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kubernetes_launcher.py
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#! /usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright 2020 Alibaba Group Holding Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import base64
import copy
import json
import logging
import os
import random
import shlex
import subprocess
import sys
import time
from gscoordinator.cluster_builder import EngineCluster
from gscoordinator.cluster_builder import MarsCluster
try:
from kubernetes import client as kube_client
from kubernetes import config as kube_config
from kubernetes import watch as kube_watch
from kubernetes.client import AppsV1Api
from kubernetes.client import CoreV1Api
from kubernetes.client.rest import ApiException as K8SApiException
from kubernetes.config import ConfigException as K8SConfigException
except ImportError:
kube_client = None
kube_config = None
kube_watch = None
AppsV1Api = None
CoreV1Api = None
K8SApiException = None
K8SConfigException = None
from graphscope.deploy.kubernetes.utils import delete_kubernetes_object
from graphscope.deploy.kubernetes.utils import get_kubernetes_object_info
from graphscope.deploy.kubernetes.utils import resolve_api_client
from graphscope.framework.utils import PipeWatcher
from graphscope.framework.utils import get_tempdir
from graphscope.proto import types_pb2
from gscoordinator.launcher import AbstractLauncher
from gscoordinator.utils import ANALYTICAL_ENGINE_PATH
from gscoordinator.utils import GRAPHSCOPE_HOME
from gscoordinator.utils import INTERACTIVE_ENGINE_SCRIPT
from gscoordinator.utils import WORKSPACE
from gscoordinator.utils import ResolveMPICmdPrefix
from gscoordinator.utils import delegate_command_to_pod
from gscoordinator.utils import parse_as_glog_level
from gscoordinator.utils import run_command
from gscoordinator.version import __version__
logger = logging.getLogger("graphscope")
class KubernetesClusterLauncher(AbstractLauncher):
def __init__(
self,
coordinator_name=None,
coordinator_service_name=None,
delete_namespace=None,
engine_cpu=None,
engine_mem=None,
engine_pod_node_selector=None,
image_pull_policy=None,
image_pull_secrets=None,
image_registry=None,
image_repository=None,
image_tag=None,
instance_id=None,
log_level=None,
mars_scheduler_cpu=None,
mars_scheduler_mem=None,
mars_worker_cpu=None,
mars_worker_mem=None,
with_dataset=False,
namespace=None,
num_workers=None,
preemptive=None,
service_type=None,
timeout_seconds=None,
vineyard_cpu=None,
vineyard_daemonset=None,
vineyard_image=None,
vineyard_mem=None,
vineyard_shared_mem=None,
volumes=None,
waiting_for_delete=None,
with_mars=False,
enabled_engines="",
dataset_proxy=None,
**kwargs,
):
super().__init__()
self._api_client = resolve_api_client()
self._core_api = kube_client.CoreV1Api(self._api_client)
self._apps_api = kube_client.AppsV1Api(self._api_client)
self._resource_object = ResourceManager(self._api_client)
self._instance_id = instance_id
self._namespace = namespace
self._delete_namespace = delete_namespace
self._coordinator_name = coordinator_name
self._coordinator_service_name = coordinator_service_name
self._owner_references = self.get_coordinator_owner_references()
self._image_registry = image_registry
self._image_repository = image_repository
self._image_tag = image_tag
image_pull_secrets = image_pull_secrets.split(",") if image_pull_secrets else []
self._glog_level = parse_as_glog_level(log_level)
self._num_workers = num_workers
self._vineyard_daemonset = vineyard_daemonset
if vineyard_daemonset is not None:
try:
self._apps_api.read_namespaced_daemon_set(
vineyard_daemonset, self._namespace
)
except K8SApiException:
logger.error(f"Vineyard daemonset {vineyard_daemonset} not found")
self._vineyard_daemonset = None
self._engine_cpu = engine_cpu
self._engine_mem = engine_mem
self._vineyard_shared_mem = vineyard_shared_mem
self._with_dataset = with_dataset
self._preemptive = preemptive
self._service_type = service_type
assert timeout_seconds is not None
self._timeout_seconds = timeout_seconds
self._waiting_for_delete = waiting_for_delete
self._with_analytical = False
self._with_analytical_java = False
self._with_interactive = False
self._with_learning = False
engines = set([item.strip() for item in enabled_engines.split(",")])
valid_engines = set(
"analytical,analytical-java,interactive,learning,gae,gae-java,gie,gle".split(
