noether.core.callbacks.online.track_outputs¶

Classes¶

TrackAdditionalOutputsCallback

Callback that is invoked during training after every gradient step to track certain outputs from the update step.

Module Contents¶

class noether.core.callbacks.online.track_outputs.TrackAdditionalOutputsCallback(callback_config, **kwargs)¶

Bases: noether.core.callbacks.periodic.PeriodicCallback

Callback that is invoked during training after every gradient step to track certain outputs from the update step. The update_outputs that are provided in the track_after_accumulation_step method are the additional_outputs field from the TrainerResult returned by the trainer’s update step.

The update_outputs that are provided in the track_after_accumulation_step method are the additional_outputs field from the TrainerResult returned by the trainer’s update step.

The provided update_outputs are assumed to be a dictionary and outputs that match keys or patterns are tracked. An update output matches if either the key matches exactly, e.g. {“some_output”: …} and keys[“some_output”]; or if one of the patterns is contained in the update key name, e.g. {“some_loss”: …} and patterns = [“loss”].

Example config:

- kind: noether.core.callbacks.TrackAdditionalOutputsCallback
  name: TrackAdditionalOutputsCallback
  every_n_updates: 1
  keys:
    - "surface_pressure_loss"

Parameters:

callback_config (noether.core.schemas.callbacks.TrackAdditionalOutputsCallbackConfig) – Configuration for the callback. See TrackAdditionalOutputsCallbackConfig for available options including keys and patterns to track.
**kwargs – Additional keyword arguments provided to the parent class.

out: pathlib.Path | None¶

patterns¶

keys¶

verbose¶

tracked_values: collections.defaultdict[str, list]¶

reduce¶

log_output¶

save_output¶

track_after_accumulation_step(*, update_counter, update_outputs, **_)¶

Track the specified outputs after each accumulation step.

Parameters:

update_counter – UpdateCounter object to track the number of updates.
update_outputs – The additional_outputs field from the TrainerResult returned by the trainer’s update step. Note that the base train_step method in the base trainer does not provide any additional outputs by default, and hence this callback can only be used if the train_step is modified to provide additional outputs.
**_ – Additional unused keyword arguments.

Return type:

None

periodic_callback(*, update_counter, **_)¶

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 (noether.core.utils.training.UpdateCounter) – 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