noether.core.callbacks.default.train_time

Classes

TrainTimeCallback

Callback to log the time spent on dataloading. Is initialized by the BaseTrainer and should not be added manually to the trainer's callbacks.

Module Contents

class noether.core.callbacks.default.train_time.TrainTimeCallback(callback_config, **kwargs)

Bases: noether.core.callbacks.periodic.PeriodicCallback

Callback to log the time spent on dataloading. Is initialized by the BaseTrainer and should not be added manually to the trainer’s callbacks.

Parameters:

• callback_config (noether.core.schemas.callbacks.CallBackBaseConfig) – Configuration for the callback. See CallBackBaseConfig for available options.

• trainer – Trainer of the current run.

• model – Model of the current run.

• data_container – DataContainer instance that provides access to all datasets.

• tracker – BaseTracker instance to log metrics to stdout/disk/online platform.

• log_writer – LogWriter instance to log metrics.

• checkpoint_writer – CheckpointWriter instance to save checkpoints.

• metric_property_provider – MetricPropertyProvider instance to access properties of metrics.

• name – Name of the callback.

train_times: dict[str, list[float]]

total_train_times: dict[str, float]

track_after_update_step(*, times, **_)

Hook called after each optimizer update step.

This method is invoked after a successful optimizer step and parameter update. It is typically used for tracking metrics that should be recorded once per update cycle, such as:

• Latest loss values

• Learning rates

• Model parameter statistics (norms, etc.)

• Training throughput and timing measurements

Unlike periodic_callback(), this hook is called on every update step, making it suitable for maintaining running averages or high-frequency telemetry.

Note

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

Parameters:

• update_counter – UpdateCounter instance to access current training progress.

• times (dict[str, float]) – Dictionary containing time measurements for various parts of the training step (e.g., ‘data_time’, ‘forward_time’, ‘backward_time’, ‘update_time’).

Return type:

None

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

after_training(**_)

Hook called once after the training loop finishes.

This method is intended to be overridden by derived classes to perform cleanup or final reporting tasks after training is complete. Common use cases include:

• Performing a final evaluation on the test set

• Saving final model weights or artifacts

• Sending notifications (e.g., via Slack or email) about the completed run

• Closing or finalizing experiment tracking sessions

Note

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

Parameters:

update_counter – UpdateCounter instance to access current training progress.

Return type:

None