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ray/python/ray/tests/test_resource_demand_scheduler.py
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import pytest
import time
import yaml
import tempfile
import shutil
import unittest
import copy
import ray
from ray.tests.test_autoscaler import SMALL_CLUSTER, MockProvider, \
MockProcessRunner
from ray.autoscaler._private.providers import (_NODE_PROVIDERS,
_clear_provider_cache)
from ray.autoscaler._private.autoscaler import StandardAutoscaler
from ray.autoscaler._private.load_metrics import LoadMetrics
from ray.autoscaler._private.commands import get_or_create_head_node
from ray.autoscaler._private.resource_demand_scheduler import \
_utilization_score, _add_min_workers_nodes, \
get_bin_pack_residual, get_nodes_for, ResourceDemandScheduler
from ray.gcs_utils import PlacementGroupTableData
from ray.core.generated.common_pb2 import Bundle, PlacementStrategy
from ray.autoscaler.tags import TAG_RAY_USER_NODE_TYPE, TAG_RAY_NODE_KIND, \
NODE_KIND_WORKER, TAG_RAY_NODE_STATUS, \
STATUS_UP_TO_DATE, STATUS_UNINITIALIZED
from ray.test_utils import same_elements
from time import sleep
TYPES_A = {
"empty_node": {
"node_config": {
"FooProperty": 42,
},
"resources": {},
"max_workers": 0,
},
"m4.large": {
"node_config": {},
"resources": {
"CPU": 2
},
"max_workers": 10,
},
"m4.4xlarge": {
"node_config": {},
"resources": {
"CPU": 16
},
"max_workers": 8,
},
"m4.16xlarge": {
"node_config": {},
"resources": {
"CPU": 64
},
"max_workers": 4,
},
"p2.xlarge": {
"node_config": {},
"resources": {
"CPU": 16,
"GPU": 1
},
"max_workers": 10,
},
"p2.8xlarge": {
"node_config": {},
"resources": {
"CPU": 32,
"GPU": 8
},
"max_workers": 4,
},
}
MULTI_WORKER_CLUSTER = dict(
SMALL_CLUSTER, **{
"available_node_types": TYPES_A,
"head_node_type": "empty_node",
"worker_default_node_type": "m4.large",
})
def test_util_score():
assert _utilization_score({"CPU": 64}, [{"TPU": 16}]) is None
assert _utilization_score({"GPU": 4}, [{"GPU": 2}]) == (0.5, 0.5)
assert _utilization_score({"GPU": 4}, [{"GPU": 1}, {"GPU": 1}]) == \
(0.5, 0.5)
assert _utilization_score({"GPU": 2}, [{"GPU": 2}]) == (2, 2)
assert _utilization_score({"GPU": 2}, [{"GPU": 1}, {"GPU": 1}]) == (2, 2)
assert _utilization_score({"GPU": 2, "TPU": 1}, [{"GPU": 2}]) == (0, 1)
assert _utilization_score({"CPU": 64}, [{"CPU": 64}]) == (64, 64)
assert _utilization_score({"CPU": 64}, [{"CPU": 32}]) == (8, 8)
assert _utilization_score({"CPU": 64}, [{"CPU": 16}, {"CPU": 16}]) == \
(8, 8)
def test_bin_pack():
assert get_bin_pack_residual([], [{"GPU": 2}, {"GPU": 2}])[0] == \
[{"GPU": 2}, {"GPU": 2}]
assert get_bin_pack_residual([{"GPU": 2}], [{"GPU": 2}, {"GPU": 2}])[0] \
== [{"GPU": 2}]
assert get_bin_pack_residual([{
"GPU": 4
}], [{
"GPU": 2
}, {
"GPU": 2
}])[0] == []
arg = [{"GPU": 2}, {"GPU": 2, "CPU": 2}]
assert get_bin_pack_residual(arg, [{"GPU": 2}, {"GPU": 2}])[0] == []
arg = [{"CPU": 2}, {"GPU": 2}]
assert get_bin_pack_residual(arg, [{
"GPU": 2
}, {
"GPU": 2
}])[0] == [{
"GPU": 2
}]
arg = [{"GPU": 3}]
assert get_bin_pack_residual(
arg, [{
"GPU": 1
}, {
"GPU": 1
}], strict_spread=False)[0] == []
assert get_bin_pack_residual(
arg, [{
"GPU": 1
}, {
"GPU": 1
}], strict_spread=True) == ([{
"GPU": 1
}], [{
"GPU": 2
}])
def test_get_nodes_packing_heuristic():
assert get_nodes_for(TYPES_A, {}, 9999, [{"GPU": 8}]) == \
{"p2.8xlarge": 1}
assert get_nodes_for(TYPES_A, {}, 9999, [{"GPU": 1}] * 6) == \
{"p2.8xlarge": 1}
assert get_nodes_for(TYPES_A, {}, 9999, [{"GPU": 1}] * 4) == \
{"p2.xlarge": 4}
assert get_nodes_for(TYPES_A, {}, 9999, [{"CPU": 32, "GPU": 1}] * 3) \
== {"p2.8xlarge": 3}
assert get_nodes_for(TYPES_A, {}, 9999, [{"CPU": 64, "GPU": 1}] * 3) \
== {}
assert get_nodes_for(TYPES_A, {}, 9999, [{"CPU": 64}] * 3) == \
{"m4.16xlarge": 3}
