Comments (2)
danijar
commented on July 17, 2024
This is expected when training with mixed precision. You could try training with --precision 32 to see if that solves the problem. You can also check out the docs on the LossScaleOptimizer for more details on why this happens when training with mixed precision. It would also be possible to ignore NaNs when aggregating the metrics in the training function indreamer.py.
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yuqingd
commented on July 17, 2024
Thank you, this was really helpful!
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