Add End-to-End Fault Tolerance#
This section helps you:
Provide additional fault tolerance for your Serve application
Understand Serve’s recovery procedures
Simulate system errors in your Serve application
Relevant Guides
This section discusses concepts from:
Serve’s architecture guide
Serve’s Kubernetes production guide
Guide: end-to-end fault tolerance for your Serve app#
Serve provides some fault tolerance features out of the box. You can provide end-to-end fault tolerance by tuning these features and running Serve on top of KubeRay.
Replica health-checking#
By default, the Serve controller periodically health-checks each Serve deployment replica and restarts it on failure.
You can define custom application-level health-checks and adjust their frequency and timeout.
To define a custom health-check, add a check_health method to your deployment class.
This method should take no arguments and return no result, and it should raise an exception if Ray Serve considers the replica unhealthy.
If the health-check fails, the Serve controller logs the exception, kills the unhealthy replica(s), and restarts them.
You can also use the deployment options to customize how frequently Serve runs the health-check and the timeout after which Serve marks a replica unhealthy.
@serve.deployment(health_check_period_s=10, health_check_timeout_s=30)
class MyDeployment:
def __init__(self, db_addr: str):
self._my_db_connection = connect_to_db(db_addr)
def __call__(self, request):
return self._do_something_cool()
# Called by Serve to check the replica's health.
def check_health(self):
if not self._my_db_connection.is_connected():
# The specific type of exception is not important.
raise RuntimeError("uh-oh, DB connection is broken.")
Worker node recovery#
KubeRay Required
You must deploy your Serve application with KubeRay to use this feature.
See Serve’s Kubernetes production guide to learn how you can deploy your app with KubeRay.
By default, Serve can recover from certain failures, such as unhealthy actors. When Serve runs on Kubernetes with KubeRay, it can also recover from some cluster-level failures, such as dead workers or head nodes.
When a worker node fails, the actors running on it also fail. Serve detects that the actors have failed, and it attempts to respawn the actors on the remaining, healthy nodes. Meanwhile, KubeRay detects that the node itself has failed, so it attempts to restart the worker pod on another running node, and it also brings up a new healthy node to replace it. Once the node comes up, if the pod is still pending, it can be restarted on that node. Similarly, Serve can also respawn any pending actors on that node as well. The deployment replicas running on healthy nodes can continue serving traffic throughout the recovery period.
Head node recovery: Ray GCS fault tolerance#
KubeRay Required
You must deploy your Serve application with KubeRay to use this feature.
See Serve’s Kubernetes production guide to learn how you can deploy your app with KubeRay.
In this section, you’ll learn how to add fault tolerance to Ray’s Global Control Store (GCS), which allows your Serve application to serve traffic even when the head node crashes.
By default, the Ray head node is a single point of failure: if it crashes, the entire Ray cluster crashes and you must restart it. When running on Kubernetes, the RayService controller health-checks the Ray cluster and restarts it if this occurs, but this introduces some downtime.
Starting with Ray 2.0+, KubeRay supports Global Control Store (GCS) fault tolerance, preventing the Ray cluster from crashing if the head node goes down. While the head node is recovering, Serve applications can still handle traffic with worker nodes but you can’t update or recover from other failures like Actors or Worker nodes crashing. Once the GCS recovers, the cluster returns to normal behavior.
You can enable GCS fault tolerance on KubeRay by adding an external Redis server and modifying your RayService Kubernetes object with the following steps:
Step 1: Add external Redis server#
GCS fault tolerance requires an external Redis database. You can choose to host your own Redis database, or you can use one through a third-party vendor. Use a highly available Redis database for resiliency.
For development purposes, you can also host a small Redis database on the same Kubernetes cluster as your Ray cluster. For example, you can add a 1-node Redis cluster by prepending these three Redis objects to your Kubernetes YAML:
kind: ConfigMap
apiVersion: v1
metadata:
name: redis-config
labels:
app: redis
data:
redis.conf: |-
port 6379
bind 0.0.0.0
protected-mode no
requirepass 5241590000000000
---
apiVersion: v1
kind: Service
metadata:
name: redis
labels:
app: redis
spec:
type: ClusterIP
ports:
- name: redis
port: 6379
selector:
app: redis
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: redis
labels:
app: redis
spec:
replicas: 1
selector:
matchLabels:
app: redis
template:
metadata:
labels:
app: redis
spec:
containers:
- name: redis
image: redis:5.0.8
command:
- "sh"
- "-c"
- "redis-server /usr/local/etc/redis/redis.conf"
ports:
- containerPort: 6379
volumeMounts:
- name: config
mountPath: /usr/local/etc/redis/redis.conf
subPath: redis.conf
volumes:
- name: config
configMap:
name: redis-config
---
This configuration is NOT production-ready, but it’s useful for development and testing. When you move to production, it’s highly recommended that you replace this 1-node Redis cluster with a highly available Redis cluster.
