What I learned building a debugger for PyTorch training loops and how it changed how I think about failure diagnosis [D]

In three linesDeveloper built NeuralDBG, a PyTorch debugger that automatically detects training failures (vanishing/exploding gradients, data anomalies). Key insight: failures are layer-localized, not global. Effective monitoring: gradient norm transitions per layer rather than raw histograms. Open-source tool available on PyPI.

Summary generated by Claude — human-verified