Trainer

EventTrainer

__init__

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

Trainer for an EventModel.

Parameters:

Name	Type	Description	Default
model	EventModel	An EventModel 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: EventDataset,
    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 EventModel with the input EventDataset.

Parameters:

Name	Type	Description	Default
dataset	EventDataset	The training data, as a EventDataset 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 EventTrainer object.	0

save

save(path: Path | str) -> None

Save the EventTrainer to a checkpoint at the given path.

Parameters:

Name	Type	Description	Default
path	Path | str	The path where to sve the checkpoint.	required

load classmethod

load(path: Path | str) -> EventTrainer

Load the EventTrainer from the checkpoint at the given path.

Parameters:

Name	Type	Description	Default
path	Path | str	The path to the loaded checkpoint.	required

Returns:

Type	Description
EventTrainer	The loaded EventTrainer.