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Training and lightning related Pydantic configurations.

LrSchedulerConfig

Bases: BaseModel

Torch learning rate scheduler Pydantic model.

Only parameters supported by the corresponding torch lr scheduler will be taken into account. For more details, check: https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate

Note that mandatory parameters (see the specific LrScheduler signature in the link above) must be provided. For example, StepLR requires step_size.

Attributes:

  • name ({'ReduceLROnPlateau', 'StepLR'}) –

    Name of the learning rate scheduler.

  • parameters (dict) –

    Parameters of the learning rate scheduler (see torch documentation).

name = Field(default='ReduceLROnPlateau') class-attribute instance-attribute

Name of the learning rate scheduler, supported schedulers are defined in SupportedScheduler.

parameters = Field(default={}, validate_default=True) class-attribute instance-attribute

Parameters of the learning rate scheduler, see PyTorch documentation for more details.

step_lr_parameter()

Check that StepLR scheduler has the mandatory step_size parameter specified.

Returns:

  • Self –

    Validated scheduler.

Raises:

  • ValueError –

    If the scheduler is StepLR and the step_size parameter is not specified.

valid_torch_parameters(user_params, values) classmethod

Validate parameters based on the learning rate scheduler's signature.

Parameters:

  • user_params (dict) –

    User parameters.

  • values (ValidationInfo) –

    Pydantic field validation info, used to get the scheduler name.

Returns:

  • dict –

    Validated scheduler parameters.

Raises:

  • ValueError –

    If there are unknown parameters for the specified scheduler.

OptimizerConfig

Bases: BaseModel

Torch optimizer Pydantic model.

Only parameters supported by the corresponding torch optimizer will be taken into account. For more details, check: https://pytorch.org/docs/stable/optim.html#algorithms

Note that mandatory parameters (see the specific Optimizer signature in the link above) must be provided. For example, SGD requires lr.

Attributes:

  • name ({'Adam', 'SGD', 'Adamax', 'AdamW'}) –

    Name of the optimizer.

  • parameters (dict) –

    Parameters of the optimizer (see torch documentation).

name = Field(default='Adam', validate_default=True) class-attribute instance-attribute

Name of the optimizer, supported optimizers are defined in SupportedOptimizer.

parameters = Field(default={}, validate_default=True) class-attribute instance-attribute

Parameters of the optimizer, see PyTorch documentation for more details.

sgd_lr_parameter()

Check that SGD optimizer has the mandatory lr parameter specified.

This is specific for PyTorch < 2.2.

Returns:

  • Self –

    Validated optimizer.

Raises:

  • ValueError –

    If the optimizer is SGD and the lr parameter is not specified.

valid_torch_parameters(user_params, values) classmethod

Validate optimizer parameters.

Parameters:

  • user_params (dict) –

    Parameters passed on to the torch optimizer.

  • values (ValidationInfo) –

    Pydantic field validation info, used to get the optimizer name.

Returns:

  • dict –

    Valid optimizer parameters.

Raises:

  • ValueError –

    If there are unknown parameters for the specified optimizer.