noether.core.schemas.callbacks

Attributes

CallbacksConfig

Classes

CallBackBaseConfig
BestCheckpointCallbackConfig
CheckpointCallbackConfig
EmaCallbackConfig
OnlineLossCallbackConfig
BestMetricCallbackConfig
TrackAdditionalOutputsCallbackConfig
OfflineLossCallbackConfig
MetricEarlyStopperConfig
FixedEarlyStopperConfig

Module Contents

class noether.core.schemas.callbacks.CallBackBaseConfig(/, **data)

Bases: pydantic.BaseModel

Parameters:

data (Any)

name: str = None

kind: str | None = None

every_n_epochs: int | None = None

Epoch-based interval. Invokes the callback after every n epochs. Mutually exclusive with other intervals.

every_n_updates: int | None = None

Update-based interval. Invokes the callback after every n updates. Mutually exclusive with other intervals.

every_n_samples: int | None = None

Sample-based interval. Invokes the callback after every n samples. Mutually exclusive with other intervals.

batch_size: int | None = None

None (use the same batch_size as for training).

Type:

Batch size to use for this callback. Default

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

validate_callback_frequency()

Ensures that exactly one frequency (‘every_n_*’) is specified and that ‘batch_size’ is present if ‘every_n_samples’ is used.

Return type:

CallBackBaseConfig

classmethod check_positive_values(v)

Ensures that all integer-based frequency and batch size fields are positive.

Parameters:

v (int | None)

Return type:

int | None

classmethod check_kind_is_not_empty(v)

Ensures the ‘kind’ field is a non-empty string.

Parameters:

v (str)

Return type:

str

class noether.core.schemas.callbacks.BestCheckpointCallbackConfig(/, **data)

Bases: CallBackBaseConfig

Parameters:

data (Any)

name: Literal['BestCheckpointCallback'] = None

metric_key: str = None

“The key of the metric to be used for checking the best model.

save_frozen_weights: bool = None

Whether to also save the frozen weights of the model.

tolerances: list[int] | None = None

“If provided, this callback will produce multiple best models which differ in the amount of intervals they allow the metric to not improve. For example, tolerance=[5] with every_n_epochs=1 will store a checkpoint where at most 5 epochs have passed until the metric improved. Additionally, the best checkpoint over the whole training will always be stored (i.e., tolerance=infinite). When setting different tolerances, one can evaluate different early stopping configurations with one training run.

model_name: str | None = None

Which model name to save (e.g., if only the encoder of an autoencoder should be stored, one could pass model_name=’encoder’ here).

model_names: list[str] | None = None

Same as model_name but allows passing multiple model_names.

class noether.core.schemas.callbacks.CheckpointCallbackConfig(/, **data)

Bases: CallBackBaseConfig

Parameters:

data (Any)

name: Literal['CheckpointCallback'] = None

save_weights: bool = None

Whether to save the weights of the model.

save_optim: bool = None

Whether to save the optimizer state.

save_latest_weights: bool = None

Whether to save the latest weights of the model. Note that the latest weights are always overwritten on the next invocation of this callback.

save_latest_optim: bool = None

Whether to save the latest optimizer state. Note that the latest optimizer state is always overwritten on the next invocation of this callback

model_name: str | None = None

The name of the model to save. If None, all models are saved.

class noether.core.schemas.callbacks.EmaCallbackConfig(/, **data)

Bases: CallBackBaseConfig

Parameters:

data (Any)

name: Literal['EmaCallback'] = None

target_factors: list[float] = None

The factors for the EMA.

model_paths: list[str | None] | None = None

The paths to the models to apply the EMA to (i.e., composite_model.encoder/composite_model.decoder, path of the PyTorch nn.Modules in the checkpoint). If None, the EMA is applied to the whole model. When training with a CompositeModel, the paths on the submodules (i.e., ‘encoder’, ‘decoder’, etc.) should be provided via this field, otherwise the EMA will be applied to the CompositeModel as a whole which is not possible to restore later on.

save_weights: bool = None

Whether to save the EMA weights.

save_last_weights: bool = None

Save the weights of the model when training is over (i.e., at the end of training, save the EMA weights).

save_latest_weights: bool = None

Save the latest EMA weights. Note that the latest weights are always overwritten on the next invocation of this callback.

class noether.core.schemas.callbacks.OnlineLossCallbackConfig(/, **data)

Bases: CallBackBaseConfig

Parameters:

data (Any)

name: Literal['OnlineLossCallback'] = None

verbose: bool = None

Whether to log the loss.

class noether.core.schemas.callbacks.BestMetricCallbackConfig(/, **data)

Bases: CallBackBaseConfig

Parameters:

data (Any)

name: Literal['BestMetricCallback'] = None

The metric to use to dermine whether the current model obtained a new best (e.g., loss/valid/total)

source_metric_key: str = None

The metrics to keep track of (e.g., loss/test/total)

target_metric_keys: list[str] | None = None

The metrics to keep track of if they are present (useful when different model configurations log different evaluation metrics to avoid reconfiguring the callback).

optional_target_metric_keys: list[str] | None = None

class noether.core.schemas.callbacks.TrackAdditionalOutputsCallbackConfig(/, **data)

Bases: CallBackBaseConfig

Parameters:

data (Any)

name: Literal['TrackAdditionalOutputsCallback'] = None

keys: list[str] | None = None

List of patterns to track. Matched if it is contained in one of the update_outputs keys.

patterns: list[str] | None = None

List of patterns to track. Matched if it is contained in one of the update_outputs keys.

verbose: bool = None

If True uses the logger to print the tracked values otherwise uses no logger.

reduce: Literal['mean', 'last'] = None

The reduction method to be applied to the tracked values to reduce to scalar. Currently supports ‘mean’ and ‘last’.

log_output: bool = None

Whether to log the tracked scalar values.

save_output: bool = None

Whether to save the tracked scalar values to disk.

class noether.core.schemas.callbacks.OfflineLossCallbackConfig(/, **data)

Bases: CallBackBaseConfig

Parameters:

data (Any)

name: Literal['OfflineLossCallback'] = None

dataset_key: str = None

The key of the dataset to be used for the loss calculation. Can be any key that is registered in the DataContainer.

output_patterns_to_log: list[str] | None = None

additional arguments passed to the parent class.

Type:

For instance, if the output key is ‘some_loss’ and the pattern is [‘loss’]. **kwargs

class noether.core.schemas.callbacks.MetricEarlyStopperConfig(/, **data)

Bases: CallBackBaseConfig

Parameters:

data (Any)

name: Literal['MetricEarlyStopper'] = None

metric_key: str

The key of the metric to monitor

tolerance: int

The number of times the metric can stagnate before stopping training

classmethod check_tolerance_positive(v)

Ensures that tolerance is at least 1.

Parameters:

v (int)

Return type:

int

class noether.core.schemas.callbacks.FixedEarlyStopperConfig(/, **data)

Bases: pydantic.BaseModel

Parameters:

data (Any)

kind: str | None = None

name: Literal['FixedEarlyStopper'] = None

stop_at_sample: int | None = None

stop_at_update: int | None = None

stop_at_epoch: int | None = None

validate_callback_frequency()

Ensures that exactly one stop (‘stop_at_*’) is specified

Return type:

FixedEarlyStopperConfig

noether.core.schemas.callbacks.CallbacksConfig