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Add Semi-Random Weighting to AutoScaler Node Scheduler #49983

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fogerd wants to merge 3 commits into from
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Add Semi-Random Weighting to AutoScaler Node Scheduler #49983

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Add Semi-Random Weighting to AutoScaler Node Scheduler
fogerd Jan 21, 2025
b226b0e
Merge branch 'ray-project:master' into master
fogerd Feb 19, 2025
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Feb 19, 2025
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6 changes: 4 additions & 2 deletions python/ray/autoscaler/_private/resource_demand_scheduler.py
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Expand Up @@ -43,6 +43,8 @@
)
from ray.core.generated.common_pb2 import PlacementStrategy

import random

logger = logging.getLogger(__name__)

# The minimum number of nodes to launch concurrently.
Expand Down Expand Up @@ -786,8 +788,8 @@ def get_nodes_for(
)
break

utilization_scores = sorted(utilization_scores, reverse=True)
best_node_type = utilization_scores[0][1]
weights = [node_types[node_type[1]].get("max_workers", 0) for node_type in utilization_scores]
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@jjyao jjyao Jan 28, 2025

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the weights should be based on utilization_scores instead of max_workers: we don't want to launch a big machine for a 1 cpu task.

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@fogerd fogerd Feb 19, 2025

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done

best_node_type = random.choices(utilization_scores, weights=weights, k=1)[0][1]
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@jjyao jjyao Jan 28, 2025

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Ideally we should remember the node type that has no availability and skip it next time.

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@fogerd fogerd Feb 19, 2025

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agree that would be ideal but works without it. it quickly cycles through nodes so not saving much time vs the extra code for state management

nodes_to_add[best_node_type] += 1
if strict_spread:
resources = resources[1:]
Expand Down