Use when an ML training run fails or misbehaves -- NaN losses, out-of-memory errors, shape mismatches, or a model that cannot fit even a single batch.
Use when an ML training run fails or misbehaves -- NaN losses, out-of-memory errors, shape mismatches, or a model that cannot fit even a single batch.