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For medium-to-large models, if the user doesn't have enough disk space (or, more commonly, has accidentally specified a path on a volume with not enough disk space), we train for a full "epoch," and crash while saving the checkpoint. It would be nice to either:
Option 1: Save a dummy checkpoint at the very start, before training. If this succeeds, assume that future checkpoints will work if --delete-previous-checkpoint is specified. As an addition, we could check if there is num_checkpoints * size(initial checkpoint) disk space remaining if --delete-previous-checkpoint is not specified, but this is not necessary.
Option 2: Estimate the size of the checkpoint (based on number of parameters) and check if we have enough disk space based on number of checkpoints requested.
The text was updated successfully, but these errors were encountered:
For medium-to-large models, if the user doesn't have enough disk space (or, more commonly, has accidentally specified a path on a volume with not enough disk space), we train for a full "epoch," and crash while saving the checkpoint. It would be nice to either:
Option 1: Save a dummy checkpoint at the very start, before training. If this succeeds, assume that future checkpoints will work if --delete-previous-checkpoint is specified. As an addition, we could check if there is num_checkpoints * size(initial checkpoint) disk space remaining if --delete-previous-checkpoint is not specified, but this is not necessary.
Option 2: Estimate the size of the checkpoint (based on number of parameters) and check if we have enough disk space based on number of checkpoints requested.
The text was updated successfully, but these errors were encountered: