ray/doc/source/serve/http-servehandle.md

Calling Deployments via HTTP and Python

This section should help you:

• understand how deployments can be called in two ways: from HTTP and from Python
• integrate Ray Serve with an existing web server

(serve-http)=

Calling Deployments via HTTP

Basic Example

When you create a deployment, it is exposed over HTTP by default at /{deployment_name}. You can change the route by specifying the route_prefix argument to the {mod}@serve.deployment <ray.serve.api.deployment> decorator.

@serve.deployment(route_prefix="/counter")
class Counter:
    def __call__(self, request):
        pass

When you make a request to the Serve HTTP server at /counter, it will forward the request to the deployment's __call__ method and provide a Starlette Request object as the sole argument. The __call__ method can return any JSON-serializable object or a Starlette Response object (e.g., to return a custom status code).

Below, we discuss some advanced features for customizing Ray Serve's HTTP functionality.

(serve-fastapi-http)=

FastAPI HTTP Deployments

If you want to define more complex HTTP handling logic, Serve integrates with FastAPI. This allows you to define a Serve deployment using the {mod}@serve.ingress <ray.serve.api.ingress> decorator that wraps a FastAPI app with its full range of features. The most basic example of this is shown below, but for more details on all that FastAPI has to offer such as variable routes, automatic type validation, dependency injection (e.g., for database connections), and more, please check out their documentation.

import ray

from fastapi import FastAPI
from ray import serve

app = FastAPI()
ray.init(address="auto", namespace="summarizer")
serve.start(detached=True)

@serve.deployment(route_prefix="/hello")
@serve.ingress(app)
class MyFastAPIDeployment:
    @app.get("/")
    def root(self):
        return "Hello, world!"

MyFastAPIDeployment.deploy()

Now if you send a request to /hello, this will be routed to the root method of our deployment. We can also easily leverage FastAPI to define multiple routes with different HTTP methods:

import ray

from fastapi import FastAPI
from ray import serve

app = FastAPI()
ray.init(address="auto", namespace="summarizer")
serve.start(detached=True)

@serve.deployment(route_prefix="/hello")
@serve.ingress(app)
class MyFastAPIDeployment:
    @app.get("/")
    def root(self):
        return "Hello, world!"

    @app.post("/{subpath}")
    def root(self, subpath: str):
        return f"Hello from {subpath}!"

MyFastAPIDeployment.deploy()

You can also pass in an existing FastAPI app to a deployment to serve it as-is:

import ray

from fastapi import FastAPI
from ray import serve

app = FastAPI()
ray.init(address="auto", namespace="summarizer")
serve.start(detached=True)

@app.get("/")
def f():
    return "Hello from the root!"

# ... add more routes, routers, etc. to `app` ...

@serve.deployment(route_prefix="/")
@serve.ingress(app)
class FastAPIWrapper:
    pass

FastAPIWrapper.deploy()

This is useful for scaling out an existing FastAPI app with no modifications necessary. Existing middlewares, automatic OpenAPI documentation generation, and other advanced FastAPI features should work as-is. You can also combine routes defined this way with routes defined on the deployment:

import ray

from fastapi import FastAPI
from ray import serve

app = FastAPI()
ray.init(address="auto", namespace="summarizer")
serve.start(detached=True)

@app.get("/")
def f():
    return "Hello from the root!"

@serve.deployment(route_prefix="/api1")
@serve.ingress(app)
class FastAPIWrapper1:
    @app.get("/subpath")
    def method(self):
        return "Hello 1!"

@serve.deployment(route_prefix="/api2")
@serve.ingress(app)
class FastAPIWrapper2:
    @app.get("/subpath")
    def method(self):
        return "Hello 2!"

FastAPIWrapper1.deploy()
FastAPIWrapper2.deploy()

In this example, requests to both /api1 and /api2 would return Hello from the root! while a request to /api1/subpath would return Hello 1! and a request to /api2/subpath would return Hello 2!.

To try it out, save a code snippet in a local python file (i.e. main.py) and in the same directory, run the following commands to start a local Ray cluster on your machine.

ray start --head
python main.py

(serve-http-adapters)=

HTTP Adapters

HTTP adapters are functions that convert raw HTTP request to Python types that you know and recognize. Its input arguments should be type annotated. At minimal, it should accept a starlette.requests.Request type. But it can also accept any type that's recognized by the FastAPI's dependency injection framework.

