&`iddlZddlmZmZmZmZmZddlZddlm Z ddl m Z ddl m Z ddlmZejeZGdde ZdS) N)AnyCallableDictOptionalUnion) Checkpoint)DataParallelTrainer) GenDataset) XGBoostConfigcJeZdZdZddddddddddeegdfeegdffdeedeedee j j dee j j d eee efd ee j jd eee efd eeffd ZxZS)XGBoostTraineraA Trainer for distributed data-parallel XGBoost training. Example ------- .. testcode:: :skipif: True import xgboost import ray.data import ray.train from ray.train.xgboost import RayTrainReportCallback, XGBoostTrainer 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 `xgboost.DMatrix` 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"] dtrain = xgboost.DMatrix(train_X, label=train_y) deval = xgboost.DMatrix(eval_X, label=eval_y) params = { "tree_method": "approx", "objective": "reg:squarederror", "eta": 1e-4, "subsample": 0.5, "max_depth": 2, } # 2. Do distributed data-parallel training. # Ray Train sets up the necessary coordinator processes and # environment variables for your workers to communicate with each other. bst = xgboost.train( params, dtrain=dtrain, evals=[(deval, "validation")], num_boost_round=10, 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(16)]) trainer = XGBoostTrainer( 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. xgboost_config: The configuration for setting up the distributed xgboost backend. Defaults to using the "rabit" backend. See :class:`~ray.train.xgboost.XGBoostConfig` 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 Datasets 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_configxgboost_configscaling_config run_configdatasetsdataset_configmetadataresume_from_checkpointtrain_loop_per_workerrrrrrrrrc tt||||p t||||| | dS)N) rrbackend_configrrrrrr)superr __init__r ) selfrrrrrrrrr __class__s h/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/train/xgboost/v2.pyrzXGBoostTrainer.__init__isZ nd##,,"7/)<]__))!#9 - )__name__ __module__ __qualname____doc__rrrrr raytrain ScalingConfig RunConfigstrr DataConfigrrr __classcell__)rs@rr r s+YY~-126<@48489=-17;   $Xb$h%74&$,9O%OP $D>  !/  !!89  SY01 4Z01 !!56 4S>* !) 4          rr )loggingtypingrrrrr ray.trainr#rray.train.data_parallel_trainerr ray.train.trainerr ray.train.xgboostr getLoggerrloggerr rrr3s77777777777777 ??????((((((++++++  8 $ $s s s s s (s s s s s r