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Training Config

Source

Training configuration.

TrainingConfig

Bases: BaseModel

Parameters related to the training.

Mandatory parameters are: - num_epochs: number of epochs, greater than 0. - batch_size: batch size, greater than 0. - augmentation: whether to use data augmentation or not (True or False).

Attributes:

  • num_epochs (int) –

    Number of epochs, greater than 0.

checkpoint_callback = CheckpointConfig() class-attribute instance-attribute

Checkpoint callback configuration, following PyTorch Lightning Checkpoint callback.

early_stopping_callback = Field(default=None, validate_default=True) class-attribute instance-attribute

Early stopping callback configuration, following PyTorch Lightning Checkpoint callback.

lightning_trainer_config = Field(default={}) class-attribute instance-attribute

Configuration for the PyTorch Lightning Trainer, following PyTorch Lightning Trainer class

logger = None class-attribute instance-attribute

Logger to use during training. If None, no logger will be used. Available loggers are defined in SupportedLogger.

__str__()

Pretty string reprensenting the configuration.

Returns:

  • str –

    Pretty string.

has_logger()

Check if the logger is defined.

Returns:

  • bool –

    Whether the logger is defined or not.