noether.core.callbacks.checkpoint.ema

Classes

EmaCallback

Callback for exponential moving average (EMA) of model weights.

Module Contents

class noether.core.callbacks.checkpoint.ema.EmaCallback(callback_config, **kwargs)

Bases: noether.core.callbacks.periodic.PeriodicCallback

Callback for exponential moving average (EMA) of model weights.

Example config:

- kind: noether.core.callbacks.EmaCallback
  every_n_epochs: 10
  save_weights: false
  save_last_weights: false
  save_latest_weights: true
  target_factors:
    - 0.9999
  name: EmaCallback
Parameters:
model_paths
target_factors
save_weights
save_last_weights
save_latest_weights
parameters: dict[tuple[str | None, float], dict[str, torch.Tensor]]
buffers: dict[str | None, dict[str, torch.Tensor]]
resume_from_checkpoint(resumption_paths, model)

Resume EMA state from a checkpoint.

Parameters:
Return type:

None

before_training(**_)

Hook called once before the training loop starts.

This method is intended to be overridden by derived classes to perform initialization tasks before training begins. Common use cases include:

  • Initializing experiment tracking (e.g., logging hyperparameters)

  • Printing model summaries or architecture details

  • Initializing specific data structures or buffers needed during training

  • Performing sanity checks on the data or configuration

Note

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

Parameters:

update_counter – UpdateCounter instance to access current training progress.

Return type:

None

apply_ema(cur_model, model_path, target_factor)

fused in-place implementation

track_after_update_step(**_)

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 – 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(*, interval_type, 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 (noether.core.callbacks.periodic.IntervalType) – “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