","
)
)
for item in engines:
if item not in valid_engines:
raise ValueError(
f"Not a valid engine name: {item}, valid engines are {valid_engines}"
)
if item == "analytical" or item == "gae":
self._with_analytical = True
if item == "interactive" or item == "gie":
self._with_interactive = True
if item == "learning" or item == "gle":
self._with_learning = True
if item == "analytical-java" or item == "gae-java":
self._with_analytical_java = True
self._with_mars = with_mars
self._mars_scheduler_cpu = mars_scheduler_cpu
self._mars_scheduler_mem = mars_scheduler_mem
self._mars_worker_cpu = mars_worker_cpu
self._mars_worker_mem = mars_worker_mem
self._pod_name_list = []
self._pod_ip_list = None
self._pod_host_ip_list = None
self._analytical_engine_endpoint = None
self._mars_service_endpoint = None
self._serving = False
self._analytical_engine_process = None
self._random_analytical_engine_rpc_port = random.randint(56001, 57000)
# interactive engine
# executor inter-processing port
# executor rpc port
# frontend port
self._interactive_port = 8233
# 8000 ~ 9000 is exposed
self._learning_start_port = 8000
self._graphlearn_services = {}
self._learning_instance_processes = {}
# workspace
self._instance_workspace = os.path.join(WORKSPACE, instance_id)
os.makedirs(self._instance_workspace, exist_ok=True)
self._session_workspace = None
self._engine_cluster = EngineCluster(
engine_cpu=engine_cpu,
engine_mem=engine_mem,
engine_pod_node_selector=engine_pod_node_selector,
glog_level=self._glog_level,
image_pull_policy=image_pull_policy,
image_pull_secrets=image_pull_secrets,
image_registry=image_registry,
image_repository=image_repository,
image_tag=image_tag,
instance_id=instance_id,
learning_start_port=self._learning_start_port,
with_dataset=with_dataset,
namespace=namespace,
num_workers=num_workers,
preemptive=preemptive,
service_type=service_type,
vineyard_cpu=vineyard_cpu,
vineyard_daemonset=vineyard_daemonset,
vineyard_image=vineyard_image,
vineyard_mem=vineyard_mem,
vineyard_shared_mem=vineyard_shared_mem,
volumes=volumes,
with_mars=with_mars,
with_analytical=self._with_analytical,
with_analytical_java=self._with_analytical_java,
with_interactive=self._with_interactive,
with_learning=self._with_learning,
dataset_proxy=dataset_proxy,
)
self._vineyard_service_endpoint = None
self.vineyard_internal_service_endpoint = None
self._mars_service_endpoint = None
if self._with_mars:
self._mars_cluster = MarsCluster(
self._instance_id, self._namespace, self._service_type
)
def __del__(self):
self.stop()
def type(self):
return types_pb2.K8S
def get_coordinator_owner_references(self):
owner_references = []
if self._coordinator_name:
try:
deployment = self._apps_api.read_namespaced_deployment(
self._coordinator_name, self._namespace
)
owner_references.append(
kube_client.V1OwnerReference(
api_version="apps/v1",
kind="Deployment",
name=self._coordinator_name,
uid=deployment.metadata.uid,
)
)
except K8SApiException:
logger.error(f"Coordinator {self._coordinator_name} not found")
return owner_references
def waiting_for_delete(self):
return self._waiting_for_delete
def get_namespace(self):
return self._namespace
def get_vineyard_stream_info(self):
hosts = [f"{self._namespace}:{host}" for host in self._pod_name_list]
return "kubernetes", hosts
def set_session_workspace(self, session_id):
self._session_workspace = os.path.join(self._instance_workspace, session_id)
os.makedirs(self._session_workspace, exist_ok=True)
def launch_etcd(self):
pass
def configure_etcd_endpoint(self):
pass
@property
def preemptive(self):
return self._preemptive
@property
def hosts(self):
"""String of a list of pod name, comma separated."""
return ",".join(self._pod_name_list)
@property
def hosts_list(self):
return self._pod_name_list
def distribute_file(self, path):
for pod in self._pod_name_list:
container = self._engine_cluster.analytical_container_name
try:
# The library may exists in the analytical pod.
test_cmd = f"test -f {path}"
logger.debug(delegate_command_to_pod(test_cmd, pod, container))
logger.info("Library exists, skip distribute")
except RuntimeError:
cmd = f"mkdir -p {os.path.dirname(path)}"
logger.debug(delegate_command_to_pod(cmd, pod, container))
cmd = f"kubectl cp {path} {pod}:{path} -c {container}"
logger.debug(run_command(cmd))
def close_analytical_instance(self):
pass
def launch_vineyard(self):
"""Launch vineyardd in k8s cluster."""
