DataLoader with num_workers>0 and pin_memory=True for GPU
- Enable cudnn.benchmark=True for fixed input sizes
- Use learning rate schedulers (CosineAnnealingLR or OneCycleLR)
- Implement early stopping based on validation metric
- Log metrics every epoch, save best model checkpoint
- Use torch.no_grad() for evaluation
- Clear gradients with optimizer.zero_grad(set_to_none=True) for efficiency
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