assert get_nodes_for(TYPES_A, {}, 9999, [{"CPU": 64}, {"CPU": 1}]) \
== {"m4.16xlarge": 1, "m4.large": 1}
assert get_nodes_for(
TYPES_A, {}, 9999, [{"CPU": 64}, {"CPU": 9}, {"CPU": 9}]) == \
{"m4.16xlarge": 1, "m4.4xlarge": 2}
assert get_nodes_for(TYPES_A, {}, 9999, [{"CPU": 16}] * 5) == \
{"m4.16xlarge": 1, "m4.4xlarge": 1}
assert get_nodes_for(TYPES_A, {}, 9999, [{"CPU": 8}] * 10) == \
{"m4.16xlarge": 1, "m4.4xlarge": 1}
assert get_nodes_for(TYPES_A, {}, 9999, [{"CPU": 1}] * 100) == \
{"m4.16xlarge": 1, "m4.4xlarge": 2, "m4.large": 2}
assert get_nodes_for(
TYPES_A, {}, 9999, [{"GPU": 1}] + ([{"CPU": 1}] * 64)) == \
{"m4.16xlarge": 1, "p2.xlarge": 1}
assert get_nodes_for(
TYPES_A, {}, 9999, ([{"GPU": 1}] * 8) + ([{"CPU": 1}] * 64)) == \
{"m4.16xlarge": 1, "p2.8xlarge": 1}
assert get_nodes_for(
TYPES_A, {}, 9999, [{
"GPU": 1
}] * 8, strict_spread=False) == {
"p2.8xlarge": 1
}
assert get_nodes_for(
TYPES_A, {}, 9999, [{
"GPU": 1
}] * 8, strict_spread=True) == {
"p2.xlarge": 8
}
def test_get_nodes_respects_max_limit():
types = {
"m4.large": {
"resources": {
"CPU": 2
},
"max_workers": 10,
},
"gpu": {
"resources": {
"GPU": 1
},
"max_workers": 99999,
},
}
assert get_nodes_for(types, {}, 2, [{"CPU": 1}] * 10) == \
{"m4.large": 2}
assert get_nodes_for(types, {"m4.large": 9999}, 9999, [{
"CPU": 1
}] * 10) == {}
assert get_nodes_for(types, {"m4.large": 0}, 9999, [{
"CPU": 1
}] * 10) == {
"m4.large": 5
}
assert get_nodes_for(types, {"m4.large": 7}, 4, [{
"CPU": 1
}] * 10) == {
"m4.large": 3
}
assert get_nodes_for(types, {"m4.large": 7}, 2, [{
"CPU": 1
}] * 10) == {
"m4.large": 2
}
def test_add_min_workers_nodes():
types = {
"m2.large": {
"resources": {
"CPU": 2
},
"min_workers": 50,
"max_workers": 100,
},
"m4.large": {
"resources": {
"CPU": 2
},
"min_workers": 0,
"max_workers": 10,
},
"gpu": {
"resources": {
"GPU": 1
},
"min_workers": 99999,
"max_workers": 99999,
},
}
assert _add_min_workers_nodes([],
{},
types) == \
([{"CPU": 2}]*50+[{"GPU": 1}]*99999, {"m2.large": 50, "gpu": 99999},
{"m2.large": 50, "gpu": 99999})
assert _add_min_workers_nodes([{"CPU": 2}]*5,
{"m2.large": 5},
types) == \
([{"CPU": 2}]*50+[{"GPU": 1}]*99999, {"m2.large": 50, "gpu": 99999},
{"m2.large": 45, "gpu": 99999})
assert _add_min_workers_nodes([{"CPU": 2}]*60,
{"m2.large": 60},
types) == \
([{"CPU": 2}]*60+[{"GPU": 1}]*99999, {"m2.large": 60, "gpu": 99999},
{"gpu": 99999})
assert _add_min_workers_nodes([{
"CPU": 2
}] * 50 + [{
"GPU": 1
}] * 99999, {
"m2.large": 50,
"gpu": 99999
}, types) == ([{
"CPU": 2
}] * 50 + [{
"GPU": 1
}] * 99999, {
"m2.large": 50,
"gpu": 99999
}, {})
def test_get_nodes_to_launch_with_min_workers():
provider = MockProvider()
new_types = copy.deepcopy(TYPES_A)
new_types["p2.8xlarge"]["min_workers"] = 2
scheduler = ResourceDemandScheduler(provider, new_types, 3)
provider.create_node({}, {TAG_RAY_USER_NODE_TYPE: "p2.8xlarge"}, 1)
nodes = provider.non_terminated_nodes({})
ips = provider.non_terminated_node_ips({})
utilizations = {ip: {"GPU": 8} for ip in ips}
to_launch = scheduler.get_nodes_to_launch(nodes, {}, [{
"GPU": 8
}], utilizations, [])
assert to_launch == {"p2.8xlarge": 1}
def test_get_nodes_to_launch_with_min_workers_and_bin_packing():
provider = MockProvider()
new_types = copy.deepcopy(TYPES_A)
new_types["p2.8xlarge"]["min_workers"] = 2
scheduler = ResourceDemandScheduler(provider, new_types, 10)
provider.create_node({}, {TAG_RAY_USER_NODE_TYPE: "p2.8xlarge"}, 1)
nodes = provider.non_terminated_nodes({})
ips = provider.non_terminated_node_ips({})
# 1 free p2.8xls
utilizations = {ip: {"GPU": 8} for ip in ips}
# 1 more on the way
pending_nodes = {"p2.8xlarge": 1}
# requires 2 p2.8xls (only 2 are in cluster/pending) and 1 p2.xlarge
demands = [{"GPU": 8}] * (len(utilizations) + 1) + [{"GPU": 1}]
to_launch = scheduler.get_nodes_to_launch(nodes, pending_nodes, demands,
utilizations, [])