Step 2: Add Redis info to RayService#
After adding the Redis objects, you also need to modify the RayService configuration.
First, you need to update your RayService metadata’s annotations:
...
apiVersion: ray.io/v1alpha1
kind: RayService
metadata:
name: rayservice-sample
spec:
...
...
apiVersion: ray.io/v1alpha1
kind: RayService
metadata:
name: rayservice-sample
annotations:
ray.io/ft-enabled: "true"
ray.io/external-storage-namespace: "my-raycluster-storage-namespace"
spec:
...
The annotations are:
ray.io/ft-enabledREQUIRED: Enables GCS fault tolerance when trueray.io/external-storage-namespaceOPTIONAL: Sets the external storage namespace
Next, you need to add the RAY_REDIS_ADDRESS environment variable to the headGroupSpec:
apiVersion: ray.io/v1alpha1
kind: RayService
metadata:
...
spec:
...
rayClusterConfig:
headGroupSpec:
...
template:
...
spec:
...
env:
...
apiVersion: ray.io/v1alpha1
kind: RayService
metadata:
...
spec:
...
rayClusterConfig:
headGroupSpec:
...
template:
...
spec:
...
env:
...
- name: RAY_REDIS_ADDRESS
value: redis:6379
RAY_REDIS_ADDRESS’s value should be your Redis database’s redis:// address. It should contain your Redis database’s host and port. An example Redis address is redis://user:secret@localhost:6379/0?foo=bar&qux=baz.
In the example above, the Redis deployment name (redis) is the host within the Kubernetes cluster, and the Redis port is 6379. The example is compatible with the previous section’s example config.
After you apply the Redis objects along with your updated RayService, your Ray cluster can recover from head node crashes without restarting all the workers!
See also
Check out the KubeRay guide on GCS fault tolerance to learn more about how Serve leverages the external Redis cluster to provide head node fault tolerance.
Spreading replicas across nodes#
One way to improve the availability of your Serve application is to spread deployment replicas across multiple nodes so that you still have enough running replicas to serve traffic even after a certain number of node failures.
By default, Serve soft spreads all deployment replicas but it has a few limitations:
The spread is soft and best-effort with no guarantee that the it’s perfectly even.
Serve tries to spread replicas among the existing nodes if possible instead of launching new nodes. For example, if you have a big enough single node cluster, Serve schedules all replicas on that single node assuming it has enough resources. However, that node becomes the single point of failure.
You can change the spread behavior of your deployment with the max_replicas_per_node
deployment option, which hard limits the number of replicas of a given deployment that can run on a single node.
If you set it to 1 then you’re effectively strict spreading the deployment replicas. If you don’t set it then there’s no hard spread constraint and Serve uses the default soft spread mentioned in the preceding paragraph. max_replicas_per_node option is per deployment and only affects the spread of replicas within a deployment. There’s no spread between replicas of different deployments.
The following code example shows how to set max_replicas_per_node deployment option:
import ray
from ray import serve
@serve.deployment(max_replicas_per_node=1)
class Deployment1:
def __call__(self, request):
return "hello"
@serve.deployment(max_replicas_per_node=2)
class Deployment2:
def __call__(self, request):
return "world"
This example has two Serve deployments with different max_replicas_per_node: Deployment1 can have at most one replica on each node and Deployment2 can have at most two replicas on each node. If you schedule two replicas of Deployment1 and two replicas of Deployment2, Serve runs a cluster with at least two nodes, each running one replica of Deployment1. The two replicas of Deployment2 may run on either a single node or across two nodes because either satisfies the max_replicas_per_node constraint.
Serve’s recovery procedures#
This section explains how Serve recovers from system failures. It uses the following Serve application and config as a working example.
