These flags are useful to help debug a model.
Fast dev run
This flag is meant for debugging a full train/val/test loop. It'll activate callbacks, everything but only with 1 training and 1 validation batch. Use this to debug a full run of your program quickly
# DEFAULT trainer = Trainer(fast_dev_run=False)
Inspect gradient norms
Looking at grad norms can help you figure out where training might be going wrong.
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# DEFAULT (-1 doesn't track norms) trainer = Trainer(track_grad_norm=-1) # track the LP norm (P=2 here) trainer = Trainer(track_grad_norm=2)
Make model overfit on subset of data
A useful debugging trick is to make your model overfit a tiny fraction of the data.
overfit_pct > 0 will overwrite train_percent_check, val_percent_check, test_percent_check
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# DEFAULT don't overfit (ie: normal training) trainer = Trainer(overfit_pct=0.0) # overfit on 1% of data trainer = Trainer(overfit_pct=0.01)
Print the parameter count by layer
By default lightning prints a list of parameters and submodules when it starts training.
Print which gradients are nan
This option prints a list of tensors with nan gradients.
# DEFAULT trainer = Trainer(print_nan_grads=False)
Log GPU usage
Lightning automatically logs gpu usage to the test tube logs. It'll only do it at the metric logging interval, so it doesn't slow down training.