<ul><li>To implement a NaN capturing solution in PyTorch, one can use PyTorch Lightning's callback interface.</li><li>A NaNCapture Lightning callback is created to handle NaN events during training.</li><li>The callback stores corrupted models and halts training upon encountering NaN values.</li><li>Reproducibility is ensured by including NaNCapture state in the checkpoints for debugging.</li><li>Loading the stored training batch for debugging relies on Lightning's LightningDataModule.</li><li>Testing the callback involves creating a problematic model to trigger NaN occurrences.</li><li>Runtime performance is minimally impacted by the NaNCapture callback, providing valuable debug capabilities.</li><li>Enhancements like capturing and restoring random states for reproducibility are also discussed.</li><li>Encountering NaN failures in machine learning can be challenging and indicate model issues.</li><li>The proposed approach using Lightning callback streamlines NaN error debugging.</li><li>This solution can save developers significant time and effort in debugging NaN errors.</li></ul>

Debugging the Dreaded NaN

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