ray.train.lightgbm.LightGBMCheckpoint.from_model#

classmethod LightGBMCheckpoint.from_model(booster: lightgbm.basic.Booster, *, preprocessor: Optional[Preprocessor] = None) → LightGBMCheckpoint[source]#

Create a Checkpoint that stores a LightGBM model.

Parameters

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

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

Returns

An LightGBMCheckpoint containing the specified Estimator.

Examples

>>> import lightgbm
>>> import numpy as np
>>> from ray.train.lightgbm import LightGBMCheckpoint
>>>
>>> train_X = np.array([[1, 2], [3, 4]])
>>> train_y = np.array([0, 1])
>>>
>>> model = lightgbm.LGBMClassifier().fit(train_X, train_y)
>>> checkpoint = LightGBMCheckpoint.from_model(model.booster_)

You can use a LightGBMCheckpoint to create an LightGBMPredictor and preform inference.

>>> from ray.train.lightgbm import LightGBMPredictor
>>>
>>> predictor = LightGBMPredictor.from_checkpoint(checkpoint)