# File name: sleepy_pid.py
from ray import serve
@serve.deployment
class SleepyPid:
def __init__(self):
import time
time.sleep(10)
def __call__(self) -> int:
import os
return os.getpid()
app = SleepyPid.bind()
# File name: config.yaml
kind: ConfigMap
apiVersion: v1
metadata:
name: redis-config
labels:
app: redis
data:
redis.conf: |-
port 6379
bind 0.0.0.0
protected-mode no
requirepass 5241590000000000
---
apiVersion: v1
kind: Service
metadata:
name: redis
labels:
app: redis
spec:
type: ClusterIP
ports:
- name: redis
port: 6379
selector:
app: redis
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: redis
labels:
app: redis
spec:
replicas: 1
selector:
matchLabels:
app: redis
template:
metadata:
labels:
app: redis
spec:
containers:
- name: redis
image: redis:5.0.8
command:
- "sh"
- "-c"
- "redis-server /usr/local/etc/redis/redis.conf"
ports:
- containerPort: 6379
volumeMounts:
- name: config
mountPath: /usr/local/etc/redis/redis.conf
subPath: redis.conf
volumes:
- name: config
configMap:
name: redis-config
---
apiVersion: ray.io/v1alpha1
kind: RayService
metadata:
name: rayservice-sample
annotations:
ray.io/ft-enabled: "true"
spec:
serviceUnhealthySecondThreshold: 300
deploymentUnhealthySecondThreshold: 300
serveConfig:
importPath: "sleepy_pid:app"
runtimeEnv: |
working_dir: "https://github.com/ray-project/serve_config_examples/archive/42d10bab77741b40d11304ad66d39a4ec2345247.zip"
deployments:
- name: SleepyPid
numReplicas: 6
rayActorOptions:
numCpus: 0
rayClusterConfig:
rayVersion: '2.3.0'
headGroupSpec:
replicas: 1
rayStartParams:
num-cpus: '2'
dashboard-host: '0.0.0.0'
redis-password: "5241590000000000"
template:
spec:
containers:
- name: ray-head
image: rayproject/ray:2.3.0
imagePullPolicy: Always
env:
- name: MY_POD_IP
valueFrom:
fieldRef:
fieldPath: status.podIP
- name: RAY_REDIS_ADDRESS
value: redis:6379
resources:
limits:
cpu: 2
memory: 2Gi
requests:
cpu: 2
memory: 2Gi
ports:
- containerPort: 6379
name: redis
- containerPort: 8265
name: dashboard
- containerPort: 10001
name: client
- containerPort: 8000
name: serve
workerGroupSpecs:
- replicas: 2
minReplicas: 2
maxReplicas: 2
groupName: small-group
rayStartParams:
node-ip-address: $MY_POD_IP
template:
spec:
containers:
- name: machine-learning
image: rayproject/ray:2.3.0
imagePullPolicy: Always
env:
- name: RAY_DISABLE_DOCKER_CPU_WARNING
value: "1"
- name: TYPE
value: "worker"
- name: CPU_REQUEST
valueFrom:
resourceFieldRef:
containerName: machine-learning
resource: requests.cpu
- name: CPU_LIMITS
valueFrom:
resourceFieldRef:
containerName: machine-learning
resource: limits.cpu
- name: MEMORY_LIMITS
valueFrom:
resourceFieldRef:
containerName: machine-learning
resource: limits.memory
- name: MEMORY_REQUESTS
valueFrom:
resourceFieldRef:
containerName: machine-learning
resource: requests.memory
- name: MY_POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
- name: MY_POD_IP
valueFrom:
fieldRef:
fieldPath: status.podIP
ports:
- containerPort: 80
name: client
lifecycle:
preStop:
exec:
command: ["/bin/sh","-c","ray stop"]
resources:
limits:
cpu: "1"
memory: "2Gi"
requests:
cpu: "500m"
memory: "2Gi"
Follow the KubeRay quickstart guide to:
Install
kubectlandHelmPrepare a Kubernetes cluster
Deploy a KubeRay operator
Then, deploy the Serve application above:
$ kubectl apply -f config.yaml
Worker node failure#
You can simulate a worker node failure in the working example. First, take a look at the nodes and pods running in your Kubernetes cluster:
$ kubectl get nodes
NAME STATUS ROLES AGE VERSION
gke-serve-demo-default-pool-ed597cce-nvm2 Ready <none> 3d22h v1.22.12-gke.1200
gke-serve-demo-default-pool-ed597cce-m888 Ready <none> 3d22h v1.22.12-gke.1200
gke-serve-demo-default-pool-ed597cce-pu2q Ready <none> 3d22h v1.22.12-gke.1200
$ kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES
ervice-sample-raycluster-thwmr-worker-small-group-bdv6q 1/1 Running 0 3m3s 10.68.2.62 gke-serve-demo-default-pool-ed597cce-nvm2 <none> <none>
ervice-sample-raycluster-thwmr-worker-small-group-pztzk 1/1 Running 0 3m3s 10.68.2.61 gke-serve-demo-default-pool-ed597cce-m888 <none> <none>
rayservice-sample-raycluster-thwmr-head-28mdh 1/1 Running 1 (2m55s ago) 3m3s 10.68.0.45 gke-serve-demo-default-pool-ed597cce-pu2q <none> <none>
