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zeonzir avatar zeonzir commented on May 29, 2024

A randomly seeded experiment should be completely possible. However, the fixed seed is necessary to recreate similar situations and repeatable experiments. I would suggest a random number generation scheme as input to the pbs script as opposed to completely removing or giving an option to avoid the seed. Let me know what you think.

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lemmersj avatar lemmersj commented on May 29, 2024

I agree with the rationale behind having the fixed seed as a capability, but I feel that the seed being random might be an expected capability (or even an expected behavior --- someone who doesn't read the yaml file too closely might not realize the random number generator is seeded by default).

I also think implementation would be very straightforward --- when the json is read, check if the seed is set to a designated "random" value. If so, set seed to the current timestamp.

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ehofesmann avatar ehofesmann commented on May 29, 2024

We are going to leave it up to the user to manually change the seed which can be automated using the --seed argument. If you still want to remove the seed then you can comment out the line at the bottom of train.py. There is currently a bug though that doesn't seed numpy so all of your experiments up to this point have been random anyway #25

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