ray/doc/source/serve/handle-guide.md

(serve-handle-explainer)=

ServeHandle: Calling Deployments from Python

Ray Serve enables you to query models both from HTTP and Python. This feature enables seamless model composition. You can get a ServeHandle corresponding to deployment, similar how you can reach a deployment through HTTP via a specific route. When you issue a request to a deployment through ServeHandle, the request is load balanced across available replicas in the same way an HTTP request is.

To call a Ray Serve deployment from python, use {mod}Deployment.get_handle <ray.serve.api.Deployment> to get a handle to the deployment, then use {mod}handle.remote <ray.serve.handle.RayServeHandle.remote> to send requests to that deployment. These requests can pass ordinary args and kwargs that are passed directly to the method. This returns a Ray ObjectRef whose result can be waited for or retrieved using ray.wait or ray.get.

@serve.deployment
class Deployment:
    def method1(self, arg):
        return f"Method1: {arg}"

    def __call__(self, arg):
        return f"__call__: {arg}"

Deployment.deploy()

handle = Deployment.get_handle()
ray.get(handle.remote("hi")) # Defaults to calling the __call__ method.
ray.get(handle.method1.remote("hi")) # Call a different method.

If you want to use the same deployment to serve both HTTP and ServeHandle traffic, the recommended best practice is to define an internal method that the HTTP handling logic will call:

@serve.deployment(route_prefix="/api")
class Deployment:
    def say_hello(self, name: str):
        return f"Hello {name}!"

    def __call__(self, request):
        return self.say_hello(request.query_params["name"])

Deployment.deploy()

Now we can invoke the same logic from both HTTP or Python:

print(requests.get("http://localhost:8000/api?name=Alice"))
# Hello Alice!

handle = Deployment.get_handle()
print(ray.get(handle.say_hello.remote("Alice")))
# Hello Alice!

(serve-sync-async-handles)=

Sync and Async Handles

Ray Serve offers two types of ServeHandle. You can use the Deployment.get_handle(..., sync=True|False) flag to toggle between them.

• When you set sync=True (the default), a synchronous handle is returned. Calling handle.remote() should return a Ray ObjectRef.
• When you set sync=False, an asyncio based handle is returned. You need to Call it with await handle.remote() to return a Ray ObjectRef. To use await, you have to run Deployment.get_handle and handle.remote in Python asyncio event loop.

The async handle has performance advantage because it uses asyncio directly; as compared to the sync handle, which talks to an asyncio event loop in a thread. To learn more about the reasoning behind these, checkout our architecture documentation.

Integrating with existing web servers

Ray Serve comes with its own HTTP server out of the box, but if you have an existing web application, you can still plug in Ray Serve to scale up your compute using the ServeHandle. For a tutorial with sample code, see {ref}serve-web-server-integration-tutorial.