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[train] fix Train/Tune integration on Client (#20351)

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* [train] fix Train/Tune integration on Client

* remove force_on_current_node
This commit is contained in:
matthewdeng 2021-11-15 14:36:33 -08:00 • committed by GitHub
parent 884bb3de33
commit 35dc3cf21b
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1 changed files with 6 additions and 11 deletions
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17
python/ray/train/trainer.py
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@ -21,8 +21,6 @@ from ray.train.constants import TUNE_INSTALLED, DEFAULT_RESULTS_DIR, \
# Ray Train should be usable even if Tune is not installed.
from ray.train.utils import construct_path
from ray.train.worker_group import WorkerGroup
from ray.util.ml_utils.node import force_on_current_node, \
get_current_node_resource_key
if TUNE_INSTALLED:
from ray import tune
@ -144,11 +142,6 @@ class Trainer:
remote_executor = ray.remote(num_cpus=0)(BackendExecutor)
if not ray.is_initialized():
ray.init()
# Assign BackendExecutor to head node.
remote_executor = force_on_current_node(remote_executor)
self._backend_executor_actor = remote_executor.remote(
backend_config=self._backend_config,
num_workers=num_workers,
@ -157,7 +150,7 @@ class Trainer:
additional_resources_per_worker=resources_per_worker,
max_retries=max_retries)
if tune is not None and tune.is_session_enabled():
if self._is_tune_enabled():
self.checkpoint_manager = TuneCheckpointManager()
else:
self.checkpoint_manager = CheckpointManager()
@ -203,6 +196,10 @@ class Trainer:
else:
raise TypeError(f"Invalid type for backend: {type(backend)}.")
def _is_tune_enabled(self):
"""Whether or not this Trainer is part of a Tune session."""
return tune is not None and tune.is_session_enabled()
def start(self, initialization_hook: Optional[Callable[[], None]] = None):
"""Starts the training execution service.
@ -790,9 +787,7 @@ def _create_tune_trainable(train_func, dataset, backend_config, num_workers,
@classmethod
def default_resource_request(cls,
config: Dict) -> PlacementGroupFactory:
node_resource_key = get_current_node_resource_key()
trainer_bundle = [{"CPU": 1}]
backend_executor_bundle = [{node_resource_key: 0.01}]
worker_resources = {"CPU": 1, "GPU": int(use_gpu)}
worker_resources_extra = {} if resources_per_worker is None else \
resources_per_worker
@ -800,7 +795,7 @@ def _create_tune_trainable(train_func, dataset, backend_config, num_workers,
**worker_resources,
**worker_resources_extra
} for _ in range(num_workers)]
bundles = trainer_bundle + backend_executor_bundle + worker_bundles
bundles = trainer_bundle + worker_bundles
return PlacementGroupFactory(bundles, strategy="PACK")
return TrainTrainable