Neg n_workers are now the same as zero #4019
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FWIW, scikit-learn and joblib use a different convention: -1 means "use the same number of workers as CPU cores", -2 means "use num_cores-1", etc. From joblib docs:
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Taking into account @kmike's comment I feel like it'd be best to raise |
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Changed it to a |
apaszke
approved these changes
Dec 5, 2017
apaszke
reviewed
Dec 5, 2017
torch/utils/data/dataloader.py
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| @@ -219,6 +219,9 @@ def __init__(self, loader): | |||
| # prime the prefetch loop | |||
| for _ in range(2 * self.num_workers): | |||
| self._put_indices() | |||
| elif self.num_workers < 0: | |||
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apaszke
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Thank you! |
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I foolishly passed the kwarg
num_workers=-1to aDataLoaderexpecting that it would default to serial processing (I correctly recalled that it should be a non-positive number, but failed to remember that it must be zero to get this behavior).Normally this would be fine, but the error message that it spat out when I did this was very confusing:
What made the matter worse is that I recently put a print statement in that file to test something, so I though I had broken pytorch, but I couldn't figure out where the difference was. After spending too much time searching for cached pyc files, I finally realized my mistake.
I wanted to submit a patch so it would at least spit out a ValueError. However, I think it is a simpler change to have it accept negative numbers. This change simply makes it so setting num_workers to a non-positive number results in the same behavior as zero.