o `۷i@sddlZddlmZmZmZmZmZmZddlZ ddlm Z ddl m Z ddl mZmZddlmZddlmZddlmZerFdd lmZeeZGd d d eZdS) N) TYPE_CHECKINGAnyCallableDictOptionalUnion) Checkpoint) GenDataset) RunConfig ScalingConfig)DataParallelTrainer)ValidationConfig) DeprecatedLightGBMConfigcseZdZdZddddddddddddd deegdfeegdffdeededdeed ee d eee e fd ee j jd eed eee efdeedee deee efdeeffddZeedefddZZS)LightGBMTrainerayA 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 from ray.train.lightgbm 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 lines below are the only changes needed # for your `lgb.train` call! "tree_learner": "data_parallel", "pre_partition": True, **ray.train.lightgbm.get_network_params(), } lgb.train( params, train_set, valid_sets=[eval_set], valid_names=["eval"], num_boost_round=1, # 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=2), ) 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. 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. datasets: The Ray Datasets to ingest for training. Datasets are keyed by name (``{name: dataset}``). Each dataset can be accessed from within the ``train_loop_per_worker`` by calling ``ray.train.get_dataset_shard(name)``. Sharding and additional configuration can be done by passing in a ``dataset_config``. 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. validation_config: [Alpha] Configuration for checkpoint validation. If provided and ``ray.train.report`` is called with the ``validation`` argument, Ray Train will validate the reported checkpoint using the validation function specified in this config. resume_from_checkpoint: [Deprecated] metadata: [Deprecated] N) train_loop_configlightgbm_configscaling_config run_configdatasetsdataset_configvalidation_configmetadataresume_from_checkpoint label_columnparamsnum_boost_roundtrain_loop_per_workerrrrrrrrrrrrrrc  sZ| dus | dus | durtdddlm}tt|j|||p!|||||| | |d dS)NzThe legacy LightGBMTrainer API is deprecated. Please switch to passing in a custom `train_loop_per_worker` function instead. See this issue for more context: https://github.com/ray-project/ray/issues/50042rr) rrbackend_configrrrrrrr)DeprecationWarningray.train.lightgbmrsuperr__init__)selfrrrrrrrrrrrrrr __class__\/home/ubuntu/vllm_env/lib/python3.10/site-packages/ray/train/v2/lightgbm/lightgbm_trainer.pyr#ws&   zLightGBMTrainer.__init__ checkpointcCstd)zRetrieve the LightGBM model stored in this checkpoint. This API is deprecated. Use `RayTrainReportCallback.get_model` instead. zZ`LightGBMTrainer.get_model` is deprecated. Use `RayTrainReportCallback.get_model` instead.)r )clsr)r'r'r( get_models zLightGBMTrainer.get_model)__name__ __module__ __qualname____doc__rrrrr r strr raytrain DataConfigr rrintr# classmethodrr+ __classcell__r'r'r%r(rs\h    .r)loggingtypingrrrrrr ray.trainr1rray.train.trainerr ray.train.v2.api.configr r &ray.train.v2.api.data_parallel_trainerr "ray.train.v2.api.validation_configr ray.util.annotationsrr!r getLoggerr,loggerrr'r'r'r(s