Warn when running an infinite epoch and overriding "epoch end" accumulating hooks

[carmocca]

🚀 Feature

When the user configures Trainer(max_steps=-1, max_epochs=-1) an endless epoch runs, so overriding training_epoch_end or validation_epoch_end with val_check_interval==float can be a problem because they will keep outputs in memory indefinitely.

Motivation

Many users are not aware of the impact of overriding these hooks so infinite epochs open the door to "memory leaks".

Pitch

Raise a warning in this case informing the user of this behaviour.

Additional context

Proposed in #11480 (comment)

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cc @Borda @carmocca @awaelchli @ninginthecloud @daniellepintz @rohitgr7 @justusschock @kaushikb11

[vedpatwardhan]

I'd like to work on this, could you assign this issue to me?

[tchaton]

Hey @vedpatwardhan,

Yes, go on !

[akihironitta]

@carmocca I was thinking it also applies to the case of only max_steps=-1 being specified because it means an endless epoch, right?

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Update: Well, I wasn't correct at all. If one specifies only max_steps=-1, max_epochs=1000 will be used (and thus it's not endless training), so no need to add the warning in this case.

Then, the 1.5 release note was quite confusing because it's not really an endless epoch with Trainer(max_steps=-1) but a complete epoch. https://github.com/PyTorchLightning/pytorch-lightning/releases/tag/1.5.0

Infinite training is now supported by setting Trainer(max_epochs=-1) for an unlimited number of epochs, or Trainer(max_steps=-1) for an endless epoch.

Note: you will want to avoid logging with on_epoch=True in case of max_steps=-1.

[vedpatwardhan]

I had a doubt while writing a test for the change being made. In the test_config_validator.py file, inside the test_fit_val_loop_config function, if a warning is created, then the BoringModel class is instantiated, could you please guide me what that class is all about, and do I need to instantiate the BoringModel class for the warning generated too? Also, what other properties of the model do I need to change before calling trainer.fit(model)?
Thanks in advance.

[ananthsub]

Here's one suggestion to test.

• Create a new class that extends the BoringModel and implements the training_epoch_end.
• Construct a Trainer which has settings configured for infinite training.
• Call trainer.fit and confirm that you see a warning emitted from the configuration validator

You can follow the pattern for the validation_epoch_end and val_check_interval==float scenario described in the issue too.