Fix Distributed Data Samplers in PyTorch Examples #2012
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Signed-off-by: Andrey Velichkevich <andrey.velichkevich@gmail.com>
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| @@ -10,141 +10,216 @@ | |||
| import torch.nn as nn | |||
| import torch.nn.functional as F | |||
| import torch.optim as optim | |||
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| WORLD_SIZE = int(os.environ.get('WORLD_SIZE', 1)) | |||
| from torch.utils.data import DistributedSampler | |||
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Could we apply the same approach to Katib?
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Sure, I will submit separate PR
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I'd like to run these examples in CI to verify if these examples are valid, but we can track it in another issues.
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/lgtm |
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Signed-off-by: Andrey Velichkevich <andrey.velichkevich@gmail.com> (cherry picked from commit 57aa34d)
Signed-off-by: Andrey Velichkevich <andrey.velichkevich@gmail.com> Signed-off-by: deepanker13 <deepanker.gupta@nutanix.com>
Signed-off-by: Andrey Velichkevich <andrey.velichkevich@gmail.com>
Signed-off-by: Andrey Velichkevich <andrey.velichkevich@gmail.com>
I fixed PyTorch training examples where we forgot to distribute data across PyTorch workers using
DistributedSampler(dataset). After that change each PyTorch worker will correctly process chunk of training data.Also, for FashionMNIST example I removed check if this script is running in distributed mode since for PyTorch we can just set arbitrary values for env variables and run this script in 1 Worker.
/assign @johnugeorge @tenzen-y @kuizhiqing