add caveat on RNGs and dataloaders #1121
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This is just to clarify the known issue (https://pytorch.org/docs/stable/notes/faq.html#my-data-loader-workers-return-identical-random-numbers) that `DataLoader`s and random number generators from external libraries (such as Numpy) may not interact as expected. I think this should be clarified in the beginner's tutorial on Datasets and DataLoaders as it uses `np.random.randint` in its `RandomCrop` example.
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Hi @chanshing! Can you sign the CLA agreement so that I can merge the pull request? Thanks! |
Hi @holly1238 just signed it now I think. Thanks! |
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This is just to clarify the known issue (https://pytorch.org/docs/stable/notes/faq.html#my-data-loader-workers-return-identical-random-numbers) that `DataLoader`s and random number generators from external libraries (such as Numpy) may not interact as expected. I think this should be clarified in the beginner's tutorial on Datasets and DataLoaders as it uses `np.random.randint` in its `RandomCrop` example. Co-authored-by: holly1238 <77758406+holly1238@users.noreply.github.com>
Follow-up of https://discuss.pytorch.org/t/dataloader-workers-generate-the-same-random-augmentations/28830/4
This is just to clarify the known issue (https://pytorch.org/docs/stable/notes/faq.html#my-data-loader-workers-return-identical-random-numbers) that
DataLoaders and random number generators from external libraries (such as Numpy) may not interact as expected. I think this should be clarified in the beginner's tutorial on Datasets and DataLoaders as it usesnp.random.randintin itsRandomCropexample.