&`iddlZddlmZmZmZmZmZmZddlZ ddlm Z ddl m Z ddl mZmZddlmZddlmZerddlmZejeZGd d eZdS) N) TYPE_CHECKINGAnyCallableDictOptionalUnion) Checkpoint) GenDataset) RunConfig ScalingConfig)DataParallelTrainer) Deprecated XGBoostConfigceZdZdZdddddddddddd deegdfeegdffdeededdeed ee d eee e fd ee j jd eee efd eedee deee efdeeffdZeedefdZxZS)XGBoostTrainera^A Trainer for distributed data-parallel XGBoost training. Example ------- .. testcode:: import xgboost import ray.data import ray.train from ray.train.xgboost import RayTrainReportCallback from ray.train.xgboost import 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=1, 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=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. 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. 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. resume_from_checkpoint: [Deprecated] metadata: [Deprecated] N) train_loop_configxgboost_configscaling_config run_configdatasetsdataset_configmetadataresume_from_checkpoint label_columnparamsnum_boost_roundtrain_loop_per_workerrrrrrrrrrrrrc | | | tdddlm} tt||||p | ||||| | dS)NzThe legacy XGBoostTrainer 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_configrrrrrr)DeprecationWarningray.train.xgboostrsuperr__init__)selfrrrrrrrrrrrrr __class__s x/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/train/v2/xgboost/xgboost_trainer.pyr$zXGBoostTrainer.__init__ns&  $!*$B  433333 nd##,,"7/)<]]__))!#9 - checkpointc td)zB[Deprecated] Retrieve the XGBoost model stored in this checkpoint.zY`XGBoostTrainer.get_model` is deprecated. Use `RayTrainReportCallback.get_model` instead.)r!)clsr)s r' get_modelzXGBoostTrainer.get_models! >   r()__name__ __module__ __qualname____doc__rrrrr r strr raytrain DataConfigrr intr$ classmethodrr, __classcell__)r&s@r'rrsZZ@-14826*.489=-17;&*+/)-!+ + + $Xb$h%74&$,9O%OP+ $D> + !1 + !/ + Y'+ 4Z01+ !!56+ 4S>*+ !) 4+ sm+ c3h(+ "#!+ + + + + + Z :   Z[     r(r)loggingtypingrrrrrr ray.trainr2r ray.train.trainerr ray.train.v2.api.configr r &ray.train.v2.api.data_parallel_trainerr ray.util.annotationsrr"r getLoggerr-loggerrr(r'rBsFFFFFFFFFFFFFFFF ((((((<<<<<<<<FFFFFF++++++0//////  8 $ $Q Q Q Q Q (Q Q Q Q Q r(