ray.train.lightgbm.LightGBMTrainer#

class ray.train.lightgbm.LightGBMTrainer(*args, **kwargs)[source]#

Bases: ray.train.gbdt_trainer.GBDTTrainer

A Trainer for data parallel LightGBM training.

This Trainer runs the LightGBM training loop in a distributed manner using multiple Ray Actors.

If you would like to take advantage of LightGBM’s built-in handling for features with the categorical data type, consider using the Categorizer preprocessor to set the dtypes in the dataset.

Note

LightGBMTrainer does not modify or otherwise alter the working of the LightGBM distributed training algorithm. Ray only provides orchestration, data ingest and fault tolerance. For more information on LightGBM distributed training, refer to LightGBM documentation.

Example

import ray

from ray.train.lightgbm import LightGBMTrainer
from ray.air.config import ScalingConfig

train_dataset = ray.data.from_items(
    [{"x": x, "y": x + 1} for x in range(32)])
trainer = LightGBMTrainer(
    label_column="y",
    params={"objective": "regression"},
    scaling_config=ScalingConfig(num_workers=3),
    datasets={"train": train_dataset}
)
result = trainer.fit()
Parameters

  • datasets – Ray Datasets to use for training and validation. Must include a “train” key denoting the training dataset. If a preprocessor is provided and has not already been fit, it will be fit on the training dataset. All datasets will be transformed by the preprocessor if one is provided. All non-training datasets will be used as separate validation sets, each reporting a separate metric.

  • label_column – Name of the label column. A column with this name must be present in the training dataset.

  • params – LightGBM training parameters passed to lightgbm.train(). Refer to LightGBM documentation for a list of possible parameters.

  • dmatrix_params – Dict of dataset name:dict of kwargs passed to respective xgboost_ray.RayDMatrix initializations, which in turn are passed to lightgbm.Dataset objects created on each worker. For example, this can be used to add sample weights with the weights parameter.

  • scaling_config – Configuration for how to scale data parallel training.

  • run_config – Configuration for the execution of the training run.

  • preprocessor – A ray.data.Preprocessor to preprocess the provided datasets.

  • resume_from_checkpoint – A checkpoint to resume training from.

  • **train_kwargs – Additional kwargs passed to lightgbm.train() function.

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