def from_checkpoint(cls, checkpoint: Checkpoint) -> "XGBoostPredictor":
"""Instantiate the predictor from a Checkpoint.
The checkpoint is expected to be a result of ``XGBoostTrainer``.
Args:
checkpoint: The checkpoint to load the model and
preprocessor from. It is expected to be from the result of a
``XGBoostTrainer`` run.
"""
checkpoint = XGBoostCheckpoint.from_checkpoint(checkpoint)
model = checkpoint.get_model()
preprocessor = checkpoint.get_preprocessor()
return cls(model=model, preprocessor=preprocessor)
def from_checkpoint(
cls,
checkpoint: Checkpoint,
model: Optional[torch.nn.Module] = None,
use_gpu: bool = False,
) -> "TorchPredictor":
"""Instantiate the predictor from a Checkpoint.
The checkpoint is expected to be a result of ``TorchTrainer``.
Args:
checkpoint: The checkpoint to load the model and
preprocessor from. It is expected to be from the result of a
``TorchTrainer`` run.
model: If the checkpoint contains a model state dict, and not
the model itself, then the state dict will be loaded to this
``model``.
use_gpu: If set, the model will be moved to GPU on instantiation and
prediction happens on GPU.
"""
checkpoint = TorchCheckpoint.from_checkpoint(checkpoint)
model = checkpoint.get_model(model)
preprocessor = checkpoint.get_preprocessor()
return cls(model=model, preprocessor=preprocessor, use_gpu=use_gpu)
def _load_checkpoint(
self, checkpoint: Checkpoint
) -> Tuple[lightgbm.Booster, Optional["Preprocessor"]]:
checkpoint = LightGBMCheckpoint.from_checkpoint(checkpoint)
return checkpoint.get_model(), checkpoint.get_preprocessor()
def _load_checkpoint(
self, checkpoint: Checkpoint
) -> Tuple[xgboost.Booster, Optional["Preprocessor"]]:
checkpoint = XGBoostCheckpoint.from_checkpoint(checkpoint)
return checkpoint.get_model(), checkpoint.get_preprocessor()
