&`ie (ddlZddlmZmZddlmZddlmZddlm Z edee Z ej e Ze d ed e d e fd Ze d edd ZdS)N)OptionalTypeVar)get_train_fn_utils)requires_train_worker) PublicAPIT)boundalpha) stabilitydatareturncDt|S)a]Broadcast small (<1kb) data from the rank 0 worker to all other workers. Serves as a barrier, meaning that all workers must call this method before the training function can continue. Example: .. testcode: from ray.train import get_context from ray.train.collective import broadcast_from_rank_zero from ray.train.torch import TorchTrainer def train_func(): ... if get_context().get_world_rank() == 0: data = {"some_key": "some_value"} else: data = None data = broadcast_from_rank_zero(data) ... trainer = TorchTrainer(train_func) trainer.fit() Args: data: The small (1kb) data to broadcast from the rank 0 worker to all other workers. Returns: The data broadcasted from the rank 0 worker. Raises: ValueError: If the data is too big. pickle.PicklingError: If the data is not pickleable. TypeError: If the data is not pickleable. )rbroadcast_from_rank_zero)r s t/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/train/collective/collectives.pyrrsP    8 8 > >>cBtS)aCreate a barrier across all workers. All workers must call this method before the training function can continue. Example: .. testcode: from ray.train import get_context from ray.train.collective import barrier from ray.train.torch import TorchTrainer def train_func(): ... print(f"Rank {get_context().get_world_rank()} is waiting at the barrier.") barrier() print(f"Rank {get_context().get_world_rank()} has passed the barrier.") ... trainer = TorchTrainer(train_func) trainer.fit() )rbarrierrrrr9s2    ' ' ) ))r)r N)loggingtypingrr/ray.train.v2._internal.execution.train_fn_utilsrray.train.v2._internal.utilrray.util.annotationsrobjectr getLogger__file__loggerrrrrrrs&$$$$$$$$NNNNNN======****** GCx'(((  8 $ $ W&?1&?&?&?&?&?R W******r