assert to_launch == {"p2.xlarge": 1}
# 3 min_workers of p2.8xlarge covers the 2 p2.8xlarge + 1 p2.xlarge demand.
# 2 p2.8xlarge are running/pending. So we need 1 more p2.8xlarge only to
# meet the min_workers constraint and the demand.
new_types["p2.8xlarge"]["min_workers"] = 3
scheduler = ResourceDemandScheduler(provider, new_types, 10)
to_launch = scheduler.get_nodes_to_launch(nodes, pending_nodes, demands,
utilizations, [])
# Make sure it does not return [("p2.8xlarge", 1), ("p2.xlarge", 1)]
assert to_launch == {"p2.8xlarge": 1}
def test_get_nodes_to_launch_limits():
provider = MockProvider()
scheduler = ResourceDemandScheduler(provider, TYPES_A, 3)
provider.create_node({}, {TAG_RAY_USER_NODE_TYPE: "p2.8xlarge"}, 2)
nodes = provider.non_terminated_nodes({})
ips = provider.non_terminated_node_ips({})
utilizations = {ip: {"GPU": 8} for ip in ips}
to_launch = scheduler.get_nodes_to_launch(nodes, {"p2.8xlarge": 1}, [{
"GPU": 8
}] * 2, utilizations, [])
assert to_launch == {}
def test_calculate_node_resources():
provider = MockProvider()
scheduler = ResourceDemandScheduler(provider, TYPES_A, 10)
provider.create_node({}, {TAG_RAY_USER_NODE_TYPE: "p2.8xlarge"}, 2)
nodes = provider.non_terminated_nodes({})
ips = provider.non_terminated_node_ips({})
# 2 free p2.8xls
utilizations = {ip: {"GPU": 8} for ip in ips}
# 1 more on the way
pending_nodes = {"p2.8xlarge": 1}
# requires 4 p2.8xls (only 3 are in cluster/pending)
demands = [{"GPU": 8}] * (len(utilizations) + 2)
to_launch = scheduler.get_nodes_to_launch(nodes, pending_nodes, demands,
utilizations, [])
assert to_launch == {"p2.8xlarge": 1}
class TestPlacementGroupScaling:
def test_strategies(self):
provider = MockProvider()
scheduler = ResourceDemandScheduler(provider, TYPES_A, 10)
provider.create_node({}, {TAG_RAY_USER_NODE_TYPE: "p2.8xlarge"}, 2)
# At this point our cluster has 2 p2.8xlarge instances (16 GPUs) and is
# fully idle.
nodes = provider.non_terminated_nodes({})
resource_demands = [{"GPU": 4}] * 2
pending_placement_groups = [
# Requires a new node (only uses 2 GPUs on it though).
PlacementGroupTableData(
state=PlacementGroupTableData.PENDING,
strategy=PlacementStrategy.STRICT_SPREAD,
bundles=[
Bundle(unit_resources={"GPU": 2}),
Bundle(unit_resources={"GPU": 2}),
Bundle(unit_resources={"GPU": 2})
]),
# Requires a new node (uses the whole node).
PlacementGroupTableData(
state=PlacementGroupTableData.PENDING,
strategy=PlacementStrategy.STRICT_PACK,
bundles=([Bundle(unit_resources={"GPU": 2})] * 4)),
# Fits across the machines that strict spread.
PlacementGroupTableData(
# runs on.
state=PlacementGroupTableData.PENDING,
strategy=PlacementStrategy.PACK,
bundles=([Bundle(unit_resources={"GPU": 2})] * 2)),
# Fits across the machines that strict spread.
PlacementGroupTableData(
# runs on.
state=PlacementGroupTableData.PENDING,
strategy=PlacementStrategy.SPREAD,
bundles=([Bundle(unit_resources={"GPU": 2})] * 2)),
]
to_launch = scheduler.get_nodes_to_launch(nodes, {}, resource_demands,
{}, pending_placement_groups)
assert to_launch == {"p2.8xlarge": 2}
def test_many_strict_spreads(self):
provider = MockProvider()
scheduler = ResourceDemandScheduler(provider, TYPES_A, 10)
provider.create_node({}, {TAG_RAY_USER_NODE_TYPE: "p2.8xlarge"}, 2)
# At this point our cluster has 2 p2.8xlarge instances (16 GPUs) and is
# fully idle.
nodes = provider.non_terminated_nodes({})
resource_demands = [{"GPU": 1}] * 6
pending_placement_groups = [
# Requires a new node (only uses 2 GPUs on it though).
PlacementGroupTableData(
state=PlacementGroupTableData.PENDING,
strategy=PlacementStrategy.STRICT_SPREAD,
bundles=[Bundle(unit_resources={"GPU": 2})] * 3),
]
# Each placement group will take up 2 GPUs per node, but the distinct
# placement groups should still reuse the same nodes.
pending_placement_groups = pending_placement_groups * 3
to_launch = scheduler.get_nodes_to_launch(nodes, {}, resource_demands,
{}, pending_placement_groups)
assert to_launch == {"p2.8xlarge": 1}
def test_packing(self):
provider = MockProvider()
scheduler = ResourceDemandScheduler(provider, TYPES_A, 10)
provider.create_node({}, {TAG_RAY_USER_NODE_TYPE: "p2.8xlarge"}, 1)
# At this point our cluster has 1 p2.8xlarge instances (8 GPUs) and is
# fully idle.
nodes = provider.non_terminated_nodes({})
resource_demands = [{"GPU": 1}] * 2
pending_placement_groups = [
PlacementGroupTableData(
state=PlacementGroupTableData.PENDING,
strategy=PlacementStrategy.STRICT_PACK,
bundles=[Bundle(unit_resources={"GPU": 2})] * 3),
]
