&`iRddlZddlmZmZmZmZmZddlZddlm Z ddl m Z ddl m Z mZddlmZejeZGdde ZdS) N)AnyCallableDictOptionalUnion) Checkpoint)DataParallelTrainer)LightGBMConfigget_network_params) GenDatasetcJeZdZdZddddddddddeegdfeegdffdeedeedee j j dee j j d eee efd ee j jd eee efd eeffd ZxZS)LightGBMTraineraA Trainer for distributed data-parallel LightGBM training. Example ------- .. testcode:: :skipif: True import lightgbm as lgb import ray.data import ray.train from ray.train.lightgbm import RayTrainReportCallback, 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.get_network_params(), } lgb.train( params, train_set, valid_sets=[eval_set], valid_names=["eval"], 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) Args: 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 defining ``train_loop_config``. Within this function you can use any of the :ref:`Ray Train Loop utilities `. train_loop_config: A configuration ``Dict`` to pass in as an argument to ``train_loop_per_worker``. This is typically used for specifying hyperparameters. lightgbm_config: The configuration for setting up the distributed lightgbm backend. See :class:`~ray.train.lightgbm.LightGBMConfig` for more info. datasets: The Ray Datasets to use for training and validation. dataset_config: The configuration for ingesting the input ``datasets``. By default, all the Ray Dataset are split equally across workers. See :class:`~ray.train.DataConfig` for more details. scaling_config: The configuration for how to scale data parallel training. ``num_workers`` determines how many Python processes are used for training, and ``use_gpu`` determines whether or not each process should use GPUs. See :class:`~ray.train.ScalingConfig` for more info. run_config: The configuration for the execution of the training run. See :class:`~ray.train.RunConfig` for more info. resume_from_checkpoint: A checkpoint to resume training from. This checkpoint can be accessed from within ``train_loop_per_worker`` by calling ``ray.train.get_checkpoint()``. metadata: Dict that should be made available via `ray.train.get_context().get_metadata()` and in `checkpoint.get_metadata()` for checkpoints saved from this Trainer. Must be JSON-serializable. N)train_loop_configlightgbm_configscaling_config run_configdatasetsdataset_configmetadataresume_from_checkpointtrain_loop_per_workerrrrrrrrrc tt||||p t||||| | dS)N) rrbackend_configrrrrrr)superr__init__r ) selfrrrrrrrrr __class__s i/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/train/lightgbm/v2.pyrzLightGBMTrainer.__init__hs\ ot$$--"7/*>n.>.>))!#9 . )__name__ __module__ __qualname____doc__rrrrr raytrain ScalingConfig RunConfigstrr DataConfigrrr __classcell__)rs@rrr s+XX|-148<@48489=-17;   $Xb$h%74&$,9O%OP $D>  ".1  !!89  SY01 4Z01 !!56 4S>* !) 4          rr)loggingtypingrrrrr ray.trainr$rray.train.data_parallel_trainerr ray.train.lightgbm.configr r ray.train.trainerr getLoggerr loggerrrrr4s77777777777777 ??????HHHHHHHH((((((  8 $ $r r r r r )r r r r r r