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[kuberay][autoscaler] Improve CPU, GPU, and memory detection. (#26219)

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This PR improves the autoscaler's resource detection logic
This commit is contained in:
Dmitri Gekhtman 2022-07-02 11:32:05 -07:00 • committed by GitHub
parent 34d1e580cb
commit 7d3ceb222c
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4 changed files with 110 additions and 59 deletions
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80
python/ray/autoscaler/_private/kuberay/autoscaling_config.py
View file

@ -1,10 +1,10 @@
import decimal
import json
import logging
import math
import time
from contextlib import suppress
from typing import Any, Dict, Optional
import kubernetes
import requests
from ray.autoscaler._private.constants import (
@ -29,8 +29,6 @@ RAYCLUSTER_FETCH_RETRY_S = 5
# Used as the name of the "head node type" by the autoscaler.
_HEAD_GROUP_NAME = "head-group"
_GPU_WARNING_LOGGED = False
class AutoscalingConfigProducer:
"""Produces an autoscaling config by reading data from the RayCluster CR.
@ -259,15 +257,14 @@ def _get_num_cpus(
k8s_resource_limits: Dict[str, str],
group_name: str,
) -> int:
if "num_cpus" in ray_start_params:
return int(ray_start_params["num_cpus"])
"""Get CPU annotation from ray_start_params or k8s_resource_limits,
with priority for ray_start_params.
"""
if "num-cpus" in ray_start_params:
return int(ray_start_params["num-cpus"])
elif "cpu" in k8s_resource_limits:
cpu_str = str(k8s_resource_limits["cpu"])
if cpu_str[-1] == "m":
# For example, '500m' rounds up to 1.
return math.ceil(int(cpu_str[:-1]) / 1000)
else:
return int(cpu_str)
cpu_quantity: str = k8s_resource_limits["cpu"]
return _round_up_k8s_quantity(cpu_quantity)
else:
# Getting the number of CPUs is important, so raise an error if we can't do it.
raise ValueError(
@ -280,13 +277,14 @@ def _get_num_cpus(
def _get_memory(
ray_start_params: Dict[str, str], k8s_resource_limits: Dict[str, Any]
) -> Optional[int]:
"""Get memory resource annotation from ray_start_params, if it is set there.
TODO, maybe: Consider container resource limits as in
https://github.com/ray-project/ray/pull/14567/files
"""Get memory resource annotation from ray_start_params or k8s_resource_limits,
with priority for ray_start_params.
"""
if "memory" in ray_start_params:
return int(ray_start_params["memory"])
elif "memory" in k8s_resource_limits:
memory_quantity: str = k8s_resource_limits["memory"]
return _round_up_k8s_quantity(memory_quantity)
return None
@ -295,34 +293,44 @@ def _get_num_gpus(
k8s_resource_limits: Dict[str, Any],
group_name: str,
) -> Optional[int]:
"""Read the number of GPUs from the Ray start params.
Potential TODO: Read GPU info from the container spec, here and in the
Ray Operator.
"""Get memory resource annotation from ray_start_params or k8s_resource_limits,
with priority for ray_start_params.
"""
if "num-gpus" in ray_start_params:
return int(ray_start_params["num-gpus"])
# Issue a warning if GPUs are present in the container spec but not in the
# ray start params.
# TODO: Consider reading GPU info from container spec.
else:
for key in k8s_resource_limits:
global _GPU_WARNING_LOGGED
if "gpu" in key and not _GPU_WARNING_LOGGED:
with suppress(Exception):
if int(k8s_resource_limits[key]) > 0:
logger.warning(
f"Detected GPUs in container resources for group {group_name}."
"To ensure Ray and the autoscaler are aware of the GPUs,"
" set the `--num-gpus` rayStartParam."
)
_GPU_WARNING_LOGGED = True
break
# e.g. nvidia.com/gpu
if key.endswith("gpu"):
# Typically, this is a string representing an interger, e.g. "1".
gpu_resource_quantity = k8s_resource_limits[key]
# Convert to int, making no assumptions on the gpu_resource_quantity,
# besides that it's valid as a K8s resource quantity.
num_gpus = _round_up_k8s_quantity(gpu_resource_quantity)
if num_gpus > 0:
# Only one GPU type supported for now, break out on first
# "/gpu" match.
return num_gpus
return None
def _round_up_k8s_quantity(quantity: str) -> int:
"""Rounds a Kubernetes resource quantity up to the nearest integer.
Args:
quantity: Resource quantity as a string in the canonical K8s form.
Returns:
The quantity, rounded up, as an integer.
"""
resource_decimal: decimal.Decimal = kubernetes.utils.quantity.parse_quantity(
quantity
)
rounded = resource_decimal.to_integral_value(rounding=decimal.ROUND_UP)
return int(rounded)
def _get_custom_resources(
ray_start_params: Dict[str, Any], group_name: str
) -> Dict[str, int]:

