ray.train.xgboost.XGBoostTrainer
ray.train.xgboost.XGBoostTrainer#
- class ray.train.xgboost.XGBoostTrainer(*args, **kwargs)[source]#
Bases:
ray.train.gbdt_trainer.GBDTTrainer
A Trainer for data parallel XGBoost training.
This Trainer runs the XGBoost training loop in a distributed manner using multiple Ray Actors.
Note
XGBoostTrainer
does not modify or otherwise alter the working of the XGBoost distributed training algorithm. Ray only provides orchestration, data ingest and fault tolerance. For more information on XGBoost distributed training, refer to XGBoost documentation.Example
import ray from ray.train.xgboost import XGBoostTrainer from ray.air.config import ScalingConfig train_dataset = ray.data.from_items( [{"x": x, "y": x + 1} for x in range(32)]) trainer = XGBoostTrainer( label_column="y", params={"objective": "reg:squarederror"}, 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 thepreprocessor
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 – XGBoost training parameters. Refer to XGBoost documentation for a list of possible parameters.
dmatrix_params – Dict of
dataset name:dict of kwargs
passed to respectivexgboost_ray.RayDMatrix
initializations, which in turn are passed toxgboost.DMatrix
objects created on each worker. For example, this can be used to add sample weights with theweights
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
xgboost.train()
function.
PublicAPI (beta): This API is in beta and may change before becoming stable.