# vineyardd is auto launched in vineyardd container
# args = f"vineyardd -size {self._vineyard_shared_mem} \
# -socket {self._engine_cluster._sock} -etcd_endpoint http://{self._pod_ip_list[0]}:2379"
pass
def close_etcd(self):
# etcd is managed by vineyard
pass
def close_vineyard(self):
# No need to close vineyardd
# Use delete deployment instead
pass
def create_interactive_instance(self, object_id: int, schema_path: str):
if not self._with_interactive:
raise NotImplementedError("Interactive engine not enabled")
"""
Args:
object_id (int): object id of the graph.
schema_path (str): path of the schema file.
"""
env = os.environ.copy()
env["GRAPHSCOPE_HOME"] = GRAPHSCOPE_HOME
container = self._engine_cluster.interactive_executor_container_name
cmd = [
INTERACTIVE_ENGINE_SCRIPT,
"create_gremlin_instance_on_k8s",
self._session_workspace,
str(object_id),
schema_path,
self.hosts,
container,
str(self._interactive_port), # executor port
str(self._interactive_port + 1), # executor rpc port
str(self._interactive_port + 2), # frontend port
self._coordinator_name,
]
self._interactive_port += 3
logger.info("Create GIE instance with command: %s", " ".join(cmd))
process = subprocess.Popen(
cmd,
start_new_session=True,
cwd=os.getcwd(),
env=env,
encoding="utf-8",
errors="replace",
stdin=subprocess.DEVNULL,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
bufsize=1,
universal_newlines=True,
)
return process
def close_interactive_instance(self, object_id):
env = os.environ.copy()
env["GRAPHSCOPE_HOME"] = GRAPHSCOPE_HOME
container = self._engine_cluster.interactive_executor_container_name
cmd = [
INTERACTIVE_ENGINE_SCRIPT,
"close_gremlin_instance_on_k8s",
self._session_workspace,
str(object_id),
self.hosts,
container,
self._instance_id,
]
logger.info("Close GIE instance with command: %s", " ".join(cmd))
process = subprocess.Popen(
cmd,
start_new_session=True,
cwd=os.getcwd(),
env=env,
encoding="utf-8",
errors="replace",
stdin=subprocess.DEVNULL,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
universal_newlines=True,
bufsize=1,
)
return process
def _create_mars_scheduler(self):
logger.info("Launching mars scheduler pod for GraphScope ...")
deployment = self._mars_cluster.get_mars_deployment()
deployment.metadata.owner_references = self._owner_references
response = self._apps_api.create_namespaced_deployment(
self._namespace, deployment
)
self._resource_object.append(response)
def _create_engine_stateful_set(self):
logger.info("Create engine headless services...")
service = self._engine_cluster.get_engine_headless_service()
service.metadata.owner_references = self._owner_references
response = self._core_api.create_namespaced_service(self._namespace, service)
self._resource_object.append(response)
logger.info("Creating engine pods...")
stateful_set = self._engine_cluster.get_engine_stateful_set()
stateful_set.metadata.owner_references = self._owner_references
response = self._apps_api.create_namespaced_stateful_set(
self._namespace, stateful_set
)
self._resource_object.append(response)
def _create_frontend_deployment(self):
logger.info("Creating frontend pods...")
deployment = self._engine_cluster.get_interactive_frontend_deployment()
deployment.metadata.owner_references = self._owner_references
response = self._apps_api.create_namespaced_deployment(
self._namespace, deployment
)
self._resource_object.append(response)
def _create_frontend_service(self):
logger.info("Creating frontend service...")
service = self._engine_cluster.get_interactive_frontend_service(8233)
service.metadata.owner_references = self._owner_references
response = self._core_api.create_namespaced_service(self._namespace, service)
self._resource_object.append(response)
def _create_vineyard_service(self):
logger.info("Creating vineyard service...")
