add support of seed_everything #2654
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…tils.seed.seed_everything
pplantinga
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Sep 11, 2024
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pplantinga
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Looks like a good PR that better follows PyTorch recommendations about randomness. I have a few minor comments but it is good enough that it could be merged now.
docs/experiment.md
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| However, due to the differences in how GPU and CPU executions work, results may not be fully reproducible even with identical seeds, especially when training models. This issue primarily affects training experiments. | ||
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| On the other hand, when preparing data using data preparation scripts, the output of these scripts is independent of the global seeds. This ensures that you will get identical outputs on different setups, even if different seeds are used. |
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Maybe we could expand this to explain important details about distributed experiments, that different seeds will be set on different machines which will affect things like augmentations, but not things like initial model parameters or data loaders.
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Done. Let me know what you think
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What does this PR do?
This PR add the support of a new function called
seed_everythingwhich tries to maximize reproducibility.When using two processes it does print:
While for one:
As a matters of fact, we offset the seed by the rank of the current process.
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