ReddRadar
Agent skill
pytorch-training
Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
Install this agent skill to your Project
npx add-skill https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/pytorch-training
Metadata
Additional technical details for this skill
- author
- researchclaw
- version
- 1.0
- category
- tooling
- priority
- 3
- references
- PyTorch Performance Tuning Guide, pytorch.org
- code template
- import torch import torch.nn as nn from torch.utils.data import DataLoader # Reproducibility torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True # Training loop model.train() for epoch in range(num_epochs): for batch in train_loader: optimizer.zero_grad(set_to_none=True) loss = criterion(model(batch['input']), batch['target']) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) optimizer.step() scheduler.step()
- trigger keywords
- training,pytorch,torch,deep learning,neural network,model
- applicable stages
- 10,12
SKILL.md
PyTorch 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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