debugpytorch

Features and purpose

Debug PyTorch issues systematically. Use when encountering tensor errors, CUDA out of memory errors, gradient problems like NaN loss or exploding gradients, shape mismatches between layers, device conflicts between CPU and GPU, autograd graph issues, DataLoader problems, dtype mismatches, or training instabilities in deep learning workflows.

Quick start

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.