NaNs don’t crash your training — they quietly destroy it.
After losing hours to a silent failure in a ResNet training run, I built a lightweight detector that pinpoints the exact layer and batch where things break. Using forward hooks and gradient checks, it catches issues early with minimal overhead — without slowing your model to a crawl.
The post PyTorch NaNs Are Silent Killers — So I Built a 3ms Hook to Catch Them at the Exact Layer appeared first on Towards Data Science.
