PlacementGroupState#
- class ray.util.state.common.PlacementGroupState(placement_group_id: str, name: str, creator_job_id: str, state: Literal['PENDING', 'PREPARED', 'CREATED', 'REMOVED', 'RESCHEDULING'], bundles: List[dict] | None = None, is_detached: bool | None = None, stats: dict | None = None, topology_strategy: dict | None = None, topology_assignments: dict | None = None)[source]#
Bases:
StateSchemaPlacementGroup State
Below columns can be used for the
--filteroption.placement_group_id
state
is_detached
name
creator_job_id
Below columns are available only when
getAPI is used,--detailis specified through CLI, ordetail=Trueis given to Python APIs.placement_group_id
stats
state
topology_assignments
name
is_detached
bundles
topology_strategy
creator_job_id
- state: Literal['PENDING', 'PREPARED', 'CREATED', 'REMOVED', 'RESCHEDULING']#
The state of the placement group.
PENDING: The placement group creation is pending scheduling. It could be because there’s not enough resources, some of creation stage has failed (e.g., failed to commit placement gropus because the node is dead).
CREATED: The placement group is created.
REMOVED: The placement group is removed.
RESCHEDULING: The placement group is rescheduling because some of bundles are dead because they were on dead nodes.
- topology_strategy: dict | None = None#
a dict mapping each topology label key (e.g. “ray.io/gpu-domain”) to a placement strategy (e.g. “STRICT_PACK”). Empty dict if the placement group does not use a topology strategy.
NOTE: This field is experimental and may change in the future.
- Type:
The topology strategy for this placement group
- topology_assignments: dict | None = None#
a dict mapping each topology label key to the value the scheduler has selected for this PG (e.g. {“ray.io/gpu-domain”: “rack-1”}). Empty dict if no topology values have been selected yet.
NOTE: This field is experimental and may change in the future.
- Type:
Topology assignments