Pytorch Patterns

This is a collection of PyTorch idioms that will save you from common mistakes like hardcoding CUDA calls or forgetting to set model.train() and model.eval(). It covers the full stack: device-agnostic code, reproducibility setup with proper seeding, shape-annotated forward passes, training loops with mixed precision and gradient clipping, and DataLoader configurations that actually use your CPU cores. The checkpoint patterns include optimizer state, which people forget surprisingly often. If you're writing PyTorch from scratch or reviewing someone else's training code, this gives you the patterns that separate working code from production-ready code.

Install to Claude Code

npx -y skills add affaan-m/ecc --skill pytorch-patterns --agent claude-code