- class ray.train.gbdt_trainer.GBDTTrainer(*args, **kwargs)#
Abstract class for scaling gradient-boosting decision tree (GBDT) frameworks.
Inherited by XGBoostTrainer and LightGBMTrainer.
datasets – 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 – Framework specific training parameters.
dmatrix_params – Dict of
dataset name:dict of kwargspassed to respective
num_boost_round – Target number of boosting iterations (trees in the model).
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 framework
DeveloperAPI: This API may change across minor Ray releases.
Converts self to a
Checks whether a given directory contains a restorable Train experiment.
Called during fit() to preprocess dataset attributes with preprocessor.
restore(path[, storage_filesystem, ...])
Restores a Train experiment from a previously interrupted/failed run.
Called during fit() to perform initial setup on the Trainer.