Trainers

TabularTrainer

__init__

__init__(
    model: TabularModel, dp_budget: DpBudget | None = None
) -> None

Trainer for a TabularModel.

Parameters:

Name	Type	Description	Default
model	TabularModel	A TabularModel instance.	required
dp_budget	DpBudget | None	The (eps, delta)-budget for differentially private (DP) training. If None (the default), the training will not be differentially private. Available only for single table datasets.	None

train

train(
    dataset: TabularDataset,
    n_epochs: int | None = None,
    n_steps: int | None = None,
    batch_size: int = 0,
    lr: float = 0.0,
    memory: int = 0,
    valid: Validation | None = None,
    hooks: Sequence[TrainHook] = (),
    accumulate_grad: int = 1,
    dp_step: DpStep | None = None,
    world_size: int = 0,
) -> None

Train the TabularModel with the input TabularDataset.

Parameters:

Name	Type	Description	Default
dataset	TabularDataset	The training data, as a TabularDataset object.	required
n_epochs	int | None	The number of training epochs. One and only one of n_epochs and n_steps must be provided.	None
n_steps	int | None	The number of training steps. One and only one of n_epochs and n_steps must be provided.	None
batch_size	int	The size of a batch of data during training. When it is not specified the user must provide the argument memory.	0
lr	float	The learning rate. If it is 0 the optimal value for the learning rate is automatically determined.	0.0
memory	int	The available memory in MB that is used to automatically compute the optimal value of the batch size.	0
valid	Validation | None	A Validation object. If None, no validation is performed.	None
hooks	Sequence[TrainHook]	A sequence of custom TrainHook objects.	()
accumulate_grad	int	The number of gradient accumulation steps. If equal to 1, the weights are updated at each step.	1
dp_step	DpStep | None	Data for differentially private step. Must be provided if and only if the trainer has a DP-budget.	None
world_size	int	Number of GPUs where to distribute the training. If 0, the training is performed on a single device, on the current device of the TabularTrainer object.	0

save

save(path: Path | str) -> None

Save the TabularTrainer to a checkpoint at the given path.

load classmethod

load(path: Path | str) -> TabularTrainer

Load the TabularTrainer from the checkpoint at the given path.

TextTrainer

__init__

__init__(model: TextModel) -> None

Trainer for a TextModel.

Parameters:

Name	Type	Description	Default
model	TextModel	A TextModel instance.	required

train

train(
    dataset: TextDataset,
    n_epochs: int | None = None,
    n_steps: int | None = None,
    batch_size: int = 0,
    lr: float = 0.0,
    memory: int = 0,
    valid: Validation | None = None,
    hooks: Sequence[TrainHook] = (),
    accumulate_grad: int = 1,
    world_size: int = 0,
) -> None

Train the TextModel with the input TextDataset.

Parameters:

Name	Type	Description	Default
dataset	TextDataset	The training data, as a TextDataset object.	required
n_epochs	int | None	The number of training epochs. One and only one of n_epochs and n_steps must be provided.	None
n_steps	int | None	The number of training steps. One and only one of n_epochs and n_steps must be provided.	None
batch_size	int	The size of a batch of data during training. When it is not specified the user must provide the argument memory.	0
lr	float	The learning rate. If it is 0 the optimal value for the learning rate is automatically determined.	0.0
memory	int	The available memory in MB that is used to automatically compute the optimal value of the batch size.	0
valid	Validation | None	A Validation object. If None, no validation is performed.	None
hooks	Sequence[TrainHook]	A sequence of custom TrainHook objects.	()
accumulate_grad	int	The number of gradient accumulation steps. If equal to 1, the weights are updated at each step.	1
world_size	int	Number of GPUs where to distribute the training. If 0, the training is performed on a single device, on the current device of the TextTrainer object.	0

save

save(path: Path | str) -> None

Save the TextTrainer to a checkpoint at the given path.

load classmethod

load(path: Path | str) -> TextTrainer

Load the TextTrainer from the checkpoint at the given path.