ray.tune.TuneContext.get_world_rank#

TuneContext.get_world_rank() → int[source]#

Get the world rank of this worker.

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")

train_dataset = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
trainer = TensorflowTrainer(
    train_loop_per_worker,
    scaling_config=ScalingConfig(num_workers=2),
    datasets={"train": train_dataset}
)
trainer.fit()

PublicAPI (beta): This API is in beta and may change before becoming stable.

Warning

DEPRECATED: This API is deprecated and may be removed in future Ray releases. get_world_rank is deprecated for Ray Tune because there is no concept of worker ranks for Ray Tune, so these methods only make sense to use in the context of a Ray Train worker.