# The 2 resource demand gpus should still be packed onto the same node
# as the 6 GPU placement group.
to_launch = scheduler.get_nodes_to_launch(nodes, {}, resource_demands,
{}, pending_placement_groups)
assert to_launch == {}
def test_get_concurrent_resource_demand_to_launch():
node_types = copy.deepcopy(TYPES_A)
node_types["p2.8xlarge"]["min_workers"] = 1
node_types["p2.8xlarge"]["max_workers"] = 10
node_types["m4.large"]["min_workers"] = 2
node_types["m4.large"]["max_workers"] = 100
provider = MockProvider()
scheduler = ResourceDemandScheduler(provider, node_types, 200)
# Sanity check.
assert len(provider.non_terminated_nodes({})) == 0
# Sanity check.
updated_to_launch = \
scheduler._get_concurrent_resource_demand_to_launch({}, [], {})
assert updated_to_launch == {}
provider.create_node({}, {
TAG_RAY_USER_NODE_TYPE: "p2.8xlarge",
TAG_RAY_NODE_KIND: NODE_KIND_WORKER,
TAG_RAY_NODE_STATUS: STATUS_UNINITIALIZED
}, 1)
provider.create_node({}, {
TAG_RAY_USER_NODE_TYPE: "m4.large",
TAG_RAY_NODE_KIND: NODE_KIND_WORKER,
TAG_RAY_NODE_STATUS: STATUS_UNINITIALIZED
}, 2)
# All nodes so far are pending/launching here.
to_launch = {"p2.8xlarge": 4, "m4.large": 40}
non_terminated_nodes = provider.non_terminated_nodes({})
pending_launches_nodes = {"p2.8xlarge": 1, "m4.large": 1}
updated_to_launch = \
scheduler._get_concurrent_resource_demand_to_launch(
to_launch, non_terminated_nodes, pending_launches_nodes)
# Note: we have 2 pending/launching gpus, 3 pending/launching cpus,
# 0 running gpu, and 0 running cpus.
assert updated_to_launch == {"p2.8xlarge": 3, "m4.large": 2}
# This starts the min workers only, so we have no more pending workers.
# The workers here are either running or in pending_launches_nodes,
# which is "launching".
for node_id in non_terminated_nodes:
provider.set_node_tags(node_id,
{TAG_RAY_NODE_STATUS: STATUS_UP_TO_DATE})
updated_to_launch = \
scheduler._get_concurrent_resource_demand_to_launch(
to_launch, non_terminated_nodes, pending_launches_nodes)
# Note that here we have 1 launching gpu, 1 launching cpu,
# 1 running gpu, and 2 running cpus.
assert updated_to_launch == {"p2.8xlarge": 4, "m4.large": 4}
# Launch the nodes. Note, after create_node the node is pending.
provider.create_node({}, {
TAG_RAY_USER_NODE_TYPE: "p2.8xlarge",
TAG_RAY_NODE_KIND: NODE_KIND_WORKER,
TAG_RAY_NODE_STATUS: STATUS_UNINITIALIZED
}, 5)
provider.create_node({}, {
TAG_RAY_USER_NODE_TYPE: "m4.large",
TAG_RAY_NODE_KIND: NODE_KIND_WORKER,
TAG_RAY_NODE_STATUS: STATUS_UNINITIALIZED
}, 5)
# Continue scaling.
non_terminated_nodes = provider.non_terminated_nodes({})
to_launch = {"m4.large": 36} # No more gpus are necessary
pending_launches_nodes = {} # No pending launches
updated_to_launch = \
scheduler._get_concurrent_resource_demand_to_launch(
to_launch, non_terminated_nodes, pending_launches_nodes)
# Note: we have 5 pending cpus. So we are not allowed to start any.
# Still only 2 running cpus.
assert updated_to_launch == {}
for node_id in non_terminated_nodes:
provider.set_node_tags(node_id,
{TAG_RAY_NODE_STATUS: STATUS_UP_TO_DATE})
updated_to_launch = \
scheduler._get_concurrent_resource_demand_to_launch(
to_launch, non_terminated_nodes, pending_launches_nodes)
# Note: that here we have 7 running cpus and nothing pending/launching.
assert updated_to_launch == {"m4.large": 7}
# Launch the nodes. Note, after create_node the node is pending.
provider.create_node({}, {
TAG_RAY_USER_NODE_TYPE: "m4.large",
TAG_RAY_NODE_KIND: NODE_KIND_WORKER,
TAG_RAY_NODE_STATUS: STATUS_UNINITIALIZED
}, 7)
# Continue scaling.
non_terminated_nodes = provider.non_terminated_nodes({})
to_launch = {"m4.large": 29}
pending_launches_nodes = {"m4.large": 1}
updated_to_launch = \
scheduler._get_concurrent_resource_demand_to_launch(
to_launch, non_terminated_nodes, pending_launches_nodes)
# Note: we have 8 pending/launching cpus and only 7 running.
# So we should not launch anything (8 < 7).
assert updated_to_launch == {}
for node_id in non_terminated_nodes:
provider.set_node_tags(node_id,
{TAG_RAY_NODE_STATUS: STATUS_UP_TO_DATE})
updated_to_launch = \
scheduler._get_concurrent_resource_demand_to_launch(
to_launch, non_terminated_nodes, pending_launches_nodes)
# Note: that here we have 14 running cpus and 1 launching.
assert updated_to_launch == {"m4.large": 13}
def test_get_nodes_to_launch_max_launch_concurrency():
provider = MockProvider()
new_types = copy.deepcopy(TYPES_A)
new_types["p2.8xlarge"]["min_workers"] = 4
new_types["p2.8xlarge"]["max_workers"] = 40
scheduler = ResourceDemandScheduler(provider, new_types, 30)
to_launch = scheduler.get_nodes_to_launch([], {}, [], [], [])
