pytorch-training

name	pytorch-training
description	Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
metadata	{"category":"tooling","trigger-keywords":"training,pytorch,torch,deep learning,neural network,model","applicable-stages":"10,12","priority":"3","version":"1.0","author":"researchclaw","references":"PyTorch Performance Tuning Guide, pytorch.org","code-template":"import torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\n\n# Reproducibility\ntorch.manual_seed(seed)\ntorch.cuda.manual_seed_all(seed)\ntorch.backends.cudnn.deterministic = True\n\n# Training loop\nmodel.train()\nfor epoch in range(num_epochs):\n for batch in train_loader:\n optimizer.zero_grad(set_to_none=True)\n loss = criterion(model(batch['input']), batch['target'])\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n"}

PyTorch Training Best Practice

1. Use torch.manual_seed() for reproducibility (set for torch, numpy, random)
2. Use DataLoader with num_workers>0 and pin_memory=True for GPU
3. Enable cudnn.benchmark=True for fixed input sizes
4. Use learning rate schedulers (CosineAnnealingLR or OneCycleLR)
5. Implement early stopping based on validation metric
6. Log metrics every epoch, save best model checkpoint
7. Use torch.no_grad() for evaluation
8. Clear gradients with optimizer.zero_grad(set_to_none=True) for efficiency