get_node_rank#

TuneContext.get_node_rank() → int[source]#

Get the rank of this node.

Examples

>>> 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())
>>> train_dataset = ray.data.from_items(
...     [{"x": x, "y": x + 1} for x in range(32)])
>>> trainer = TorchTrainer(train_loop_per_worker,
...     scaling_config=ScalingConfig(num_workers=1),
...     datasets={"train": train_dataset})
>>> print("[ignore]"); trainer.fit()
[ignore]...

Returns:: The rank of this node within the cluster.
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_node_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