Early stopping¶

Stopping an epoch early¶

You can stop an epoch early by overriding on_train_batch_start() to return -1 when some condition is met.

If you do this repeatedly, for every epoch you had originally requested, then this will stop your entire run.

Early stopping based on metric using the EarlyStopping Callback¶

The EarlyStopping callback can be used to monitor a validation metric and stop the training when no improvement is observed.

To enable it:

Import EarlyStopping callback.
Log the metric you want to monitor using log() method.
Init the callback, and set monitor to the logged metric of your choice.
Pass the EarlyStopping callback to the Trainer callbacks flag.

from pytorch_lightning.callbacks.early_stopping import EarlyStopping

def validation_step(...):
    self.log('val_loss', loss)

trainer = Trainer(callbacks=[EarlyStopping(monitor='val_loss')])

You can customize the callbacks behaviour by changing its parameters.

early_stop_callback = EarlyStopping(
   monitor='val_accuracy',
   min_delta=0.00,
   patience=3,
   verbose=False,
   mode='max'
)
trainer = Trainer(callbacks=[early_stop_callback])

In case you need early stopping in a different part of training, subclass EarlyStopping and change where it is called:

class MyEarlyStopping(EarlyStopping):

    def on_validation_end(self, trainer, pl_module):
        # override this to disable early stopping at the end of val loop
        pass

    def on_train_end(self, trainer, pl_module):
        # instead, do it at the end of training loop
        self._run_early_stopping_check(trainer, pl_module)

Note

The EarlyStopping callback runs at the end of every validation epoch, which, under the default configuration, happen after every training epoch. However, the frequency of validation can be modified by setting various parameters in the Trainer, for example check_val_every_n_epoch and val_check_interval. It must be noted that the patience parameter counts the number of validation epochs with no improvement, and not the number of training epochs. Therefore, with parameters check_val_every_n_epoch=10 and patience=3, the trainer will perform at least 40 training epochs before being stopped.