o 1 i@sddlmZmZddlmZmZddlmZddl m Z m Z m Z e ddGdddeZ e Gd d d e Ze Gd d d e Zd S))ABCabstractmethod)AnyDict)get_train_context) Deprecated DeveloperAPI PublicAPIZstable)Z stabilityc@seZdZdZedeeeffddZedefddZ edefddZ ed d Z edefd d Z e defd dZe defddZe defddZe defddZe defddZe defddZee ddZdS) TrainContextz(Abstract interface for training context.returncCsddlm}t|)zB[Deprecated] User metadata dict passed to the Trainer constructor.r)!_GET_METADATA_DEPRECATION_MESSAGE)ray.train.contextr DeprecationWarning)selfr rd/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/ray/train/v2/api/context.py get_metadatas zTrainContext.get_metadatacCddlm}t|d)z4[Deprecated] Trial name for the corresponding trial.r*_TUNE_SPECIFIC_CONTEXT_DEPRECATION_MESSAGEget_trial_namer rrformatrrrrrr zTrainContext.get_trial_namecCr)z2[Deprecated] Trial id for the corresponding trial.rr get_trial_idrrrrrrrzTrainContext.get_trial_idcCr)z9[Deprecated] Trial resources for the corresponding trial.rrget_trial_resourcesrrrrrr'rz TrainContext.get_trial_resourcescCr)a[Deprecated] Log directory corresponding to the trial directory for a Tune session. This is deprecated for Ray Train and should no longer be called in Ray Train workers. If this directory is needed, please pass it into the `train_loop_config` directly. rr get_trial_dirrrrrrr0s zTrainContext.get_trial_dircCdS)z,Experiment name for the corresponding trial.Nrrrrrget_experiment_name=sz TrainContext.get_experiment_namecCr)aGet the current world size (i.e. total number of workers) for this run. .. testcode:: import ray.train from ray.train.torch import TorchTrainer NUM_WORKERS = 2 def train_fn_per_worker(config): assert ray.train.get_context().get_world_size() == NUM_WORKERS trainer = TorchTrainer( train_fn_per_worker, scaling_config=ray.train.ScalingConfig(num_workers=NUM_WORKERS), ) trainer.fit() Nrrrrrget_world_sizeBzTrainContext.get_world_sizecCr)aGet the world rank of this worker. .. testcode:: import ray.train from ray.train.torch import TorchTrainer def train_fn_per_worker(config): if ray.train.get_context().get_world_rank() == 0: print("Worker 0") trainer = TorchTrainer( train_fn_per_worker, scaling_config=ray.train.ScalingConfig(num_workers=2), ) trainer.fit() Nrrrrrget_world_rankYzTrainContext.get_world_rankcCr)aGet the local rank of this worker (rank of the worker on its node). .. testcode:: import ray.train from ray.train.torch import TorchTrainer def train_fn_per_worker(config): if ray.train.get_context().get_local_rank() == 0: print("Local rank 0 worker") trainer = TorchTrainer( train_fn_per_worker, scaling_config=ray.train.ScalingConfig(num_workers=2), ) trainer.fit() Nrrrrrget_local_rankor$zTrainContext.get_local_rankcCr)a!Get the local world size of this node (i.e. number of workers on this node). Example: .. testcode:: import ray.train from ray.train.torch import TorchTrainer def train_fn_per_worker(): print(ray.train.get_context().get_local_world_size()) trainer = TorchTrainer( train_fn_per_worker, scaling_config=ray.train.ScalingConfig(num_workers=2), ) trainer.fit() Nrrrrrget_local_world_sizer"z!TrainContext.get_local_world_sizecCr)aGet the rank of this node. Example: .. testcode:: import ray.train from ray.train.torch import TorchTrainer def train_fn_per_worker(): print(ray.train.get_context().get_node_rank()) trainer = TorchTrainer( train_fn_per_worker, scaling_config=ray.train.ScalingConfig(num_workers=1), ) trainer.fit() Nrrrrr get_node_rankr"zTrainContext.get_node_rankcCr)aEReturns the :class:`~ray.train._internal.storage.StorageContext` storage context which gives advanced access to the filesystem and paths configured through `RunConfig`. NOTE: This is a DeveloperAPI, and the `StorageContext` interface may change without notice between minor versions. 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