End-to-End Tutorial

By the end of this tutorial, you will have learned the basics of Ray Serve and will be ready to pick and choose from the advanced topics in the sidebar.

First, install Ray Serve and all of its dependencies by running the following command in your terminal:

pip install "ray[serve]"

Now we will write a Python script to serve a simple “Counter” class over HTTP. You may open an interactive Python terminal and copy in the lines below as we go.

First, import Ray and Ray Serve:

import ray
from ray import serve

Ray Serve runs on top of a Ray cluster, so the next step is to start a local Ray cluster:

ray.init()

Note

ray.init() will start a single-node Ray cluster on your local machine, which will allow you to use all your CPU cores to serve requests in parallel. To start a multi-node cluster, see Ray Cluster Overview.

Next, start the Ray Serve runtime:

serve.start()

Warning

When the Python script exits, Ray Serve will shut down. If you would rather keep Ray Serve running in the background you can use serve.start(detached=True) (see Deploying Ray Serve for details).

Now we will define a simple Counter class. The goal is to serve this class behind an HTTP endpoint using Ray Serve.

By default, Ray Serve offers a simple HTTP proxy that will send requests to the class’ __call__ method. The argument to this method will be a Starlette Request object.

@serve.deployment
class Counter:
  def __init__(self):
      self.count = 0

  def __call__(self, request):
      self.count += 1
      return {"count": self.count}

Note

Besides classes, you can also serve standalone functions with Ray Serve in the same way.

Notice that we made this class into a Deployment with the @serve.deployment decorator. This decorator is where we could set various configuration options such as the number of replicas, unique name of the deployment (it defaults to the class name), or the HTTP route prefix to expose the deployment at. See the Deployment package reference for more details. In order to deploy this, we simply need to call Counter.deploy().

Counter.deploy()

Note

Deployments can be configured to improve performance, for example by increasing the number of replicas of the class being served in parallel. For details, see Configuring a Deployment.

Now that our deployment is up and running, let’s test it out by making a query over HTTP. In your browser, simply visit http://127.0.0.1:8000/Counter, and you should see the output {"count": 1"}. If you keep refreshing the page, the count should increase, as expected.

Now let’s say we want to update this deployment to add another method to decrement the counter. Here, because we want more flexible HTTP configuration we’ll use Serve’s FastAPI integration. For more information on this, please see FastAPI HTTP Deployments.

from fastapi import FastAPI

app = FastAPI()

@serve.deployment
@serve.ingress(app)
class Counter:
  def __init__(self):
      self.count = 0

  @app.get("/")
  def get(self):
      return {"count": self.count}

  @app.get("/incr")
  def incr(self):
      self.count += 1
      return {"count": self.count}

  @app.get("/decr")
  def decr(self):
      self.count -= 1
      return {"count": self.count}

We’ve now redefined the Counter class to wrap a FastAPI application. This class is exposing three HTTP routes: /Counter will get the current count, /Counter/incr will increment the count, and /Counter/decr will decrement the count.

To redeploy this updated version of the Counter, all we need to do is run Counter.deploy() again. Serve will perform a rolling update here to replace the existing replicas with the new version we defined.

Counter.deploy()

If we test out the HTTP endpoint again, we can see this in action. Note that the count has been reset to zero because the new version of Counter was deployed.

> curl -X GET localhost:8000/Counter/
{"count": 0}
> curl -X GET localhost:8000/Counter/incr
{"count": 1}
> curl -X GET localhost:8000/Counter/decr
{"count": 0}

Congratulations, you just built and ran your first Ray Serve application! You should now have enough context to dive into the Core API: Deployments to get a deeper understanding of Ray Serve. For more interesting example applications, including integrations with popular machine learning frameworks and Python web servers, be sure to check out Advanced Tutorials.