o ci @sddlZddlmZmZddlZddlmZddlm Z ddl m Z dZ edee dZeeZe dd d ed efd d Ze dd dddZdS)N)OptionalTypeVar)get_train_context) PublicAPIiT)boundalpha) stabilitydatareturncCsf|durtt|}|tkrtd|dtdt}|}t |j j | | |ddS)awBroadcast 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: :skipif: True 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. Nz Data size z- bytes exceeds the maximum broadcast size of z bytesz-ray.train.collective.broadcast_from_rank_zero world_rank world_sizer caller_method_name)lenpickledumps_MAX_BROADCAST_SIZE_BYTESloggerwarningrget_synchronization_actorraygetbroadcast_from_rank_zeroremoteget_world_rankget_world_size)r data_bytes train_context sync_actorr T/home/ubuntu/.local/lib/python3.10/site-packages/ray/train/collective/collectives.pyrs$)rcCs0t}|}t|jj||dddS)aCreate a barrier across all workers. All workers must call this method before the training function can continue. Example: .. testcode: :skipif: True 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() Nzray.train.collective.barrierr )rrrrrrrr)rrr r r!barrierQsr")r N)loggingtypingrrrray.cloudpickle cloudpickler(ray.train.v2._internal.execution.contextrray.util.annotationsrrobjectr getLogger__file__rrr"r r r r!s    =