redis-75c8b8b65d-4qgfz 1/1 Running 0 3m3s 10.68.2.60 gke-serve-demo-default-pool-ed597cce-nvm2 <none> <none>
Open a separate terminal window and port-forward to one of the worker nodes:
$ kubectl port-forward ervice-sample-raycluster-thwmr-worker-small-group-bdv6q 8000
Forwarding from 127.0.0.1:8000 -> 8000
Forwarding from [::1]:8000 -> 8000
While the port-forward is running, you can query the application in another terminal window:
$ curl localhost:8000
418
The output is the process ID of the deployment replica that handled the request. The application launches 6 deployment replicas, so if you run the query multiple times, you should see different process IDs:
$ curl localhost:8000
418
$ curl localhost:8000
256
$ curl localhost:8000
385
Now you can simulate worker failures. You have two options: kill a worker pod or kill a worker node. Let’s start with the worker pod. Make sure to kill the pod that you’re not port-forwarding to, so you can continue querying the living worker while the other one relaunches.
$ kubectl delete pod ervice-sample-raycluster-thwmr-worker-small-group-pztzk
pod "ervice-sample-raycluster-thwmr-worker-small-group-pztzk" deleted
$ curl localhost:8000
6318
While the pod crashes and recovers, the live pod can continue serving traffic!
Tip
Killing a node and waiting for it to recover usually takes longer than killing a pod and waiting for it to recover. For this type of debugging, it’s quicker to simulate failures by killing at the pod level rather than at the node level.
You can similarly kill a worker node and see that the other nodes can continue serving traffic:
$ kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES
ervice-sample-raycluster-thwmr-worker-small-group-bdv6q 1/1 Running 0 65m 10.68.2.62 gke-serve-demo-default-pool-ed597cce-nvm2 <none> <none>
ervice-sample-raycluster-thwmr-worker-small-group-mznwq 1/1 Running 0 5m46s 10.68.1.3 gke-serve-demo-default-pool-ed597cce-m888 <none> <none>
rayservice-sample-raycluster-thwmr-head-28mdh 1/1 Running 1 (65m ago) 65m 10.68.0.45 gke-serve-demo-default-pool-ed597cce-pu2q <none> <none>
redis-75c8b8b65d-4qgfz 1/1 Running 0 65m 10.68.2.60 gke-serve-demo-default-pool-ed597cce-nvm2 <none> <none>
$ kubectl delete node gke-serve-demo-default-pool-ed597cce-m888
node "gke-serve-demo-default-pool-ed597cce-m888" deleted
$ curl localhost:8000
385
Head node failure#
You can simulate a head node failure by either killing the head pod or the head node. First, take a look at the running pods in your cluster:
$ kubectl get pods -o wide
NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES
ervice-sample-raycluster-thwmr-worker-small-group-6f2pk 1/1 Running 0 6m59s 10.68.2.64 gke-serve-demo-default-pool-ed597cce-nvm2 <none> <none>
ervice-sample-raycluster-thwmr-worker-small-group-bdv6q 1/1 Running 0 79m 10.68.2.62 gke-serve-demo-default-pool-ed597cce-nvm2 <none> <none>
rayservice-sample-raycluster-thwmr-head-28mdh 1/1 Running 1 (79m ago) 79m 10.68.0.45 gke-serve-demo-default-pool-ed597cce-pu2q <none> <none>
redis-75c8b8b65d-4qgfz 1/1 Running 0 79m 10.68.2.60 gke-serve-demo-default-pool-ed597cce-nvm2 <none> <none>
Port-forward to one of your worker pods. Make sure this pod is on a separate node from the head node, so you can kill the head node without crashing the worker:
$ kubectl port-forward ervice-sample-raycluster-thwmr-worker-small-group-bdv6q
Forwarding from 127.0.0.1:8000 -> 8000
Forwarding from [::1]:8000 -> 8000
In a separate terminal, you can make requests to the Serve application:
$ curl localhost:8000
418
You can kill the head pod to simulate killing the Ray head node:
$ kubectl delete pod rayservice-sample-raycluster-thwmr-head-28mdh
pod "rayservice-sample-raycluster-thwmr-head-28mdh" deleted
$ curl localhost:8000
If you have configured GCS fault tolerance on your cluster, your worker pod can continue serving traffic without restarting when the head pod crashes and recovers. Without GCS fault tolerance, KubeRay restarts all worker pods when the head pod crashes, so you’ll need to wait for the workers to restart and the deployments to reinitialize before you can port-forward and send more requests.
