Disable tests that use DataLoader with multiple workers for Windows#5322
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soumith merged 1 commit intopytorch:masterfrom Feb 21, 2018
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Disable tests that use DataLoader with multiple workers for Windows#5322soumith merged 1 commit intopytorch:masterfrom
soumith merged 1 commit intopytorch:masterfrom
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AlexanderRadionov
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ezyang
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dded ind_worker_queue parameter to data.DataLoader. It makes preprocessing determinate. DataLoader in multiprocessing mode may cause non-deterministic issue. Even if radom_seed has frozen, each subprocess may get tasks in unstable order. This is caused by different I/O time while data loads. If you use augmentation while data loading, it makes results unreproduceble. Look at the https://discuss.pytorch.org/t/deterministic-non-deterministic-results-with-pytorch/9087 To fix this issue I have added the individual queue for each worker. In this case each worker get tasks in the stable order. In summary, subprocess produces the stable results. To reproduce issue you may change ind_worker_queue to False and run the script several times. Code to reproduce issue is in the corresponding PR. * TestIndividualWorkerQueue added to DataLoader tests * Review fixes * "Simplify" code by removing itertools * Rebase conflicts fix * Review fixes * Fixed shutdown behavior * Removed ind_worker_queue flag. * Rebase on master * Disable tests that use DataLoader with multiple workers (#5322)
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laurentdupin
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dded ind_worker_queue parameter to data.DataLoader. It makes preprocessing determinate. DataLoader in multiprocessing mode may cause non-deterministic issue. Even if radom_seed has frozen, each subprocess may get tasks in unstable order. This is caused by different I/O time while data loads. If you use augmentation while data loading, it makes results unreproduceble. Look at the https://discuss.pytorch.org/t/deterministic-non-deterministic-results-with-pytorch/9087 To fix this issue I have added the individual queue for each worker. In this case each worker get tasks in the stable order. In summary, subprocess produces the stable results. To reproduce issue you may change ind_worker_queue to False and run the script several times. Code to reproduce issue is in the corresponding PR. * TestIndividualWorkerQueue added to DataLoader tests * Review fixes * "Simplify" code by removing itertools * Rebase conflicts fix * Review fixes * Fixed shutdown behavior * Removed ind_worker_queue flag. * Rebase on master * Disable tests that use DataLoader with multiple workers (pytorch#5322)
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It seems that DataLoader with multiple workers have been causing CUDA out-of-memory errors in the Windows CI test (such as
test_batch_sampler,test_multi_keepandtest_multi_drop). @ssnl is looking into this issue.Added to #4092.