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Summary: "need to be" -> "need not be" Pull Request resolved: https://github.com/pytorch/pytorch/pull/11571 Differential Revision: D9786001 Pulled By: soumith fbshipit-source-id: 7cc408f5c8bfcc56d4b5c153646f30e1cec37539
43 lines
1.2 KiB
ReStructuredText
43 lines
1.2 KiB
ReStructuredText
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Reproducibility
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===============
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Completely reproducible results are not guaranteed across PyTorch releases,
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individual commits or different platforms. Furthermore, results need not be
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reproducible between CPU and GPU executions, even when using identical seeds.
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However, in order to make computations deterministic on your specific problem on
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one specific platform and PyTorch release, there are a couple of steps to take.
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There are two pseudorandom number generators involved in PyTorch, which you will
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need to seed manually to make runs reproducible. Furthermore, you should ensure
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that all other libraries your code relies on an which use random numbers also
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use a fixed seed.
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PyTorch
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.......
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You can use :meth:`torch.manual_seed()` to seed the RNG for all devices (both
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CPU and CUDA)
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import torch
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torch.manual_seed(0)
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CuDNN
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.....
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When running on the CuDNN backend, one further option must be set::
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torch.backends.cudnn.deterministic = True
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.. warning::
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Deterministic mode can have a performance impact, depending on your model.
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Numpy
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.....
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If you or any of the libraries you are using rely on Numpy, you should seed the
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Numpy RNG as well. This can be done with::
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import numpy as np
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np.random.seed(0)
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