Serve controller failure#
You can simulate a Serve controller failure by manually killing the Serve actor.
If you’re running KubeRay, exec into one of your pods:
$ kubectl get pods
NAME READY STATUS RESTARTS AGE
ervice-sample-raycluster-mx5x6-worker-small-group-hfhnw 1/1 Running 0 118m
ervice-sample-raycluster-mx5x6-worker-small-group-nwcpb 1/1 Running 0 118m
rayservice-sample-raycluster-mx5x6-head-bqjhw 1/1 Running 0 118m
redis-75c8b8b65d-4qgfz 1/1 Running 0 3h36m
$ kubectl exec -it rayservice-sample-raycluster-mx5x6-head-bqjhw -- bash
ray@rayservice-sample-raycluster-mx5x6-head-bqjhw:~$
You can use the Ray State API to inspect your Serve app:
$ ray summary actors
======== Actors Summary: 2022-10-04 21:06:33.678706 ========
Stats:
------------------------------------
total_actors: 10
Table (group by class):
------------------------------------
CLASS_NAME STATE_COUNTS
0 ProxyActor ALIVE: 3
1 ServeReplica:SleepyPid ALIVE: 6
2 ServeController ALIVE: 1
$ ray list actors --filter "class_name=ServeController"
======== List: 2022-10-04 21:09:14.915881 ========
Stats:
------------------------------
Total: 1
Table:
------------------------------
ACTOR_ID CLASS_NAME STATE NAME PID
0 70a718c973c2ce9471d318f701000000 ServeController ALIVE SERVE_CONTROLLER_ACTOR 48570
You can then kill the Serve controller via the Python interpreter. Note that you’ll need to use the NAME from the ray list actor output to get a handle to the Serve controller.
$ python
>>> import ray
>>> controller_handle = ray.get_actor("SERVE_CONTROLLER_ACTOR", namespace="serve")
>>> ray.kill(controller_handle, no_restart=True)
>>> exit()
You can use the Ray State API to check the controller’s status:
$ ray list actors --filter "class_name=ServeController"
======== List: 2022-10-04 21:36:37.157754 ========
Stats:
------------------------------
Total: 2
Table:
------------------------------
ACTOR_ID CLASS_NAME STATE NAME PID
0 3281133ee86534e3b707190b01000000 ServeController ALIVE SERVE_CONTROLLER_ACTOR 49914
1 70a718c973c2ce9471d318f701000000 ServeController DEAD SERVE_CONTROLLER_ACTOR 48570
You should still be able to query your deployments while the controller is recovering:
# If you're running KubeRay, you
# can do this from inside the pod:
$ python
>>> import requests
>>> requests.get("http://localhost:8000").json()
347
Note
While the controller is dead, replica health-checking and deployment autoscaling will not work. They’ll continue working once the controller recovers.
Deployment replica failure#
You can simulate replica failures by manually killing deployment replicas. If you’re running KubeRay, make sure to exec into a Ray pod before running these commands.
