ray.train.lightgbm.LightGBMTrainer#
- class ray.train.lightgbm.LightGBMTrainer(train_loop_per_worker: Callable[[], None] | Callable[[Dict], None], *, train_loop_config: Dict | None = None, lightgbm_config: LightGBMConfig | None = None, scaling_config: ScalingConfig | None = None, run_config: RunConfig | None = None, datasets: Dict[str, Dataset | Callable[[], Dataset]] | None = None, dataset_config: DataConfig | None = None, metadata: Dict[str, Any] | None = None, resume_from_checkpoint: Checkpoint | None = None)[source]#
Bases:
DataParallelTrainer
A Trainer for distributed data-parallel LightGBM training.
Example
import lightgbm as lgb import ray.data import ray.train from ray.train.lightgbm import RayTrainReportCallback from ray.train.lightgbm.v2 import LightGBMTrainer def train_fn_per_worker(config: dict): # (Optional) Add logic to resume training state from a checkpoint. # ray.train.get_checkpoint() # 1. Get the dataset shard for the worker and convert to a `lgb.Dataset` train_ds_iter, eval_ds_iter = ( ray.train.get_dataset_shard("train"), ray.train.get_dataset_shard("validation"), ) train_ds, eval_ds = train_ds_iter.materialize(), eval_ds_iter.materialize() train_df, eval_df = train_ds.to_pandas(), eval_ds.to_pandas() train_X, train_y = train_df.drop("y", axis=1), train_df["y"] eval_X, eval_y = eval_df.drop("y", axis=1), eval_df["y"] train_set = lgb.Dataset(train_X, label=train_y) eval_set = lgb.Dataset(eval_X, label=eval_y) # 2. Run distributed data-parallel training. # `get_network_params` sets up the necessary configurations for LightGBM # to set up the data parallel training worker group on your Ray cluster. params = { "objective": "regression", # Adding the line below is the only change needed # for your `lgb.train` call! **ray.train.lightgbm.v2.get_network_params(), } lgb.train( params, train_set, valid_sets=[eval_set], valid_names=["eval"], # To access the checkpoint from trainer, you need this callback. callbacks=[RayTrainReportCallback()], ) train_ds = ray.data.from_items([{"x": x, "y": x + 1} for x in range(32)]) eval_ds = ray.data.from_items( [{"x": x, "y": x + 1} for x in range(32, 32 + 16)] ) trainer = LightGBMTrainer( train_fn_per_worker, datasets={"train": train_ds, "validation": eval_ds}, scaling_config=ray.train.ScalingConfig(num_workers=4), ) result = trainer.fit() booster = RayTrainReportCallback.get_model(result.checkpoint)
- Parameters:
train_loop_per_worker – The training function to execute on each worker. This function can either take in zero arguments or a single
Dict
argument which is set by definingtrain_loop_config
. Within this function you can use any of the Ray Train Loop utilities.train_loop_config – A configuration
Dict
to pass in as an argument totrain_loop_per_worker
. This is typically used for specifying hyperparameters.lightgbm_config – The configuration for setting up the distributed lightgbm backend. See
LightGBMConfig
for more info.scaling_config – The configuration for how to scale data parallel training.
num_workers
determines how many Python processes are used for training, anduse_gpu
determines whether or not each process should use GPUs. SeeScalingConfig
for more info.run_config – The configuration for the execution of the training run. See
RunConfig
for more info.datasets – The Ray Datasets to ingest for training. Datasets are keyed by name (
{name: dataset}
). Each dataset can be accessed from within thetrain_loop_per_worker
by callingray.train.get_dataset_shard(name)
. Sharding and additional configuration can be done by passing in adataset_config
.dataset_config – The configuration for ingesting the input
datasets
. By default, all the Ray Dataset are split equally across workers. SeeDataConfig
for more details.resume_from_checkpoint – A checkpoint to resume training from. This checkpoint can be accessed from within
train_loop_per_worker
by callingray.train.get_checkpoint()
.metadata – Dict that should be made available via
ray.train.get_context().get_metadata()
and incheckpoint.get_metadata()
for checkpoints saved from this Trainer. Must be JSON-serializable.
Methods
[Deprecated] Checks if a Train experiment can be restored from a previously interrupted/failed run.
Launches the Ray Train controller to run training on workers.
Retrieve the LightGBM model stored in this checkpoint.
[Deprecated] Restores a Train experiment from a previously interrupted/failed run.