class ray.train.xgboost.XGBoostCheckpoint(local_path: Optional[Union[str, os.PathLike]] = None, data_dict: Optional[dict] = None, uri: Optional[str] = None)[source]#

Bases: ray.air.checkpoint.Checkpoint

A Checkpoint with XGBoost-specific functionality.

Create this from a generic Checkpoint by calling XGBoostCheckpoint.from_checkpoint(ckpt).

PublicAPI (beta): This API is in beta and may change before becoming stable.

classmethod from_model(booster: xgboost.core.Booster, *, preprocessor: Optional[Preprocessor] = None) XGBoostCheckpoint[source]#

Create a Checkpoint that stores an XGBoost model.

  • booster – The XGBoost model to store in the checkpoint.

  • preprocessor – A fitted preprocessor to be applied before inference.


An XGBoostCheckpoint containing the specified Estimator.


>>> import numpy as np
>>> import ray
>>> from ray.train.xgboost import XGBoostCheckpoint
>>> import xgboost
>>> train_X = np.array([[1, 2], [3, 4]])
>>> train_y = np.array([0, 1])
>>> model = xgboost.XGBClassifier().fit(train_X, train_y)
>>> checkpoint = XGBoostCheckpoint.from_model(model.get_booster())

You can use a XGBoostCheckpoint to create an XGBoostPredictor and preform inference.

>>> from ray.train.xgboost import XGBoostPredictor
>>> predictor = XGBoostPredictor.from_checkpoint(checkpoint)
get_model() xgboost.core.Booster[source]#

Retrieve the XGBoost model stored in this checkpoint.