Early Stopping

Stopping an Epoch Early

You can stop and skip the rest of the current 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 training.

EarlyStopping Callback

The EarlyStopping callback can be used to monitor a 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.

  • Set the mode based on the metric needs to be monitored.

  • Pass the EarlyStopping callback to the Trainer callbacks flag.

from lightning.pytorch.callbacks.early_stopping import EarlyStopping

class LitModel(LightningModule):
    def validation_step(self, batch, batch_idx):
        loss = ...
        self.log("val_loss", loss)

model = LitModel()
trainer = Trainer(callbacks=[EarlyStopping(monitor="val_loss", mode="min")])

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])

Additional parameters that stop training at extreme points:

  • stopping_threshold: Stops training immediately once the monitored quantity reaches this threshold. It is useful when we know that going beyond a certain optimal value does not further benefit us.

  • divergence_threshold: Stops training as soon as the monitored quantity becomes worse than this threshold. When reaching a value this bad, we believes the model cannot recover anymore and it is better to stop early and run with different initial conditions.

  • check_finite: When turned on, it stops training if the monitored metric becomes NaN or infinite.

  • check_on_train_epoch_end: When turned on, it checks the metric at the end of a training epoch. Use this only when you are monitoring any metric logged within training-specific hooks on epoch-level.

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

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


The EarlyStopping callback runs at the end of every validation epoch by default. 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 checks 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.