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[KubeRay] support suspending worker groups in KubeRay autoscaler #49768

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[KubeRay] support suspending worker groups in KubeRay autoscaler #49768

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6 changes: 4 additions & 2 deletions python/ray/autoscaler/_private/kuberay/autoscaling_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -213,16 +213,18 @@ def _node_type_from_group_spec(
if is_head:
# The head node type has no workers because the head is not a worker.
min_workers = max_workers = 0
suspend = False
else:
# `minReplicas` and `maxReplicas` are required fields for each workerGroupSpec
min_workers = group_spec["minReplicas"]
max_workers = group_spec["maxReplicas"]
suspend = group_spec.get("suspend", False)

resources = _get_ray_resources_from_group_spec(group_spec, is_head)

node_type = {
"min_workers": min_workers,
"max_workers": max_workers,
"min_workers": min_workers if not suspend else 0,
"max_workers": max_workers if not suspend else 0,
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We need to let the autoscaler know that the suspended worker group should have no workers; otherwise, the autoscaler will keep trying to scale it up.

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Since the available_node_types definition does not provide similar functionality (suspending a node type), the best way to do that is to set its max_workers and min_workers to 0.

# `node_config` is a legacy field required for compatibility.
# Pod config data is required by the operator but not by the autoscaler.
"node_config": {},
Expand Down
26 changes: 26 additions & 0 deletions python/ray/tests/kuberay/test_autoscaling_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -140,6 +140,16 @@ def _get_basic_autoscaling_config() -> dict:
}


def _get_autoscaling_config_with_groups_suspended() -> dict:
"""The expected autoscaling with all groups suspended."""
config = _get_basic_autoscaling_config()
for _, spec in config["available_node_types"].items():
spec["max_workers"] = 0
spec["min_workers"] = 0
config["max_workers"] = 0
return config


def _get_ray_cr_no_cpu_error() -> dict:
"""Incorrectly formatted Ray CR without num-cpus rayStartParam and without resource
limits. Autoscaler should raise an error when reading this.
Expand Down Expand Up @@ -236,6 +246,14 @@ def _get_ray_cr_with_only_requests() -> dict:
return cr


def _get_ray_cr_with_groups_suspended() -> dict:
"""CR with all worker groups suspended"""
cr = get_basic_ray_cr()
for group in cr["spec"]["workerGroupSpecs"]:
group["suspend"] = True
return cr


def _get_autoscaling_config_with_options() -> dict:
config = _get_basic_autoscaling_config()
config["upscaling_speed"] = 1
Expand Down Expand Up @@ -312,6 +330,14 @@ def test_resource_quantity(input: str, output: int):
None,
id="autoscaler-options",
),
pytest.param(
_get_ray_cr_with_groups_suspended(),
_get_autoscaling_config_with_groups_suspended(),
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This tests both max_workers and min_workers are set to 0 in the generated autoscaling config.

None,
None,
None,
id="groups-suspended",
),
pytest.param(
_get_ray_cr_with_tpu_custom_resource(),
_get_basic_autoscaling_config(),
Expand Down
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