def _reconcile_scaling_config_with_trial_resources(
self, scaling_config: ScalingConfig) -> ScalingConfig:
"""
ResourceChangingScheduler workaround.
Ensures that the scaling config matches trial resources.
This should be replaced with RCS returning a ScalingConfig
in the future.
"""
trial_resources = self.trial_resources
# This will be false if the resources are default
if not isinstance(trial_resources, PlacementGroupFactory):
return scaling_config
if scaling_config:
scaling_config = trainer_cls._validate_scaling_config(
scaling_config)
scaling_config_from_trial_resources = (
ScalingConfig.from_placement_group_factory(trial_resources)
)
# This check should always pass if ResourceChangingScheduler is not
# used.
if scaling_config_from_trial_resources != scaling_config:
scaling_config = trainer_cls._validate_scaling_config(
scaling_config_from_trial_resources)
return scaling_config
def test_scaling_config_pgf_equivalance(trainer_resources,
resources_per_worker_and_use_gpu,
num_workers, placement_strategy):
resources_per_worker, use_gpu = resources_per_worker_and_use_gpu
scaling_config = ScalingConfig(
trainer_resources=trainer_resources,
num_workers=num_workers,
resources_per_worker=resources_per_worker,
use_gpu=use_gpu,
placement_strategy=placement_strategy,
)
pgf = scaling_config.as_placement_group_factory()
scaling_config_from_pgf = ScalingConfig.from_placement_group_factory(pgf)
assert scaling_config == scaling_config_from_pgf
assert scaling_config_from_pgf.as_placement_group_factory() == pgf
