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pytorch-guide
Avoid common PyTorch mistakes and apply robust training patterns
skills/domains/ai-ml/pytorch-guide/SKILL.md
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# PyTorch Guide
## Overview
PyTorch is the dominant deep learning framework in academic research, used in the majority of papers at NeurIPS, ICML, and ICLR. Its eager execution model, Pythonic API, and seamless integration with the Python scientific stack make it the default choice for prototyping and publishing research code.
However, PyTorch's flexibility is a double-edged sword. Subtle bugs -- forgetting `model.eval()`, accumulating gradients across batches, incorrect device placement, memory leaks from detached tensors -- can silently corrupt results without raising errors. These issues are especially dangerous in research settings where ground truth is unknown.
This guide catalogs the most common PyTorch mistakes, provides battle-tested training patterns, and covers performance optimization techniques that every researcher should know. The patterns here are drawn from top-tier ML research codebases and the PyTorch team's own best practice recommendations.
## Common Mistakes and Fixes
### The Big Five Mistakes
