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Accurate Time Remaining for Training #1096

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pertschuk opened this issue Mar 8, 2020 · 4 comments
Closed

Accurate Time Remaining for Training #1096

pertschuk opened this issue Mar 8, 2020 · 4 comments
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feature Is an improvement or enhancement good first issue Good for newcomers

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@pertschuk
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🚀 Feature

Currently tqdm time remaining in an epoch doesn't account for the fact that validation steps are faster and so is often wildly inaccurate, fluctuating between the time remaining if all steps were validation and if all steps were training.

Motivation

Would make it much easier to see how long an epoch is gonna take without having to do the math manually.

Pitch

Could just not include validation steps in the the overall epoch tqdm iterator but that would still not be accurate, could break the epoch down into the validation chunks and show a training bar for each and then saw (1/4) if validation steps are 0.25.

Could somehow calculate the speed of validation step vs training and produce accurate times for both (not sure how difficult)...

Alternatives

Remove the time remaining from tqdm all together? If it's not accurate.

Additional context

Epoch 1:  30%|██████████████████▌                                          | 3300/10868 [10:30<24:27,  5.16it/s, loss=0.473, train_loss=0.548]
@pertschuk pertschuk added feature Is an improvement or enhancement help wanted Open to be worked on labels Mar 8, 2020
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github-actions bot commented Mar 8, 2020

Hi! thanks for your contribution!, great first issue!

@Borda
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Borda commented Mar 11, 2020

Hey, good point, if you have some elegant solution in mind, could you send a PR?

@Borda Borda added the good first issue Good for newcomers label Mar 11, 2020
@pertschuk
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@Borda Sure, I will look into this next week when I (hopefully) have more time

@pertschuk
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pertschuk commented Mar 19, 2020

I couldn't find a good way to do with with tqdm, best temporary fix I found is to set smoothing=0 so it just takes an average of all steps rather than trying to exponentially weight most recent steps.

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Labels
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