# Respects min_workers despite concurrency limitation.
assert to_launch == {"p2.8xlarge": 4}
provider.create_node({}, {
TAG_RAY_USER_NODE_TYPE: "p2.8xlarge",
TAG_RAY_NODE_STATUS: STATUS_UNINITIALIZED
}, 1)
nodes = provider.non_terminated_nodes({})
ips = provider.non_terminated_node_ips({})
utilizations = {ip: {"GPU": 8} for ip in ips}
launching_nodes = {"p2.8xlarge": 1}
# requires 41 p2.8xls (currently 1 pending, 1 launching, 0 running}
demands = [{"GPU": 8}] * (len(utilizations) + 40)
to_launch = scheduler.get_nodes_to_launch(nodes, launching_nodes, demands,
utilizations, [])
# Enforces max launch to 5 when < 5 running. 2 are pending/launching.
assert to_launch == {"p2.8xlarge": 3}
provider.create_node({}, {
TAG_RAY_USER_NODE_TYPE: "p2.8xlarge",
TAG_RAY_NODE_STATUS: STATUS_UP_TO_DATE
}, 8)
nodes = provider.non_terminated_nodes({})
ips = provider.non_terminated_node_ips({})
utilizations = {ip: {"GPU": 8} for ip in ips}
launching_nodes = {"p2.8xlarge": 1}
# requires 17 p2.8xls (currently 1 pending, 1 launching, 8 running}
demands = [{"GPU": 8}] * (len(utilizations) + 15)
to_launch = scheduler.get_nodes_to_launch(nodes, launching_nodes, demands,
utilizations, [])
# We are allowed to launch up to 8 more since 8 are running.
# We already have 2 pending/launching, so only 6 remain.
assert to_launch == {"p2.8xlarge": 6}
class LoadMetricsTest(unittest.TestCase):
def testResourceDemandVector(self):
lm = LoadMetrics()
lm.update(
"1.1.1.1", {"CPU": 2}, {"CPU": 1}, {},
waiting_bundles=[{
"GPU": 1
}],
infeasible_bundles=[{
"CPU": 16
}])
assert same_elements(lm.get_resource_demand_vector(), [{
"CPU": 16
}, {
"GPU": 1
}])
def testPlacementGroupLoad(self):
lm = LoadMetrics()
pending_placement_groups = [
PlacementGroupTableData(
state=PlacementGroupTableData.RESCHEDULING,
strategy=PlacementStrategy.PACK,
bundles=([Bundle(unit_resources={"GPU": 2})] * 2)),
PlacementGroupTableData(
state=PlacementGroupTableData.RESCHEDULING,
strategy=PlacementStrategy.SPREAD,
bundles=([Bundle(unit_resources={"GPU": 2})] * 2)),
]
lm.update(
"1.1.1.1", {}, {}, {},
pending_placement_groups=pending_placement_groups)
assert lm.get_pending_placement_groups() == pending_placement_groups
class AutoscalingTest(unittest.TestCase):
def setUp(self):
_NODE_PROVIDERS["mock"] = \
lambda config: self.create_provider
self.provider = None
self.tmpdir = tempfile.mkdtemp()
def tearDown(self):
self.provider = None
del _NODE_PROVIDERS["mock"]
_clear_provider_cache()
shutil.rmtree(self.tmpdir)
ray.shutdown()
def waitForNodes(self, expected, comparison=None, tag_filters={}):
MAX_ITER = 50
for i in range(MAX_ITER):
n = len(self.provider.non_terminated_nodes(tag_filters))
if comparison is None:
comparison = self.assertEqual
try:
comparison(n, expected)
return
except Exception:
if i == MAX_ITER - 1:
raise
time.sleep(.1)
def create_provider(self, config, cluster_name):
assert self.provider
return self.provider
def write_config(self, config):
path = self.tmpdir + "/simple.yaml"
with open(path, "w") as f:
f.write(yaml.dump(config))
return path
def testGetOrCreateMultiNodeType(self):
config_path = self.write_config(MULTI_WORKER_CLUSTER)
self.provider = MockProvider()
runner = MockProcessRunner()
get_or_create_head_node(
MULTI_WORKER_CLUSTER,
config_path,
no_restart=False,
restart_only=False,
yes=True,
override_cluster_name=None,
_provider=self.provider,
_runner=runner)
self.waitForNodes(1)
runner.assert_has_call("1.2.3.4", "init_cmd")
runner.assert_has_call("1.2.3.4", "setup_cmd")
runner.assert_has_call("1.2.3.4", "start_ray_head")
self.assertEqual(self.provider.mock_nodes[0].node_type, "empty_node")
self.assertEqual(
self.provider.mock_nodes[0].node_config.get("FooProperty"), 42)
self.assertEqual(
self.provider.mock_nodes[0].node_config.get("TestProp"), 1)
self.assertEqual(
self.provider.mock_nodes[0].tags.get(TAG_RAY_USER_NODE_TYPE),
"empty_node")
def testScaleUpMinSanity(self):
config_path = self.write_config(MULTI_WORKER_CLUSTER)
self.provider = MockProvider()
runner = MockProcessRunner()
autoscaler = StandardAutoscaler(
config_path,
LoadMetrics(),
max_failures=0,
process_runner=runner,
update_interval_s=0)
assert len(self.provider.non_terminated_nodes({})) == 0
autoscaler.update()
self.waitForNodes(2)
autoscaler.update()
self.waitForNodes(2)
def testPlacementGroup(self):