service = self._engine_cluster.get_vineyard_service()
service.metadata.owner_references = self._owner_references
response = self._core_api.create_namespaced_service(self._namespace, service)
self._resource_object.append(response)
def _create_learning_service(self, object_id):
logger.info("Creating learning service...")
service = self._engine_cluster.get_learning_service(
object_id, self._learning_start_port
)
service.metadata.owner_references = self._owner_references
response = self._core_api.create_namespaced_service(self._namespace, service)
self._graphlearn_services[object_id] = response
self._resource_object.append(response)
def get_engine_config(self):
config = {
"vineyard_service_name": self._engine_cluster.vineyard_service_name,
"vineyard_rpc_endpoint": self._vineyard_service_endpoint,
}
if self._with_mars:
config["mars_endpoint"] = self._mars_service_endpoint
return config
def _create_services(self):
self._create_engine_stateful_set()
if self._with_interactive:
self._create_frontend_deployment()
# self._create_frontend_service()
if self._with_mars:
# scheduler used by Mars
self._create_mars_scheduler()
if self._vineyard_daemonset is None:
self._create_vineyard_service()
def _waiting_for_services_ready(self):
logger.info("Waiting for services ready...")
selector = ""
namespace = self._namespace
start_time = time.time()
event_messages = []
while True:
# TODO: Add label selector to filter out deployments.
statefulsets = self._apps_api.list_namespaced_stateful_set(namespace)
service_available = False
for rs in statefulsets.items:
if rs.metadata.name == self._engine_cluster.engine_stateful_set_name:
# logger.info(
# "Engine pod: %s ready / %s total",
# rs.status.ready_replicas,
# self._num_workers,
# )
if rs.status.ready_replicas == self._num_workers:
# service is ready
service_available = True
break
# check container status
labels = rs.spec.selector.match_labels
selector = ",".join(f"{k}={v}" for k, v in labels.items())
pods = self._core_api.list_namespaced_pod(
namespace=namespace, label_selector=selector
)
for pod in pods.items:
pod_name = pod.metadata.name
field_selector = "involvedObject.name=" + pod_name
stream = kube_watch.Watch().stream(
self._core_api.list_namespaced_event,
namespace,
field_selector=field_selector,
timeout_seconds=1,
)
for event in stream:
msg = f"[{pod_name}]: {event['object'].message}"
if msg not in event_messages:
event_messages.append(msg)
logger.info(msg)
if event["object"].reason == "Failed":
raise RuntimeError("Kubernetes event error: " + msg)
if service_available:
break
if self._timeout_seconds + start_time < time.time():
raise TimeoutError("GraphScope Engines launching timeout.")
time.sleep(2)
self._pod_name_list = []
self._pod_ip_list = []
self._pod_host_ip_list = []
pods = self._core_api.list_namespaced_pod(
namespace=namespace, label_selector=selector
)
for pod in pods.items:
self._pod_name_list.append(pod.metadata.name)
self._pod_ip_list.append(pod.status.pod_ip)
self._pod_host_ip_list.append(pod.status.host_ip)
assert len(self._pod_ip_list) > 0
self._analytical_engine_endpoint = (
f"{self._pod_ip_list[0]}:{self._random_analytical_engine_rpc_port}"
)
self._vineyard_service_endpoint = (
self._engine_cluster.get_vineyard_service_endpoint(self._api_client)
)
self.vineyard_internal_endpoint = (
f"{self._pod_ip_list[0]}:{self._engine_cluster._vineyard_service_port}"
)
logger.info("GraphScope engines pod is ready.")
