AutoResearchClaw pytorch-training

Name: pytorch-training
Author: aiming-lab

Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.

install

source · Clone the upstream repo

git clone https://github.com/aiming-lab/AutoResearchClaw

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/aiming-lab/AutoResearchClaw "$T" && mkdir -p ~/.claude/skills && cp -r "$T/researchclaw/skills/builtin/tooling/pytorch-training" ~/.claude/skills/aiming-lab-autoresearchclaw-pytorch-training && rm -rf "$T"

manifest: researchclaw/skills/builtin/tooling/pytorch-training/SKILL.md

source content

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