William Falcon
bf09060fef
* early stopping callback is not default * added a default logger * added default checkpoint callback * added default checkpoint/loggers * added default checkpoint/loggers * updated docs * cleaned demos * cleaned demos * cleaned demos * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers
2.5 KiB
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
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)
Early stopping
The trainer already sets up default early stopping for you. To modify this behavior, pass in your own EarlyStopping callback.
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'
)
trainer = Trainer(early_stop_callback=early_stop_callback)
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)
Gradient Clipping
Gradient clipping may be enabled to avoid exploding gradients. Specifically, this will clip the gradient norm computed over all model parameters together.
# 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.
# 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
# DEFAULT
trainer = Trainer(train_percent_check=1.0)
# check 10% only
trainer = Trainer(train_percent_check=0.1)