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.train 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 – The Ray Datasets to use for training and validation. Must include a “train” key denoting the training dataset. 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 theweight
parameter.num_boost_round – Target number of boosting iterations (trees in the model). Note that unlike in
xgboost.train
, this is the target number of trees, meaning that if you setnum_boost_round=10
and pass a model that has already been trained for 5 iterations, it will be trained for 5 iterations more, instead of 10 more.scaling_config – Configuration for how to scale data parallel training.
run_config – Configuration for the execution of the training run.
resume_from_checkpoint – A checkpoint to resume training from.
metadata – Dict that should be made available in
checkpoint.get_metadata()
for checkpoints saved from this Trainer. Must be JSON-serializable.**train_kwargs – Additional kwargs passed to
xgboost.train()
function.
PublicAPI (beta): This API is in beta and may change before becoming stable.
Methods
Converts self to a
tune.Trainable
class.can_restore
(path[, storage_filesystem])Checks whether a given directory contains a restorable Train experiment.
fit
()Runs training.
get_model
(checkpoint)Retrieve the XGBoost model stored in this checkpoint.
restore
(path[, storage_filesystem, ...])Restores a Train experiment from a previously interrupted/failed run.
setup
()Called during fit() to perform initial setup on the Trainer.