- deploy and update your Serve application over time
- monitor your Serve application using the Ray Dashboard and logging
```{contents} Deploying Ray Serve
```
(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.start <ray.serve.start>`.
Once {mod}`serve.start <ray.serve.start>` has been called, further API calls can be used to create and update the deployments that will be used to serve your Python code (including ML models).
The Serve instance will be torn down when the script exits.
service using `serve.start(detached=True)`. In this case, the Serve instance will continue to
run on the Ray cluster even after the script that calls it exits. If you want to run another script
to update the Serve instance, you can run another script that connects to the same Ray cluster and makes further API calls (e.g., to create, update, or delete a deployment). Note that there can only be one detached Serve instance on each Ray cluster.
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.start()` 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 and run `serve.start()`.
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.
```python
import ray
from ray import serve
import time
# This will start Ray locally and start Serve on top of it.
serve.start()
@serve.deployment
def my_func(request):
return "hello"
my_func.deploy()
# Serve will be shut down once the script exits, so keep it alive manually.
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.
serve start # Start Serve on the local Ray cluster.
```
```python
import ray
from ray import serve
# This will connect to the running Ray cluster.
ray.init(address="auto", namespace="serve")
@serve.deployment
def my_func(request):
return "hello"
my_func.deploy()
```
## 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}`ray-k8s-deploy` for more information.
Here, we will be using the [Ray Cluster Launcher](cluster-cloud) 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.
```yaml
# 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:
```yaml
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:
```shell
# 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:
Save this script locally as `deploy.py` and run it on the head node using `ray submit`:
> ```shell
> $ 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.
> ```shell
> # 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.