logger.info("Engines pod name list: %s", self._pod_name_list)
logger.info("Engines pod ip list: %s", self._pod_ip_list)
logger.info("Engines pod host ip list: %s", self._pod_host_ip_list)
logger.info("Vineyard service endpoint: %s", self._vineyard_service_endpoint)
if self._with_mars:
self._mars_service_endpoint = self._mars_cluster.get_mars_service_endpoint(
self._api_client
)
logger.info("Mars service endpoint: %s", self._mars_service_endpoint)
def _dump_resource_object(self):
resource = {}
if self._delete_namespace:
resource[self._namespace] = "Namespace"
else:
# coordinator info
resource[self._coordinator_name] = "Deployment"
resource[self._coordinator_service_name] = "Service"
self._resource_object.dump(extra_resource=resource)
def create_analytical_instance(self):
if not (self._with_analytical or self._with_analytical_java):
raise NotImplementedError("Analytical engine not enabled")
logger.info(
"Starting GAE rpc service on %s ...", self._analytical_engine_endpoint
)
# generate and distribute hostfile
kube_hosts_path = os.path.join(get_tempdir(), "kube_hosts")
with open(kube_hosts_path, "w") as f:
for i, pod_ip in enumerate(self._pod_ip_list):
f.write(f"{pod_ip} {self._pod_name_list[i]}\n")
for pod in self._pod_name_list:
container = self._engine_cluster.analytical_container_name
cmd = f"kubectl -n {self._namespace} cp {kube_hosts_path} {pod}:/tmp/hosts_of_nodes -c {container}"
cmd = shlex.split(cmd)
subprocess.check_call(cmd)
# launch engine
rmcp = ResolveMPICmdPrefix(rsh_agent=True)
cmd, mpi_env = rmcp.resolve(self._num_workers, ",".join(self._pod_name_list))
cmd.append(ANALYTICAL_ENGINE_PATH)
cmd.extend(["--host", "0.0.0.0"])
cmd.extend(["--port", str(self._random_analytical_engine_rpc_port)])
cmd.extend(["-v", str(self._glog_level)])
mpi_env["GLOG_v"] = str(self._glog_level)
cmd.extend(["--vineyard_socket", self._engine_cluster.vineyard_ipc_socket])
logger.info("Analytical engine launching command: %s", " ".join(cmd))
env = os.environ.copy()
env["GRAPHSCOPE_HOME"] = GRAPHSCOPE_HOME
env.update(mpi_env)
self._analytical_engine_process = subprocess.Popen(
cmd,
env=env,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
encoding="utf-8",
errors="replace",
universal_newlines=True,
bufsize=1,
)
stdout_watcher = PipeWatcher(
self._analytical_engine_process.stdout, sys.stdout, drop=True
)
stderr_watcher = PipeWatcher(
self._analytical_engine_process.stderr, sys.stderr, drop=True
)
setattr(self._analytical_engine_process, "stdout_watcher", stdout_watcher)
setattr(self._analytical_engine_process, "stderr_watcher", stderr_watcher)
def _delete_dangling_coordinator(self):
# delete service
try:
self._core_api.delete_namespaced_service(
self._coordinator_service_name, self._namespace
)
except K8SApiException as ex:
if ex.status == 404:
logger.warning(
"coordinator service %s not found", self._coordinator_service_name
)
else:
logger.exception(
"Deleting dangling coordinator service %s failed",
self._coordinator_service_name,
)
try:
self._apps_api.delete_namespaced_deployment(
self._coordinator_name, self._namespace
)
except K8SApiException as ex:
if ex.status == 404:
logger.warning(
"coordinator deployment %s not found", self._coordinator_name
)
else:
logger.exception(
"Deleting dangling coordinator %s failed", self._coordinator_name
)
if self._waiting_for_delete:
start_time = time.time()
while True:
try:
self._apps_api.read_namespaced_deployment(
self._coordinator_name, self._namespace
)
except K8SApiException as ex:
if ex.status != 404:
logger.exception(
"Deleting dangling coordinator %s failed",
self._coordinator_name,
)
break
else:
time.sleep(1)
if time.time() - start_time > self._timeout_seconds:
logger.error(
"Deleting dangling coordinator %s timeout",
self._coordinator_name,
)
def start(self):
if self._serving:
return True
try:
self._create_services()
self._waiting_for_services_ready()
self._dump_resource_object()
self._serving = True
except Exception: # pylint: disable=broad-except
time.sleep(1)
logger.exception("Error when launching GraphScope on kubernetes cluster")
self.stop()
return False
return True
def stop(self, is_dangling=False):
if self._serving:
logger.info("Cleaning up kubernetes resources")
for target in self._resource_object:
delete_kubernetes_object(
api_client=self._api_client,
target=target,
wait=self._waiting_for_delete,
timeout_seconds=self._timeout_seconds,
)
self._resource_object.clear()
if is_dangling:
logger.info("Dangling coordinator detected, cleaning up...")
