noether.core.callbacks.default.lr

Classes

LrCallback

Callback to log the learning rate of the optimizer.

Module Contents

class noether.core.callbacks.default.lr.LrCallback(callback_config, trainer, model, data_container, tracker, log_writer, checkpoint_writer, metric_property_provider, name=None)

Bases: noether.core.callbacks.periodic.PeriodicCallback

Callback to log the learning rate of the optimizer.

This callback is initialized by the BaseTrainer and should not be added manually to the trainer’s callbacks.

Parameters:
periodic_callback(**_)

Hook called periodically based on the configured intervals.

This method is the primary entry point for periodic actions in subclasses. It is triggered when any of the configured intervals (every_n_epochs, every_n_updates, or every_n_samples) are reached.

Subclasses should override this method to implement periodic logic such as:

  • Calculating and logging expensive validation metrics

  • Saving specific model checkpoints or artifacts

  • Visualizing training progress (e.g., plotting samples)

  • Adjusting training hyperparameters or model state

Note

This method is executed within a torch.no_grad() context.

Parameters:

  • interval_type – “epoch”, “update”, “sample” or “eval” indicating which interval triggered this callback.

  • update_counter – UpdateCounter instance providing details about the current training progress (epoch, update, sample counts).

  • **kwargs – Additional keyword arguments passed from the triggering hook (e.g., from after_epoch() or after_update()).

Return type:

None