Training Config
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:
| Name | Type | Description |
|---|---|---|
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.
has_logger()
Check if the logger is defined.
Returns:
| Type | Description |
|---|---|
bool
|
Whether the logger is defined or not. |