lightning/docs/Trainer/debugging.md

These flags are useful to help debug a model.

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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)

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Inspect gradient norms

Looking at grad norms can help you figure out where training might be going wrong.

# 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)

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Make model overfit on subset of data

A useful debugging trick is to make your model overfit a tiny fraction of the data.

setting overfit_pct > 0 will overwrite train_percent_check, val_percent_check, test_percent_check

# DEFAULT don't overfit (ie: normal training)
trainer = Trainer(overfit_pct=0.0)

# overfit on 1% of data 
trainer = Trainer(overfit_pct=0.01)

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Print the parameter count by layer

By default lightning prints a list of parameters and submodules when it starts training.

# DEFAULT print a full list of all submodules and their parameters.
trainer = Trainer(weights_summary='full')

# only print the top-level modules (i.e. the children of LightningModule).
trainer = Trainer(weights_summary='top')

---

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.