Understanding self.log()

LightningModule
1

Does self.log(xxx) respect the trainer parameter log_every_n_steps?
If I call self.log(xxx) inside training_step, does that mean it will not log anything until it reaches the required step?
If xxx is expensive to compute, how should one do this?

2

it depends, it self.log(xxx) is done with on_epoch=False then it will be somewhat ignored if it doesn’t reach the required step w.r.t log_every_n_step but if on_epoch=True then it will be used to aggregate results at epoch end.

3

Hey!
Thank you for your answer, but I don’t understand it. Can you try to explain it one more time?

  1. So if I set log_every_n_steps = 100 in trainer.
    in training_step, I compute loss and want it to be logged every log_every_n_steps = 100 steps and in addition, I want it to log at the end of an epoch. Then in this case I just set on_step=True, on_epoch=True?

  2. Also does self.log (when on_epoch = True) execute compute() inside? In case let’s say I want to compute torchmetrics.Accuracy metric on the whole processed batches by the end of my epoch, and I set on_epoch=True, should I add .compute() before and .reset() after? or it is done automatically?

Thank you again!