$ ray summary actors
======== Actors Summary: 2022-10-04 21:40:36.454488 ========
Stats:
------------------------------------
total_actors: 11
Table (group by class):
------------------------------------
CLASS_NAME STATE_COUNTS
0 ProxyActor ALIVE: 3
1 ServeController ALIVE: 1
2 ServeReplica:SleepyPid ALIVE: 6
$ ray list actors --filter "class_name=ServeReplica:SleepyPid"
======== List: 2022-10-04 21:41:32.151864 ========
Stats:
------------------------------
Total: 6
Table:
------------------------------
ACTOR_ID CLASS_NAME STATE NAME PID
0 39e08b172e66a5d22b2b4cf401000000 ServeReplica:SleepyPid ALIVE SERVE_REPLICA::SleepyPid#RlRptP 203
1 55d59bcb791a1f9353cd34e301000000 ServeReplica:SleepyPid ALIVE SERVE_REPLICA::SleepyPid#BnoOtj 348
2 8c34e675edf7b6695461d13501000000 ServeReplica:SleepyPid ALIVE SERVE_REPLICA::SleepyPid#SakmRM 283
3 a95405318047c5528b7483e701000000 ServeReplica:SleepyPid ALIVE SERVE_REPLICA::SleepyPid#rUigUh 347
4 c531188fede3ebfc868b73a001000000 ServeReplica:SleepyPid ALIVE SERVE_REPLICA::SleepyPid#gbpoFe 383
5 de8dfa16839443f940fe725f01000000 ServeReplica:SleepyPid ALIVE SERVE_REPLICA::SleepyPid#PHvdJW 176
You can use the NAME from the ray list actor output to get a handle to one of the replicas:
$ python
>>> import ray
>>> replica_handle = ray.get_actor("SERVE_REPLICA::SleepyPid#RlRptP", namespace="serve")
>>> ray.kill(replica_handle, no_restart=True)
>>> exit()
While the replica is restarted, the other replicas can continue processing requests. Eventually the replica restarts and continues serving requests:
$ python
>>> import requests
>>> requests.get("http://localhost:8000").json()
383
Proxy failure#
You can simulate Proxy failures by manually killing ProxyActor actors. If you’re running KubeRay, make sure to exec into a Ray pod before running these commands.
$ ray summary actors
======== Actors Summary: 2022-10-04 21:51:55.903800 ========
Stats:
------------------------------------
total_actors: 12
Table (group by class):
------------------------------------
CLASS_NAME STATE_COUNTS
0 ProxyActor ALIVE: 3
1 ServeController ALIVE: 1
2 ServeReplica:SleepyPid ALIVE: 6
$ ray list actors --filter "class_name=ProxyActor"
======== List: 2022-10-04 21:52:39.853758 ========
Stats:
------------------------------
Total: 3
Table:
------------------------------
ACTOR_ID CLASS_NAME STATE NAME PID
0 283fc11beebb6149deb608eb01000000 ProxyActor ALIVE SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-91f9a685e662313a0075efcb7fd894249a5bdae7ee88837bea7985a0 101
1 2b010ce28baeff5cb6cb161e01000000 ProxyActor ALIVE SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-cc262f3dba544a49ea617d5611789b5613f8fe8c86018ef23c0131eb 133
2 7abce9dd241b089c1172e9ca01000000 ProxyActor ALIVE SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-7589773fc62e08c2679847aee9416805bbbf260bee25331fa3389c4f 267
You can use the NAME from the ray list actor output to get a handle to one of the replicas:
$ python
>>> import ray
>>> proxy_handle = ray.get_actor("SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-91f9a685e662313a0075efcb7fd894249a5bdae7ee88837bea7985a0", namespace="serve")
>>> ray.kill(proxy_handle, no_restart=False)
>>> exit()
While the proxy is restarted, the other proxies can continue accepting requests. Eventually the proxy restarts and continues accepting requests. You can use the ray list actor command to see when the proxy restarts:
$ ray list actors --filter "class_name=ProxyActor"
======== List: 2022-10-04 21:58:41.193966 ========
Stats:
------------------------------
Total: 3
Table:
------------------------------
ACTOR_ID CLASS_NAME STATE NAME PID
0 283fc11beebb6149deb608eb01000000 ProxyActor ALIVE SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-91f9a685e662313a0075efcb7fd894249a5bdae7ee88837bea7985a0 57317
1 2b010ce28baeff5cb6cb161e01000000 ProxyActor ALIVE SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-cc262f3dba544a49ea617d5611789b5613f8fe8c86018ef23c0131eb 133
2 7abce9dd241b089c1172e9ca01000000 ProxyActor ALIVE SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-7589773fc62e08c2679847aee9416805bbbf260bee25331fa3389c4f 267
Note that the PID for the first ProxyActor has changed, indicating that it restarted.