pytorch-lightning/docs/Trainer/Training Loop.md

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Accumulated gradients

Accumulated gradients runs K small batches of size N before doing a backwards pass. The effect is a large effective batch size of size KxN.

# DEFAULT (ie: no accumulated grads)
trainer = Trainer(accumulate_grad_batches=1)

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Anneal Learning rate

Cut the learning rate by 10 at every epoch listed in this list.

# DEFAULT (don't anneal)
trainer = Trainer(lr_scheduler_milestones=None)

# cut LR by 10 at 100, 200, and 300 epochs 
trainer = Trainer(lr_scheduler_milestones=[100, 200, 300])

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Check 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.

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Check which gradients are nan

This option prints a list of tensors with nan gradients.

# DEFAULT
trainer = Trainer(print_nan_grads=False)

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Check validation every n epochs

If you have a small dataset you might want to check validation every n epochs

# DEFAULT
trainer = Trainer(check_val_every_n_epoch=1)

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Display metrics in progress bar

# DEFAULT
trainer = Trainer(progress_bar=True)

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Display the parameter count by layer

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

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Force training for min or max epochs

It can be useful to force training for a minimum number of epochs or limit to a max number

# DEFAULT
trainer = Trainer(min_nb_epochs=1, max_nb_epochs=1000)

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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.

# 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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Set how much of the training set to check

If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag

# DEFAULT
trainer = Trainer(train_percent_check=1.0)

# check 10% only
trainer = Trainer(train_percent_check=0.1)