noether.core.callbacks.default.param_count¶

Classes¶

ParamCountCallback

Callback to log the number of trainable and frozen parameters of the model.

Module Contents¶

class noether.core.callbacks.default.param_count.ParamCountCallback(trainer, model, data_container, tracker, log_writer, checkpoint_writer, metric_property_provider, name=None)¶

Bases: noether.core.callbacks.base.CallbackBase

Callback to log the number of trainable and frozen parameters of the model.

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

Parameters:

trainer (noether.training.trainers.BaseTrainer) – Trainer of the current run.
model (noether.core.models.ModelBase) – Model of the current run.
data_container (noether.data.container.DataContainer) – DataContainer instance that provides access to all datasets.
tracker (noether.core.trackers.BaseTracker) – BaseTracker instance to log metrics to stdout/disk/online platform.
log_writer (noether.core.writers.LogWriter) – LogWriter instance to log metrics to stdout/disk/online platform.
checkpoint_writer (noether.core.writers.CheckpointWriter) – CheckpointWriter instance to save checkpoints during training.
metric_property_provider (noether.core.providers.metric_property.MetricPropertyProvider) – MetricPropertyProvider instance to access properties of metrics.
name (str | None) – Name of the callback.

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