Detect loss spikes and high losses during training #1473
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dakinggg
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dakinggg
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KillLossSpike callback that 1) compares the train loss against the running average loss, and 2) checks the magnitude of running losses at the end of every batch/training step. This is intended to catch 1) rapid steep spikes and 2) divergent runs respectively.
If we identify a spike or divergence, we log a message to the run metadata. This message is added to the TRAIN_UPDATED run event in MAPI, so it will be displayed to the user in the run events. The message recommends the user to stop and resubmit the run with a lower learning rate.
This change will make it easier to query spiky runs. We also log the loss window leading up to the identified spike for data analysis purposes.
Once we have collected enough data and feel confident about our cancellation threshold, we will switch to the hard user error
LossSpikeErrorwhich cancels the run without retry and prompts the user to resubmit with a lower learning rate.