2
python/ray/autoscaler/_private/kuberay/run_autoscaler.py
View file

@ -71,7 +71,7 @@ def _setup_logging() -> None:
filename=ray_constants.MONITOR_LOG_FILE_NAME, # monitor.log
max_bytes=ray_constants.LOGGING_ROTATE_BYTES,
backup_count=ray_constants.LOGGING_ROTATE_BACKUP_COUNT,
logger_name="ray", # Root of the logging hierachy for Ray code.
logger_name="ray", # Root of the logging hierarchy for Ray code.
)
# Logs will also be written to the container's stdout.
# The stdout handler was set up in the cli entry point.

5
python/ray/scripts/scripts.py
View file

@ -41,7 +41,6 @@ from ray.autoscaler._private.commands import (
)
from ray.autoscaler._private.constants import RAY_PROCESSES
from ray.autoscaler._private.fake_multi_node.node_provider import FAKE_HEAD_NODE_ID
from ray.autoscaler._private.kuberay.run_autoscaler import run_kuberay_autoscaler
from ray.dashboard.modules.job.cli import job_cli_group
from ray.experimental.state.api import get_log, list_logs
from ray.experimental.state.common import DEFAULT_RPC_TIMEOUT, DEFAULT_LOG_LIMIT
@ -2292,6 +2291,10 @@ def kuberay_autoscaler(cluster_name: str, cluster_namespace: str) -> None:
KubeRay cluster configs.
`ray kuberay-autoscaler` is NOT a public CLI.
"""
# Delay import to avoid introducing Ray core dependency on the Python Kubernetes
# client.
from ray.autoscaler._private.kuberay.run_autoscaler import run_kuberay_autoscaler
run_kuberay_autoscaler(cluster_name, cluster_namespace)

