Add tensorflow and pytorch random seed setting & Update requirements. #612
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Why did you close this? I started reviewing and tidying last night. I think it is a great PR, thank you very much. |
I tried adding pytorch and tensorflow requirements to pass the test, but it seems I did something wrong. This PR failed checks. |
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Should I resubmit this PR? |
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You didn’t mess anything up. The requirements are compiled on the OS you run I will try come up with a solution here, perhaps adding tensorflow to the |
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Maybe you can use Thanks for your explanation! |
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How is this PR going? |
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I will work on it when I can. Please do not ping. It is not helpful and you should have no expectation of free service. If you are going to post a message, provide information, such as "I tried using my fork in a project and it worked well" or similar. |
Tests and README are included. Add support for setting tensorflow and pytorch random seed. It can help detect flaky tests due to the randomness, provide reproducibility. Co-Authored-By: Saikat Dutta <saikatdutta.pro2011@gmail.com>
for more information, see https://pre-commit.ci
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Python 3.12 has a segmentation fault, both locally and on CI. TracebackThis occurs reliably and disappears when removing the |
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Yes, I'm trying to do so. But I'm not sure how to create a minimal example of this case. I noticed that except Python 3.12, other versions of CI worked well. With the below dependencies installed: While the Python 3.12 CI failed with the below dependencies installed: which |
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Huh, interesting. It would be surprising though if the missing package caused the segmentation fault. A segfault is when code reaches for memory outside of its allotted area. |
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Please don’t just close PRs when you give up. I may find the time to finish this, or another contributor may be interested. |
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Tests and README are included.
Add support for setting tensorflow and pytorch random seed. It can help detect flaky tests due to the randomness, and provide reproducibility.
Reference:
[1] Dutta, Saikat, et al. "Detecting flaky tests in probabilistic and machine learning applications." Proceedings of the 29th ACM SIGSOFT international symposium on software testing and analysis. 2020.