# delete everything inside namespace of graphscope instance
if self._delete_namespace:
# delete namespace created by graphscope
self._core_api.delete_namespace(self._namespace)
if self._waiting_for_delete:
start_time = time.time()
while True:
try:
self._core_api.read_namespace(self._namespace)
except K8SApiException as ex:
if ex.status != 404:
logger.exception(
"Deleting dangling namespace %s failed",
self._namespace,
)
break
else:
time.sleep(1)
if time.time() - start_time > self._timeout_seconds:
logger.error(
"Deleting namespace %s timeout", self._namespace
)
else:
# delete coordinator deployment and service
self._delete_dangling_coordinator()
self._serving = False
logger.info("Kubernetes launcher stopped")
def create_learning_instance(self, object_id, handle, config):
if not self._with_learning:
raise NotImplementedError("Learning engine not enabled")
# allocate service for ports
# prepare arguments
handle = json.loads(
base64.b64decode(handle.encode("utf-8", errors="ignore")).decode(
"utf-8", errors="ignore"
)
)
hosts = ",".join(
[
f"{pod_name}:{port}"
for pod_name, port in zip(
self._pod_name_list,
self._engine_cluster.get_learning_ports(self._learning_start_port),
)
]
)
handle["server"] = hosts
handle = base64.b64encode(
json.dumps(handle).encode("utf-8", errors="ignore")
).decode("utf-8", errors="ignore")
# launch the server
self._learning_instance_processes[object_id] = []
for pod_index, pod in enumerate(self._pod_name_list):
container = self._engine_cluster.learning_container_name
sub_cmd = f"/opt/rh/rh-python38/root/usr/bin/python3 -m gscoordinator.learning {handle} {config} {pod_index}"
cmd = f"kubectl -n {self._namespace} exec -it -c {container} {pod} -- {sub_cmd}"
logging.debug("launching learning server: %s", " ".join(cmd))
cmd = shlex.split(cmd)
proc = subprocess.Popen(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
encoding="utf-8",
errors="replace",
universal_newlines=True,
bufsize=1,
)
stdout_watcher = PipeWatcher(
proc.stdout,
sys.stdout,
drop=True,
suppressed=(not logger.isEnabledFor(logging.DEBUG)),
)
setattr(proc, "stdout_watcher", stdout_watcher)
self._learning_instance_processes[object_id].append(proc)
# Create Service
self._create_learning_service(object_id)
# update the port usage record
self._learning_start_port += len(self._pod_name_list)
# parse the service hosts and ports
return self._engine_cluster.get_graphlearn_service_endpoint(
self._api_client, object_id, self._pod_host_ip_list
)
def close_learning_instance(self, object_id):
if object_id not in self._learning_instance_processes:
return
# delete the services
target = self._graphlearn_services[object_id]
try:
delete_kubernetes_object(
api_client=self._api_client,
target=target,
wait=self._waiting_for_delete,
timeout_seconds=self._timeout_seconds,
)
except Exception: # pylint: disable=broad-except
logger.exception("Failed to delete graphlearn service for %s", object_id)
# terminate the process
for proc in self._learning_instance_processes[object_id]:
try:
proc.terminate()
proc.wait(1)
except Exception: # pylint: disable=broad-except
logger.exception("Failed to terminate graphlearn server")
self._learning_instance_processes[object_id].clear()
class ResourceManager(object):
"""A class to manager kubernetes object.
Object managed by this class will dump meta info to disk file
for pod preStop lifecycle management.
meta info format:
{
"my-deployment": "Deployment",
"my-service": "Service"
}
"""
_resource_object_path = os.path.join(get_tempdir(), "resource_object") # fixed
def __init__(self, api_client):
self._api_client = api_client
self._resource_object = []
self._meta_info = {}
def append(self, target):
self._resource_object.append(target)
self._meta_info.update(
get_kubernetes_object_info(api_client=self._api_client, target=target)
)
self.dump()
def extend(self, targets):
self._resource_object.extend(targets)
for target in targets:
self._meta_info.update(
get_kubernetes_object_info(api_client=self._api_client, target=target)
)
self.dump()
def clear(self):
self._resource_object.clear()
self._meta_info.clear()
def __str__(self):
return str(self._meta_info)
def __getitem__(self, index):
return self._resource_object[index]
def dump(self, extra_resource=None):
"""Dump meta info to disk file.
Args:
extra_resource (dict): extra resource to dump.
A typical scenario is dumping meta info of namespace
for coordinator dangling processing.
"""
if extra_resource is not None:
rlt = copy.deepcopy(self._meta_info)
rlt.update(extra_resource)
else:
rlt = self._meta_info
with open(self._resource_object_path, "w") as f:
json.dump(rlt, f)