Improve PyTorch Performance with Multiple Workers and Shuffling #207
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This pull request has been linked to Shortcut Story #27742: Pytorch slower with higher num_workers. |
ktsitsi
approved these changes
Apr 18, 2023
tests/readers/test_pytorch.py
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| shuffle_buffer_size=shuffle_buffer_size, | ||
| batch_size=batch_size, | ||
| num_workers=num_workers, | ||
| persistent_workers=True, |
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Can we avoid duplication here?
dataloader = PyTorchTileDBDataLoader(
*all_array_params,
shuffle_buffer_size=shuffle_buffer_size,
batch_size=batch_size,
num_workers=num_workers,
persistent_workers=True if num_workers > 0 else False
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I guess we can. Will fix this.
georgeSkoumas
deleted the
gsk/sc-27742/improve_pytorch_performance_with_multiple_workers_and_suffling
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This PR removes row shuffling and only keeps batch shuffling on the Datapipe object via the use of datapipe.shuffle functionality.