# Note this is mostly an integration test. See
# testPlacementGroupScaling for more comprehensive tests.
config = copy.deepcopy(MULTI_WORKER_CLUSTER)
config["min_workers"] = 0
config["max_workers"] = 999
config_path = self.write_config(config)
self.provider = MockProvider()
runner = MockProcessRunner()
lm = LoadMetrics()
autoscaler = StandardAutoscaler(
config_path,
lm,
max_failures=0,
process_runner=runner,
update_interval_s=0)
self.provider.create_node({}, {
TAG_RAY_NODE_KIND: "head",
TAG_RAY_USER_NODE_TYPE: "m4.4xlarge"
}, 1)
head_ip = self.provider.non_terminated_node_ips({})[0]
assert len(self.provider.non_terminated_nodes({})) == 1
autoscaler.update()
self.waitForNodes(1)
pending_placement_groups = [
PlacementGroupTableData(
state=PlacementGroupTableData.RESCHEDULING,
strategy=PlacementStrategy.STRICT_SPREAD,
bundles=[Bundle(unit_resources={"GPU": 2})] * 3),
PlacementGroupTableData(
state=PlacementGroupTableData.RESCHEDULING,
strategy=PlacementStrategy.PACK,
bundles=([Bundle(unit_resources={"GPU": 2})] * 5)),
]
# Since placement groups are implemented with custom resources, this is
# an example of the accompanying resource demands. Note the resource
# demand autoscaler will be unable to fulfill these demands, but we
# should still handle the other infeasible/waiting bundles.
placement_group_resource_demands = [{
"GPU_group_0_6c2506ac733bc37496295b02c4fad446": 0.0101,
"GPU_group_6c2506ac733bc37496295b02c4fad446": 0.0101
}]
lm.update(
head_ip, {"CPU": 16}, {"CPU": 16}, {},
infeasible_bundles=placement_group_resource_demands,
waiting_bundles=[{
"GPU": 8
}],
pending_placement_groups=pending_placement_groups)
autoscaler.update()
self.waitForNodes(5)
for i in range(1, 5):
assert self.provider.mock_nodes[i].node_type == "p2.8xlarge"
pending_placement_groups = [
PlacementGroupTableData(
state=PlacementGroupTableData.RESCHEDULING,
strategy=PlacementStrategy.STRICT_PACK,
bundles=([Bundle(unit_resources={"GPU": 2})] * 4)),
PlacementGroupTableData(
state=PlacementGroupTableData.RESCHEDULING,
strategy=PlacementStrategy.SPREAD,
bundles=([Bundle(unit_resources={"GPU": 2})] * 2)),
]
def testScaleUpMinWorkers(self):
config = copy.deepcopy(MULTI_WORKER_CLUSTER)
config["min_workers"] = 2
config["max_workers"] = 50
config["idle_timeout_minutes"] = 1
# Since config["min_workers"] > 1, the remaining worker is started
# with the default worker node type.
config["available_node_types"]["p2.8xlarge"]["min_workers"] = 1
config_path = self.write_config(config)
self.provider = MockProvider()
runner = MockProcessRunner()
lm = LoadMetrics()
autoscaler = StandardAutoscaler(
config_path,
lm,
max_failures=0,
process_runner=runner,
update_interval_s=0)
assert len(self.provider.non_terminated_nodes({})) == 0
autoscaler.update()
self.waitForNodes(2)
assert len(self.provider.mock_nodes) == 2
assert {
self.provider.mock_nodes[0].node_type,
self.provider.mock_nodes[1].node_type
} == {"p2.8xlarge", "m4.large"}
self.provider.create_node({}, {
TAG_RAY_USER_NODE_TYPE: "p2.8xlarge",
TAG_RAY_NODE_KIND: NODE_KIND_WORKER
}, 2)
self.provider.create_node({}, {
TAG_RAY_USER_NODE_TYPE: "m4.16xlarge",
TAG_RAY_NODE_KIND: NODE_KIND_WORKER
}, 2)
assert len(self.provider.non_terminated_nodes({})) == 6
# Make sure that after idle_timeout_minutes we don't kill idle
# min workers.
for node_id in self.provider.non_terminated_nodes({}):
lm.last_used_time_by_ip[self.provider.internal_ip(node_id)] = -60
autoscaler.update()
self.waitForNodes(2)
cnt = 0
for id in self.provider.mock_nodes:
if self.provider.mock_nodes[id].state == "running" or \
self.provider.mock_nodes[id].state == "pending":
assert self.provider.mock_nodes[id].node_type in {
"p2.8xlarge", "m4.large"
}
cnt += 1
assert cnt == 2
def testScaleUpIgnoreUsed(self):
config = MULTI_WORKER_CLUSTER.copy()
# Commenting out this line causes the test case to fail?!?!
config["min_workers"] = 0
config["target_utilization_fraction"] = 1.0
config_path = self.write_config(config)
self.provider = MockProvider()
self.provider.create_node({}, {
TAG_RAY_NODE_KIND: "head",
TAG_RAY_USER_NODE_TYPE: "p2.xlarge"
}, 1)
head_ip = self.provider.non_terminated_node_ips({})[0]
self.provider.finish_starting_nodes()
runner = MockProcessRunner()
lm = LoadMetrics(local_ip=head_ip)
autoscaler = StandardAutoscaler(
config_path,
lm,
max_failures=0,
process_runner=runner,
update_interval_s=0)
autoscaler.update()
self.waitForNodes(1)
lm.update(head_ip, {"CPU": 4, "GPU": 1}, {}, {})
self.waitForNodes(1)
lm.update(
head_ip, {
"CPU": 4,
"GPU": 1
}, {"GPU": 0}, {},
waiting_bundles=[{
"GPU": 1
}])
autoscaler.update()
self.waitForNodes(2)
assert self.provider.mock_nodes[1].node_type == "p2.xlarge"
def testRequestBundlesAccountsForHeadNode(self):
config = MULTI_WORKER_CLUSTER.copy()
config["head_node_type"] = "p2.8xlarge"
config["min_workers"] = 0
config["max_workers"] = 50
config_path = self.write_config(config)
self.provider = MockProvider()
self.provider.create_node({}, {
TAG_RAY_USER_NODE_TYPE: "p2.8xlarge",
TAG_RAY_NODE_KIND: "head"
}, 1)
runner = MockProcessRunner()
autoscaler = StandardAutoscaler(
config_path,
LoadMetrics(),
max_failures=0,
process_runner=runner,
update_interval_s=0)
