noether.core.callbacks.default.lr¶
Classes¶
Callback to log the learning rate of the optimizer. |
Module Contents¶
- class noether.core.callbacks.default.lr.LrCallback(*args, **kwargs)¶
Bases:
noether.core.callbacks.periodic.PeriodicCallbackCallback to log the learning rate of the optimizer.
This callback is initialized by the
BaseTrainerand should not be added manually to the trainer’s callbacks.- Parameters:
callback_config – Configuration for the callback. See
CallBackBaseConfigfor available options.trainer – Trainer of the current run.
model – Model of the current run.
data_container –
DataContainerinstance that provides access to all datasets.tracker –
BaseTrackerinstance to log metrics to stdout/disk/online platform.log_writer –
LogWriterinstance to log metrics.checkpoint_writer –
CheckpointWriterinstance to save checkpoints.metric_property_provider –
MetricPropertyProviderinstance to access properties of metrics.name – Name of the callback.
args (Any)
kwargs (Any)
- 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, orevery_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 –
UpdateCounterinstance 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()orafter_update()).
- Return type:
None