o ci@sLddlmZmZddlmZddlmZmZm Z e ddGdddZ dS) )AnyDict)get_train_context) Deprecated DeveloperAPI PublicAPIstable) stabilityc@seZdZedeeeffddZedefddZedefddZ edd Z edefd d Z defd d Z de fddZde fddZde fddZde fddZde fddZeddZdS) TrainContextreturncCsddlm}t|)zB[Deprecated] User metadata dict passed to the Trainer constructor.r)!_GET_METADATA_DEPRECATION_MESSAGE)ray.train.contextr DeprecationWarning)selfr rL/home/ubuntu/.local/lib/python3.10/site-packages/ray/train/v2/api/context.py get_metadata s 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$rz TrainContext.get_trial_resourcescCr)a[Deprecated] Log directory corresponding to the trial directory for a Tune session. This is deprecated for Ray Train and should no longer be called in Ray Train workers. If this directory is needed, please pass it into the `train_loop_config` directly. rr get_trial_dirrrrrrr-s zTrainContext.get_trial_dircC tS)z,Experiment name for the corresponding trial.)get_internal_train_contextget_experiment_namerrrrr :s z TrainContext.get_experiment_namecCr)aGet the current world size (i.e. total number of workers) for this run. .. testcode:: import ray from ray import train from ray.train import ScalingConfig from ray.train.tensorflow import TensorflowTrainer NUM_WORKERS = 2 def train_loop_per_worker(config): assert train.get_context().get_world_size() == NUM_WORKERS trainer = TensorflowTrainer( train_loop_per_worker, scaling_config=ScalingConfig(num_workers=NUM_WORKERS), ) trainer.fit() .. testoutput:: :hide: ... )rget_world_sizer!rrrr"> zTrainContext.get_world_sizecCr)arGet the world rank of this worker. .. testcode:: import ray from ray import train from ray.train import ScalingConfig from ray.train.tensorflow import TensorflowTrainer def train_loop_per_worker(config): if train.get_context().get_world_rank() == 0: print("Worker 0") trainer = TensorflowTrainer( train_loop_per_worker, scaling_config=ScalingConfig(num_workers=2), ) trainer.fit() .. testoutput:: :hide: ... )rget_world_rankr!rrrr$Zs zTrainContext.get_world_rankcCr)aGet the local rank of this worker (rank of the worker on its node). .. testcode:: import torch import ray from ray import train from ray.train import ScalingConfig from ray.train.torch import TorchTrainer def train_loop_per_worker(config): if torch.cuda.is_available(): torch.cuda.set_device(train.get_context().get_local_rank()) ... trainer = TorchTrainer( train_loop_per_worker, scaling_config=ScalingConfig(num_workers=2, use_gpu=True), ) trainer.fit() .. testoutput:: :hide: ... )rget_local_rankr!rrrr%us zTrainContext.get_local_rankcCr)aGet the local world size of this node (i.e. number of workers on this node). Example: .. testcode:: import ray from ray import train from ray.train import ScalingConfig from ray.train.torch import TorchTrainer def train_loop_per_worker(): print(train.get_context().get_local_world_size()) trainer = TorchTrainer( train_loop_per_worker, scaling_config=ScalingConfig(num_workers=1), ) trainer.fit() .. testoutput:: :hide: ... )rget_local_world_sizer!rrrr&r#z!TrainContext.get_local_world_sizecCr)azGet the rank of this node. Example: .. testcode:: import ray from ray import train from ray.train import ScalingConfig from ray.train.torch import TorchTrainer def train_loop_per_worker(): print(train.get_context().get_node_rank()) trainer = TorchTrainer( train_loop_per_worker, scaling_config=ScalingConfig(num_workers=1), ) trainer.fit() .. testoutput:: :hide: ... )r get_node_rankr!rrrr'r#zTrainContext.get_node_rankcCr)aFReturns 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 developer API, and the `StorageContext` interface may change without notice between minor versions. )r get_storager!rrrr(s zTrainContext.get_storageN)__name__ __module__ __qualname__rrstrrrrrrrr intr"r$r%r&r'rr(rrrrr s&  r N) typingrr(ray.train.v2._internal.execution.contextrrray.util.annotationsrrrr rrrrs