Tune batch size using distributed training to catch edge case CUDA OOMs #2934
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arnavgarg1
approved these changes
Jan 14, 2023
Unit Test Results 6 files ± 0 6 suites ±0 2h 48m 43s ⏱️ + 1h 52m 2s For more details on these failures, see this check. Results for commit 3db937d. ± Comparison against base commit 9e3a98f. ♻️ This comment has been updated with latest results. |
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…/ludwig into distributed-auto-batch
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arnavgarg1
reviewed
Mar 22, 2023
tgaddair
commented
Apr 3, 2023
ludwig/trainers/trainer.py
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| @@ -349,6 +360,34 @@ def write_step_summary(cls, train_summary_writer, combined_loss, all_losses, ste | |||
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| train_summary_writer.flush() | |||
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| def train_for_tuning( | |||
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This function is no longer used. There's a new API called BatchSizeEvaluator that we use instead.
tgaddair
commented
Apr 3, 2023
| ) | ||
| best_samples_per_sec = 0 | ||
| best_batch_size = None | ||
| try: |
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Please go back to using the BatchSizeEvaluator which abstracts away all this boilerplate.
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In some cases, CUDA OOMs can occur only when the additional overhead of distributed training (from the GPU fusion buffer in Horovod) are introduced. This PR runs batch size eval in a full distributed context to ensure these edge cases are accounted for when selecting the best batch size.