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.
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)
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)
The trainer already sets up default early stopping for you. To modify this behavior, pass in your own EarlyStopping callback.
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from pytorch_lightning.callbacks import EarlyStopping # DEFAULTS used by Trainer early_stop_callback = EarlyStopping( monitor='val_loss', min_delta=0.00, patience=3, verbose=False, mode='min' ) # without passing anything in, uses the default callback above trainer = Trainer() # pass in your own to override the default callback trainer = Trainer(early_stop_callback=early_stop_callback) # pass in None to disable it trainer = Trainer(early_stop_callback=None)
Force disable early stop
To disable early stopping pass None to the early_stop_callback
# DEFAULT trainer = Trainer(early_stop_callback=None)
Gradient clipping may be enabled to avoid exploding gradients. Specifically, this will clip the gradient norm computed over all model parameters together.
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# DEFAULT (ie: don't clip) trainer = Trainer(gradient_clip_val=0) # clip gradients with norm above 0.5 trainer = Trainer(gradient_clip_val=0.5)
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)
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.
train_percent_check will be overwritten by overfit_pct if
overfit_pct > 0
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# DEFAULT trainer = Trainer(train_percent_check=1.0) # check 10% only trainer = Trainer(train_percent_check=0.1)
Packed sequences as inputs
When using PackedSequence, do 2 things:
1. return either a padded tensor in dataset or a list of variable length tensors in the dataloader collate_fn (example above shows the list implementation).
2. Pack the sequence in forward or training and validation steps depending on use case.
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# For use in dataloader def collate_fn(batch): x = [item for item in batch] y = [item for item in batch] return x, y # In module def training_step(self, batch, batch_nb): x = rnn.pack_sequence(batch, enforce_sorted=False) y = rnn.pack_sequence(batch, enforce_sorted=False)
Truncated Back Propagation Through Time
There are times when multiple backwards passes are needed for each batch. For example, it may save memory to use Truncated Back Propagation Through Time when training RNNs.
When this flag is enabled each batch is split into sequences of size truncated_bptt_steps and passed to training_step(...) separately. A default splitting function is provided, however, you can override it for more flexibility. See tbptt_split_batch.
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# DEFAULT (single backwards pass per batch) trainer = Trainer(truncated_bptt_steps=None) # (split batch into sequences of size 2) trainer = Trainer(truncated_bptt_steps=2)