assert len(self.provider.non_terminated_nodes({})) == 1
# These requests fit on the head node.
autoscaler.update()
self.waitForNodes(1)
autoscaler.request_resources([{"CPU": 1}])
autoscaler.update()
self.waitForNodes(1)
assert len(self.provider.mock_nodes) == 1
autoscaler.request_resources([{"GPU": 8}])
autoscaler.update()
self.waitForNodes(1)
# This request requires an additional worker node.
autoscaler.request_resources([{"GPU": 8}] * 2)
autoscaler.update()
self.waitForNodes(2)
assert self.provider.mock_nodes[1].node_type == "p2.8xlarge"
def testRequestBundles(self):
config = MULTI_WORKER_CLUSTER.copy()
config["min_workers"] = 0
config["max_workers"] = 50
config_path = self.write_config(config)
self.provider = MockProvider()
runner = MockProcessRunner()
autoscaler = StandardAutoscaler(
config_path,
LoadMetrics(),
max_failures=0,
process_runner=runner,
update_interval_s=0)
assert len(self.provider.non_terminated_nodes({})) == 0
autoscaler.update()
self.waitForNodes(0)
autoscaler.request_resources([{"CPU": 1}])
autoscaler.update()
self.waitForNodes(1)
assert self.provider.mock_nodes[0].node_type == "m4.large"
autoscaler.request_resources([{"GPU": 8}])
autoscaler.update()
self.waitForNodes(2)
assert self.provider.mock_nodes[1].node_type == "p2.8xlarge"
autoscaler.request_resources([{"CPU": 32}] * 4)
autoscaler.update()
self.waitForNodes(4)
assert self.provider.mock_nodes[2].node_type == "m4.16xlarge"
assert self.provider.mock_nodes[3].node_type == "m4.16xlarge"
def testResourcePassing(self):
config = MULTI_WORKER_CLUSTER.copy()
config["min_workers"] = 0
config["max_workers"] = 50
config_path = self.write_config(config)
self.provider = MockProvider()
runner = MockProcessRunner()
autoscaler = StandardAutoscaler(
config_path,
LoadMetrics(),
max_failures=0,
process_runner=runner,
update_interval_s=0)
assert len(self.provider.non_terminated_nodes({})) == 0
autoscaler.update()
self.waitForNodes(0)
autoscaler.request_resources([{"CPU": 1}])
autoscaler.update()
self.waitForNodes(1)
assert self.provider.mock_nodes[0].node_type == "m4.large"
autoscaler.request_resources([{"GPU": 8}])
autoscaler.update()
self.waitForNodes(2)
assert self.provider.mock_nodes[1].node_type == "p2.8xlarge"
# TODO (Alex): Autoscaler creates the node during one update then
# starts the updater in the enxt update. The sleep is largely
# unavoidable because the updater runs in its own thread and we have no
# good way of ensuring that the commands are sent in time.
autoscaler.update()
sleep(0.1)
# These checks are done separately because we have no guarantees on the
# order the dict is serialized in.
runner.assert_has_call("172.0.0.0", "RAY_OVERRIDE_RESOURCES=")
runner.assert_has_call("172.0.0.0", "\"CPU\":2")
runner.assert_has_call("172.0.0.1", "RAY_OVERRIDE_RESOURCES=")
runner.assert_has_call("172.0.0.1", "\"CPU\":32")
runner.assert_has_call("172.0.0.1", "\"GPU\":8")
def testScaleUpLoadMetrics(self):
config = MULTI_WORKER_CLUSTER.copy()
config["min_workers"] = 0
config["max_workers"] = 50
config_path = self.write_config(config)
self.provider = MockProvider()
runner = MockProcessRunner()
lm = LoadMetrics()
autoscaler = StandardAutoscaler(
config_path,
lm,
max_failures=0,
process_runner=runner,
update_interval_s=0)
assert len(self.provider.non_terminated_nodes({})) == 0
autoscaler.update()
self.waitForNodes(0)
autoscaler.update()
lm.update(
"1.2.3.4", {}, {}, {},
waiting_bundles=[{
"GPU": 1
}],
infeasible_bundles=[{
"CPU": 16
}])
autoscaler.update()
self.waitForNodes(2)
nodes = {
self.provider.mock_nodes[0].node_type,
self.provider.mock_nodes[1].node_type
}
assert nodes == {"p2.xlarge", "m4.4xlarge"}
def testCommandPassing(self):
t = "custom"
config = MULTI_WORKER_CLUSTER.copy()
config["available_node_types"]["p2.8xlarge"][
"worker_setup_commands"] = ["new_worker_setup_command"]
config["available_node_types"]["p2.xlarge"][
"initialization_commands"] = ["new_worker_initialization_cmd"]
