ray/doc/source/serve/deploying-serve.md

(serve-deploy-tutorial)=

Deploying Ray Serve

This section should help you:

• understand how Ray Serve runs on a Ray cluster beyond the basics
• deploy and update your Serve application over time
• monitor your Serve application using the Ray Dashboard and logging

(ray-serve-instance-lifetime)=

Lifetime of a Ray Serve Instance

Ray Serve instances run on top of Ray clusters and are started using {mod}serve.run <ray.serve.run>. Once {mod}serve.run <ray.serve.run> is called, a Serve instance is created automatically. The Serve instance will be torn down when the script exits.

:::{note} All Serve actors– including the Serve controller, the HTTP proxies, and the deployment replicas– run in the "serve" namespace, even if the Ray driver namespace is different. :::

If serve.run() is called again in a process in which there is already a running Serve instance, Serve will re-connect to the existing instance (regardless of whether the original instance was detached or not). To reconnect to a Serve instance that exists in the Ray cluster but not in the current process, connect to the cluster with ray.init(address=...) and run serve.run().

Deploying on a Single Node

While Ray Serve makes it easy to scale out on a multi-node Ray cluster, in some scenarios a single node may suit your needs. There are two ways you can run Ray Serve on a single node, shown below. In general, Option 2 is recommended for most users because it allows you to fully make use of Serve's ability to dynamically update running deployments.

1. Start Ray and deploy with Ray Serve all in a single Python file.

:start-after: __deploy_in_single_file_1_start__
:end-before: __deploy_in_single_file_1_end__
:language: python

2. First running ray start --head on the machine, then connecting to the running local Ray cluster using ray.init(address="auto") in your Serve script(s). You can run multiple scripts to update your deployments over time.

ray start --head # Start local Ray cluster.
serve start # Start Serve on the local Ray cluster.

:start-after: __deploy_in_single_file_2_start__
:end-before: __deploy_in_single_file_2_end__
:language: python

(deploying-serve-on-kubernetes)=

Deploying on Kubernetes

In order to deploy Ray Serve on Kubernetes, we need to do the following:

1. Start a Ray cluster on Kubernetes.
2. Expose the head node of the cluster as a Service.
3. Start Ray Serve on the cluster.

There are multiple ways to start a Ray cluster on Kubernetes, see {ref}kuberay-index for more information. Here, we will be using the Ray Cluster Launcher tool, which has support for Kubernetes as a backend.

The cluster launcher takes in a yaml config file that describes the cluster. Here, we'll be using the Kubernetes default config with a few small modifications. First, we need to make sure that the head node of the cluster, where Ray Serve will run its HTTP server, is exposed as a Kubernetes Service. There is already a default head node service defined in the services field of the config, so we just need to make sure that it's exposing the right port: 8000, which Ray Serve binds on by default.

# Service that maps to the head node of the Ray cluster.
- apiVersion: v1
  kind: Service
  metadata:
      name: ray-head
  spec:
      # Must match the label in the head pod spec below.
      selector:
          component: ray-head
      ports:
          - protocol: TCP
            # Port that this service will listen on.
            port: 8000
            # Port that requests will be sent to in pods backing the service.
            targetPort: 8000

Then, we also need to make sure that the head node pod spec matches the selector defined here and exposes the same port:

head_node:
  apiVersion: v1
  kind: Pod
  metadata:
    # Automatically generates a name for the pod with this prefix.
    generateName: ray-head-

    # Matches the selector in the service definition above.
    labels:
        component: ray-head

  spec:
    # ...
    containers:
    - name: ray-node
      # ...
      ports:
          - containerPort: 8000 # Ray Serve default port.
    # ...

The rest of the config remains unchanged for this example, though you may want to change the container image or the number of worker pods started by default when running your own deployment. Now, we just need to start the cluster:

# Start the cluster.
$ ray up ray/python/ray/autoscaler/kubernetes/example-full.yaml

# Check the status of the service pointing to the head node. If configured
# properly, you should see the 'Endpoints' field populated with an IP
# address like below. If not, make sure the head node pod started
# successfully and the selector/labels match.
$ kubectl -n ray describe service ray-head
  Name:              ray-head
  Namespace:         ray
  Labels:            <none>
  Annotations:       <none>
  Selector:          component=ray-head
  Type:              ClusterIP
  IP:                10.100.188.203
  Port:              <unset>  8000/TCP
  TargetPort:        8000/TCP
  Endpoints:         192.168.73.98:8000
  Session Affinity:  None
  Events:            <none>

With the cluster now running, we can run a simple script to start Ray Serve and deploy a "hello world" deployment:

:start-after: __deploy_in_k8s_start__
:end-before: __deploy_in_k8s_end__
:language: python

Save this script locally as deploy.py and run it on the head node using ray submit:

$ ray submit ray/python/ray/autoscaler/kubernetes/example-full.yaml deploy.py

Now we can try querying the service by sending an HTTP request to the service from within the Kubernetes cluster.

# Get a shell inside of the head node.
$ ray attach ray/python/ray/autoscaler/kubernetes/example-full.yaml

# Query the Ray Serve deployment. This can be run from anywhere in the
# Kubernetes cluster.
$ curl -X GET http://$RAY_HEAD_SERVICE_HOST:8000/hello
hello world

In order to expose the Ray Serve deployment externally, we would need to deploy the Service we created here behind an Ingress or a NodePort. Please refer to the Kubernetes documentation for more information.

Health Checking

By default, each actor making up a Serve deployment is health checked and restarted on failure.

:::{note} User-defined health checks are experimental and may be subject to change before the interface is stabilized. If you have any feedback or run into any issues or unexpected behaviors, please file an issue on GitHub. :::

You can customize this behavior to perform an application-level health check or to adjust the frequency/timeout. To define a custom healthcheck, define a check_health method on your deployment class. This method should take no arguments and return no result, raising an exception if the replica should be considered unhealthy. You can also customize how frequently the health check is run and the timeout when a replica will be deemed unhealthy if it hasn't responded in the deployment options.

@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()

    # Will be called by Serve to check the health of the replica.
    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.")

:::{tip} You can use the Serve CLI command serve status to get status info about your live deployments. The CLI was included with Serve when you did pip install "ray[serve]". If you're checking your deployments on a remote Ray cluster, make sure to include the Ray cluster's dashboard address in the command: serve status --address [dashboard_address]. :::

Failure Recovery

Ray Serve is resilient to any component failures within the Ray cluster out of the box. You can checkout the detail of how process and worker node failure handled at {ref}serve-ft-detail. However, when the Ray head node goes down, you would need to recover the state by creating a new Ray cluster and re-deploys all Serve deployments into that cluster.

:::{note} Ray currently cannot survive head node failure and we recommend using application specific failure recovery solutions. Although Ray is not currently highly available (HA), it is on the long term roadmap and being actively worked on. :::

Ray Serve provides the feature to help recovering the state. This feature enables Serve to write all your deployment configuration and code into Global Control Store (GCS). Upon Ray cluster failure and restarts, you can simply call Serve to reconstruct the state.

In Kubernetes environment, we recommend using KubeRay (a Kubernetes operator for Ray Serve, see {ref}kuberay-index) to help deploy your Serve applications with Kubernetes, and help you recover the node crash from Customized Resource.

Feel free to open new GitHub issues if you hit any problems from Failure Recovery.