Source code for ray.train.lightgbm.lightgbm_trainer

from typing import Dict, Any, Optional, Tuple, TYPE_CHECKING

from ray.air.checkpoint import Checkpoint
from ray.train.gbdt_trainer import GBDTTrainer
from ray.util.annotations import PublicAPI
from ray.train.lightgbm.lightgbm_checkpoint import LightGBMCheckpoint

import lightgbm
import lightgbm_ray
from lightgbm_ray.tune import TuneReportCheckpointCallback, TuneReportCallback

    from import Preprocessor

[docs]@PublicAPI(stability="beta") class LightGBMTrainer(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 :class:`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: .. code-block:: python import ray from ray.train.lightgbm import LightGBMTrainer from ray.air.config import ScalingConfig train_dataset = [{"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 = Args: 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 :class:`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 to preprocess the provided datasets. resume_from_checkpoint: A checkpoint to resume training from. **train_kwargs: Additional kwargs passed to ``lightgbm.train()`` function. """ # Currently, the RayDMatrix in lightgbm_ray is the same as in xgboost_ray # but it is explicitly set here for forward compatibility _dmatrix_cls: type = lightgbm_ray.RayDMatrix _ray_params_cls: type = lightgbm_ray.RayParams _tune_callback_report_cls: type = TuneReportCallback _tune_callback_checkpoint_cls: type = TuneReportCheckpointCallback _default_ray_params: Dict[str, Any] = { "checkpoint_frequency": 1, "allow_less_than_two_cpus": True, "num_actors": 1, "cpus_per_actor": 2, "gpus_per_actor": 0, } _init_model_arg_name: str = "init_model" def _train(self, **kwargs): return lightgbm_ray.train(**kwargs) def _load_checkpoint( self, checkpoint: Checkpoint ) -> Tuple[lightgbm.Booster, Optional["Preprocessor"]]: checkpoint = LightGBMCheckpoint.from_checkpoint(checkpoint) return checkpoint.get_model(), checkpoint.get_preprocessor() def _save_model(self, model: lightgbm.LGBMModel, path: str): model.booster_.save_model(path) def _model_iteration(self, model: lightgbm.LGBMModel) -> int: return model.booster_.current_iteration()