class lightning.pytorch.callbacks.LearningRateFinder(min_lr=1e-08, max_lr=1, num_training_steps=100, mode='exponential', early_stop_threshold=4.0, update_attr=True, attr_name='')[source]

Bases: Callback

The LearningRateFinder callback enables the user to do a range test of good initial learning rates, to reduce the amount of guesswork in picking a good starting learning rate.


This is an experimental feature.

  • min_lr (float) – Minimum learning rate to investigate

  • max_lr (float) – Maximum learning rate to investigate

  • num_training_steps (int) – Number of learning rates to test

  • mode (str) –

    Search strategy to update learning rate after each batch:

    • 'exponential' (default): Increases the learning rate exponentially.

    • 'linear': Increases the learning rate linearly.

  • early_stop_threshold (Optional[float]) – Threshold for stopping the search. If the loss at any point is larger than early_stop_threshold*best_loss then the search is stopped. To disable, set to None.

  • update_attr (bool) – Whether to update the learning rate attribute or not.

  • attr_name (str) – Name of the attribute which stores the learning rate. The names ‘learning_rate’ or ‘lr’ get automatically detected. Otherwise, set the name here.


# Customize LearningRateFinder callback to run at different epochs.
# This feature is useful while fine-tuning models.
from lightning.pytorch.callbacks import LearningRateFinder

class FineTuneLearningRateFinder(LearningRateFinder):
    def __init__(self, milestones, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.milestones = milestones

    def on_fit_start(self, *args, **kwargs):

    def on_train_epoch_start(self, trainer, pl_module):
        if trainer.current_epoch in self.milestones or trainer.current_epoch == 0:
            self.lr_find(trainer, pl_module)

trainer = Trainer(callbacks=[FineTuneLearningRateFinder(milestones=(5, 10))])

MisconfigurationException – If learning rate/lr in model or model.hparams isn’t overridden, or if you are using more than one optimizer.

on_fit_start(trainer, pl_module)[source]

Called when fit begins.

Return type: