lightning/docs/Trainer/Training Loop.md

The lightning training loop handles everything except the actual computations of your model. To decide what will happen in your training loop, define the training_step function.

Below are all the things lightning automates for you in the training loop.

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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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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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Force disable early stop

Use this to turn off early stopping and run training to the max_epoch

# DEFAULT
trainer = Trainer(enable_early_stop=True)

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Gradient Clipping

Use this to turn off early stopping and run training to the max_epoch

# DEFAULT (ie: don't clip)
trainer = Trainer(gradient_clip=0)

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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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Set how much of the training set to check

If you don't want to check 100% of the training 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)