row_log_interval / checkpoint_callback and early_stop_callback
I raised an issue in the past regarding the train score not being logged, which was resolved by setting row_log_interval less than the number of batches per epoch.
This also appears to interfere with callbacks. My model stopped writing checkpoints (other than the first) and early stopping also wasn't invoked. I think this occurred when I increased batch_size and didn't reduce row_log_interval accordingly. Worse was a single epoch=0.ckpt file was actually created but no subsequent one - if I understand correctly trainer.test() then restores this checkpoint rather than testing the trained model?
Is this the case or is something else going on here? If it is, it seems like there needs to be a mechanism to default row_log_interval to be at least once per epoch without the user having to manually calculate this? In particular batch_size is a hyperparameter and row_log_interval isn't (or shouldn't be) but it does (seem to) indirectly impact the model score if it prevents callbacks from being invoked?