80
python/ray/tests/kuberay/test_autoscaling_config.py
View file

@ -1,3 +1,4 @@
import copy
from pathlib import Path
import requests
from typing import Any, Dict, Optional
@ -10,20 +11,30 @@ import yaml
from ray.autoscaler._private.kuberay.autoscaling_config import (
_derive_autoscaling_config_from_ray_cr,
AutoscalingConfigProducer,
_round_up_k8s_quantity,
)
AUTOSCALING_CONFIG_MODULE_PATH = "ray.autoscaler._private.kuberay.autoscaling_config"
def _get_basic_ray_cr() -> dict:
"""Returns the example Ray CR included in the Ray documentation."""
"""Returns the example Ray CR included in the Ray documentation,
modified to include a GPU worker group.
"""
cr_path = str(
Path(__file__).resolve().parents[2]
/ "autoscaler"
/ "kuberay"
/ "ray-cluster.complete.yaml"
)
return yaml.safe_load(open(cr_path).read())
config = yaml.safe_load(open(cr_path).read())
gpu_group = copy.deepcopy(config["spec"]["workerGroupSpecs"][0])
gpu_group["groupName"] = "gpu-group"
gpu_group["template"]["spec"]["containers"][0]["resources"]["limits"].setdefault(
"nvidia.com/gpu", 3
)
config["spec"]["workerGroupSpecs"].append(gpu_group)
return config
def _get_basic_autoscaling_config() -> dict:
@ -44,6 +55,7 @@ def _get_basic_autoscaling_config() -> dict:
"node_config": {},
"resources": {
"CPU": 1,
"memory": 1000000000,
"Custom1": 1,
"Custom2": 5,
},
@ -54,10 +66,24 @@ def _get_basic_autoscaling_config() -> dict:
"node_config": {},
"resources": {
"CPU": 1,
"memory": 536870912,
"Custom2": 5,
"Custom3": 1,
},
},
# Same as "small-group" with a GPU entry added.
"gpu-group": {
"max_workers": 300,
"min_workers": 1,
"node_config": {},
"resources": {
"CPU": 1,
"memory": 536870912,
"Custom2": 5,
"Custom3": 1,
"GPU": 3,
},
},
},
"auth": {},
"cluster_synced_files": [],
@ -69,7 +95,7 @@ def _get_basic_autoscaling_config() -> dict:
"head_start_ray_commands": [],
"idle_timeout_minutes": 5,
"initialization_commands": [],
"max_workers": 300,
"max_workers": 600,
"setup_commands": [],
"upscaling_speed": 1,
"worker_nodes": {},
@ -99,19 +125,25 @@ def _get_no_cpu_error() -> str:
)
def _get_ray_cr_memory_and_gpu() -> dict:
"""CR with memory and gpu rayStartParams."""
def _get_ray_cr_with_overrides() -> dict:
"""CR with memory, cpu, and gpu overrides from rayStartParams."""
cr = _get_basic_ray_cr()
cr["spec"]["workerGroupSpecs"][0]["rayStartParams"]["memory"] = "300000000"
cr["spec"]["workerGroupSpecs"][0]["rayStartParams"]["num-gpus"] = "1"
# num-gpus rayStartParam with no gpus in container limits
cr["spec"]["workerGroupSpecs"][0]["rayStartParams"]["num-gpus"] = "100"
# num-gpus rayStartParam overriding gpus in container limits
cr["spec"]["workerGroupSpecs"][1]["rayStartParams"]["num-gpus"] = "100"
cr["spec"]["workerGroupSpecs"][0]["rayStartParams"]["num-cpus"] = "100"
return cr
def _get_autoscaling_config_memory_and_gpu() -> dict:
def _get_autoscaling_config_with_overrides() -> dict:
"""Autoscaling config with memory and gpu annotations."""
config = _get_basic_autoscaling_config()
config["available_node_types"]["small-group"]["resources"]["memory"] = 300000000
config["available_node_types"]["small-group"]["resources"]["GPU"] = 1
config["available_node_types"]["small-group"]["resources"]["GPU"] = 100
config["available_node_types"]["small-group"]["resources"]["CPU"] = 100
config["available_node_types"]["gpu-group"]["resources"]["GPU"] = 100
return config
@ -151,6 +183,21 @@ def _get_autoscaling_config_with_options() -> dict:
return config
@pytest.mark.parametrize(
"input,output",
[
# There's no particular discipline to these test cases.
("100m", 1),
("15001m", 16),
("2", 2),
("100Mi", 104857600),
("1G", 1000000000),
],
)
def test_resource_quantity(input: str, output: int):
assert _round_up_k8s_quantity(input) == output, output
PARAM_ARGS = ",".join(
[
"ray_cr_in",
@ -182,20 +229,12 @@ TEST_DATA = (
id="no-cpu-error",
),
pytest.param(
_get_ray_cr_memory_and_gpu(),
_get_autoscaling_config_memory_and_gpu(),
_get_ray_cr_with_overrides(),
_get_autoscaling_config_with_overrides(),
None,
None,
None,
id="memory-and-gpu",
),
pytest.param(
_get_ray_cr_missing_gpu_arg(),
_get_basic_autoscaling_config(),
None,
None,
_get_gpu_complaint(),
id="gpu-complaint",
id="overrides",
),
pytest.param(
_get_ray_cr_with_autoscaler_options(),
@ -239,7 +278,8 @@ def test_cr_image_consistency():
cr = _get_basic_ray_cr()
group_specs = [cr["spec"]["headGroupSpec"]] + cr["spec"]["workerGroupSpecs"]
assert len(group_specs) == 2
# Head, CPU group, GPU group.
assert len(group_specs) == 3
ray_containers = [
group_spec["template"]["spec"]["containers"][0] for group_spec in group_specs