get_local_rank

TuneContext.get_local_rank() → int

Get the local rank of this worker (rank of the worker on its node).

Examples

>>> 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())
...     ...
>>> train_dataset = ray.data.read_csv("s3://anonymous@ray-example-data/iris.csv")
>>> trainer = TorchTrainer(
...     train_loop_per_worker,
...     scaling_config=ScalingConfig(num_workers=2, use_gpu=True),
...     datasets={"train": train_dataset}
... )
>>> print("[ignore]"); trainer.fit()
[ignore]...

Returns:

This worker’s rank among the workers on its node, or 0 if called outside a Ray Train or Ray Tune function.

Return type:

int

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_local_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. See this issue for more context and migration options: ray-project/ray#49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0