For example, here is an adapter that extra the json content from request.

async def json_resolver(request: starlette.requests.Request):
    return await request.json()

Here is an adapter that accept two HTTP query parameters.

def parse_query_args(field_a: int, field_b: str):
    return YourDataClass(field_a, field_b)

You can specify different type signatures to facilitate HTTP fields extraction include query parameters, body parameters, and many other data types. For more detail, you can take a look at FastAPI documentation.

You can use adapters in different scenarios within Serve:

• Ray AIR ModelWrapper
• Serve Deployment Graph DAGDriver
• Embedded in Bring Your Own FastAPI Application

Let's go over them one by one.

Ray AIR ModelWrapper

Ray Serve provides a suite of adapters to convert HTTP requests to ML inputs like numpy arrays. You can just use it with Ray AI Runtime (AIR) model wrapper feature to one click deploy pre-trained models.

For example, we provide a simple adapter for n-dimensional array.

With model wrappers, you can specify it via the http_adapter field.

from ray import serve
from ray.serve.http_adapters import json_to_ndarray
from ray.serve.model_wrappers import ModelWrapperDeployment

ModelWrapperDeployment.options(name="my_model").deploy(
    my_ray_air_predictor,
    my_ray_air_checkpoint,
    http_adapter=json_to_ndarray
)

:::{note} Serve also supports pydantic models as a short-hand for HTTP adapters in model wrappers. Instead of functions, you can directly pass in a pydantic model class to mean "validate the HTTP body with this schema". Once validated, the model instance will passed to the predictor.

from pydantic import BaseModel

class User(BaseModel):
    user_id: int
    user_name: str

...
ModelWrapperDeployment.deploy(..., http_adapter=User)

:::

Serve Deployment Graph DAGDriver

In Serve Deployment Graph, you can configure ray.serve.drivers.DAGDriver to accept an http adapter via it's http_adapter field.

For example, the json request adapters parse JSON in HTTP body:

from ray.serve.drivers import DAGDriver
from ray.serve.http_adapters import json_request
from ray.experimental.dag.input_node import InputNode

with InputNode() as input_node:
    ...
    dag = DAGDriver.bind(other_node, http_adapter=json_request)

:::{note} Serve also supports pydantic models as a short-hand for HTTP adapters in model wrappers. Instead of functions, you can directly pass in a pydantic model class to mean "validate the HTTP body with this schema". Once validated, the model instance will passed as input_node variable.

from pydantic import BaseModel

class User(BaseModel):
    user_id: int
    user_name: str

...
DAGDriver.bind(other_node, http_adapter=User)

:::

Embedded in Bring Your Own FastAPI Application

You can also bring the adapter to your own FastAPI app using Depends. The input schema will automatically be part of the generated OpenAPI schema with FastAPI.

from fastapi import FastAPI, Depends
from ray.serve.http_adapters import json_to_ndarray

app = FastAPI()

@app.post("/endpoint")
async def endpoint(np_array = Depends(json_to_ndarray)):
    ...

It has the following schema for input:

(serve-ndarray-schema)=

.. autopydantic_model:: ray.serve.http_adapters.NdArray

List of Built-in Adapters

Here is a list of adapters and please feel free to contribute more!

.. automodule:: ray.serve.http_adapters
    :members: json_to_ndarray, image_to_ndarray, starlette_request, json_request

Configuring HTTP Server Locations

By default, Ray Serve starts a single HTTP server on the head node of the Ray cluster. You can configure this behavior using the http_options={"location": ...} flag in {mod}serve.start <ray.serve.start>:

• "HeadOnly": start one HTTP server on the head node. Serve assumes the head node is the node you executed serve.start on. This is the default.
• "EveryNode": start one HTTP server per node.
• "NoServer" or None: disable HTTP server.

:::{note} Using the "EveryNode" option, you can point a cloud load balancer to the instance group of Ray cluster to achieve high availability of Serve's HTTP proxies. :::

Enabling CORS and other HTTP middlewares

Serve supports arbitrary Starlette middlewares and custom middlewares in Starlette format. The example below shows how to enable Cross-Origin Resource Sharing (CORS). You can follow the same pattern for other Starlette middlewares.

from starlette.middleware import Middleware
from starlette.middleware.cors import CORSMiddleware

client = serve.start(
    http_options={"middlewares": [
        Middleware(
            CORSMiddleware, allow_origins=["*"], allow_methods=["*"])
    ]})

(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.