config["available_node_types"]["p2.xlarge"]["resources"][t] = 1
# Commenting out this line causes the test case to fail?!?!
config["min_workers"] = 0
config["max_workers"] = 10
config_path = self.write_config(config)
self.provider = MockProvider()
runner = MockProcessRunner()
autoscaler = StandardAutoscaler(
config_path,
LoadMetrics(),
max_failures=0,
process_runner=runner,
update_interval_s=0)
assert len(self.provider.non_terminated_nodes({})) == 0
autoscaler.update()
self.waitForNodes(0)
autoscaler.request_resources([{"CPU": 1}])
autoscaler.update()
self.waitForNodes(1)
assert self.provider.mock_nodes[0].node_type == "m4.large"
autoscaler.request_resources([{"GPU": 8}])
autoscaler.update()
self.waitForNodes(2)
assert self.provider.mock_nodes[1].node_type == "p2.8xlarge"
autoscaler.request_resources([{"GPU": 1}] * 9)
autoscaler.update()
self.waitForNodes(3)
assert self.provider.mock_nodes[2].node_type == "p2.xlarge"
autoscaler.update()
sleep(0.1)
runner.assert_has_call(self.provider.mock_nodes[1].internal_ip,
"new_worker_setup_command")
runner.assert_not_has_call(self.provider.mock_nodes[1].internal_ip,
"setup_cmd")
runner.assert_not_has_call(self.provider.mock_nodes[1].internal_ip,
"worker_setup_cmd")
runner.assert_has_call(self.provider.mock_nodes[2].internal_ip,
"new_worker_initialization_cmd")
runner.assert_not_has_call(self.provider.mock_nodes[2].internal_ip,
"init_cmd")
def testDockerWorkers(self):
config = MULTI_WORKER_CLUSTER.copy()
config["available_node_types"]["p2.8xlarge"]["docker"] = {
"worker_image": "p2.8x_image:latest",
"worker_run_options": ["p2.8x-run-options"]
}
config["available_node_types"]["p2.xlarge"]["docker"] = {
"worker_image": "p2x_image:nightly"
}
config["docker"]["worker_run_options"] = ["standard-run-options"]
config["docker"]["image"] = "default-image:nightly"
config["docker"]["worker_image"] = "default-image:nightly"
# Commenting out this line causes the test case to fail?!?!
config["min_workers"] = 0
config["max_workers"] = 10
config_path = self.write_config(config)
self.provider = MockProvider()
runner = MockProcessRunner()
autoscaler = StandardAutoscaler(
config_path,
LoadMetrics(),
max_failures=0,
process_runner=runner,
update_interval_s=0)
assert len(self.provider.non_terminated_nodes({})) == 0
autoscaler.update()
self.waitForNodes(0)
autoscaler.request_resources([{"CPU": 1}])
autoscaler.update()
self.waitForNodes(1)
assert self.provider.mock_nodes[0].node_type == "m4.large"
autoscaler.request_resources([{"GPU": 8}])
autoscaler.update()
self.waitForNodes(2)
assert self.provider.mock_nodes[1].node_type == "p2.8xlarge"
autoscaler.request_resources([{"GPU": 1}] * 9)
autoscaler.update()
self.waitForNodes(3)
assert self.provider.mock_nodes[2].node_type == "p2.xlarge"
autoscaler.update()
# Fill up m4, p2.8, p2 and request 2 more CPUs
autoscaler.request_resources([{
"CPU": 2
}, {
"CPU": 16
}, {
"CPU": 32
}, {
"CPU": 2
}])
autoscaler.update()
self.waitForNodes(4)
assert self.provider.mock_nodes[3].node_type == "m4.16xlarge"
autoscaler.update()
sleep(0.1)
runner.assert_has_call(self.provider.mock_nodes[1].internal_ip,
"p2.8x-run-options")
runner.assert_has_call(self.provider.mock_nodes[1].internal_ip,
"p2.8x_image:latest")
runner.assert_not_has_call(self.provider.mock_nodes[1].internal_ip,
"default-image:nightly")
runner.assert_not_has_call(self.provider.mock_nodes[1].internal_ip,
"standard-run-options")
runner.assert_has_call(self.provider.mock_nodes[2].internal_ip,
"p2x_image:nightly")
runner.assert_has_call(self.provider.mock_nodes[2].internal_ip,
"standard-run-options")
runner.assert_not_has_call(self.provider.mock_nodes[2].internal_ip,
"p2.8x-run-options")
runner.assert_has_call(self.provider.mock_nodes[3].internal_ip,
"default-image:nightly")
runner.assert_has_call(self.provider.mock_nodes[3].internal_ip,
"standard-run-options")
runner.assert_not_has_call(self.provider.mock_nodes[3].internal_ip,
"p2.8x-run-options")
runner.assert_not_has_call(self.provider.mock_nodes[3].internal_ip,
"p2x_image:nightly")
def testUpdateConfig(self):
config = MULTI_WORKER_CLUSTER.copy()
config_path = self.write_config(config)
self.provider = MockProvider()
runner = MockProcessRunner()
autoscaler = StandardAutoscaler(
config_path,
LoadMetrics(),
max_failures=0,
process_runner=runner,
update_interval_s=0)
assert len(self.provider.non_terminated_nodes({})) == 0
autoscaler.update()
self.waitForNodes(2)
config["min_workers"] = 0
config["available_node_types"]["m4.large"]["node_config"][
"field_changed"] = 1
config_path = self.write_config(config)
autoscaler.update()
self.waitForNodes(0)
def testEmptyDocker(self):
config = MULTI_WORKER_CLUSTER.copy()
del config["docker"]
config["min_workers"] = 0
config["max_workers"] = 10
config_path = self.write_config(config)
self.provider = MockProvider()
runner = MockProcessRunner()
autoscaler = StandardAutoscaler(
config_path,
LoadMetrics(),
max_failures=0,
process_runner=runner,
update_interval_s=0)
assert len(self.provider.non_terminated_nodes({})) == 0
autoscaler.update()
self.waitForNodes(0)
autoscaler.request_resources([{"CPU": 1}])
autoscaler.update()
self.waitForNodes(1)
assert self.provider.mock_nodes[0].node_type == "m4.large"
autoscaler.request_resources([{"GPU": 8}])
autoscaler.update()
self.waitForNodes(2)
assert self.provider.mock_nodes[1].node_type == "p2.8xlarge"
if __name__ == "__main__":
import sys
sys.exit(pytest.main(["-v", __file__]))
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