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

HTTP Handling

This section helps you understand how to:

• send HTTP requests to Serve deployments
• use Ray Serve to integrate with FastAPI
• use customized HTTP Adapters
• choose which feature to use for your use case

Choosing the right HTTP feature

Serve offers a layered approach to expose your model with the right HTTP API.

Considering your use case, you can choose the right level of abstraction:

• If you are comfortable working with the raw request object, use starlette.request.Requests API.
• If you want a fully fledged API server with validation and doc generation, use the FastAPI integration.
• If you just want a pre-defined HTTP schema, use the DAGDriver with http_adapter.

(serve-http)=

Calling Deployments via HTTP

When you deploy a Serve application, the ingress deployment (the one passed to serve.run) will be exposed over HTTP.

:start-after: __begin_starlette__
:end-before: __end_starlette__
:language: python

Requests to the Serve HTTP server at / are routed to the deployment's __call__ method with 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 or custom headers).

Often for ML models, you just need the API to accept a numpy array. You can use Serve's DAGDriver to simply the request parsing.

:start-after: __begin_dagdriver__
:end-before: __end_dagdriver__
:language: python

Serve provides a library of HTTP adapters to help you avoid boilerplate code. The [later section](serve-http-adapters) dives deeper into how these works.

(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.

:start-after: __begin_fastapi__
:end-before: __end_fastapi__
:language: python

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:

:start-after: __begin_fastapi_multi_routes__
:end-before: __end_fastapi_multi_routes__
:language: python

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

:start-after: __begin_byo_fastapi__
:end-before: __end_byo_fastapi__
:language: python

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.

Serve currently does not support WebSockets. If you have a use case that requires it, please [let us know](https://github.com/ray-project/ray/issues/new/choose)!

(serve-http-adapters)=

HTTP Adapters

HTTP adapters are functions that convert raw HTTP requests to basic Python types that you know and recognize.

For example, here is an adapter that extracts the JSON content from a request:

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

The input arguments to an HTTP adapter should be type-annotated. At a minimum, the adapter should accept a starlette.requests.Request type (https://www.starlette.io/requests/#request), but it can also accept any type that's recognized by FastAPI's dependency injection framework.

Here is an HTTP adapter that accepts 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 the extraction of HTTP fields, including

• query parameters,
• body parameters,
• many other data types.

For more details, you can take a look at the FastAPI documentation.

In addition to above adapters, you also use other adapters. Below we examine at least three:

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

Ray AIR Predictor

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

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

When using model wrappers, you can specify your HTTP adapter via the http_adapter field:

from ray import serve
from ray.serve.http_adapters import json_to_ndarray
from ray.serve import PredictorDeployment

serve.run(PredictorDeployment.options(name="my_model").bind(
    my_ray_air_predictor,
    my_ray_air_checkpoint,
    http_adapter=json_to_ndarray
))

:::{note} my_ray_air_predictor and my_ray_air_checkpoint are two arguments int PredictorDeployment constructor. For detailed usage, please checkout Ray AI Runtime (AIR) model wrapper :::

Serve Deployment Graph DAGDriver

When using a Serve deployment graph, you can configure ray.serve.drivers.DAGDriver to accept an HTTP adapter via its http_adapter field.

For example, the json_request adapter parses JSON in the HTTP body:

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

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

Embedded in your existing FastAPI Application

You can also bring the adapter to your own FastAPI app using Depends. The input schema automatically become 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)):
    ...

Pydantic models as adapters

Serve also supports pydantic models as a shorthand for HTTP adapters in model wrappers. Instead of using a function to define your HTTP adapter as in the examples above, you can directly pass in a pydantic model class to effectively tell Ray Serve to 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

# ...

PredictorDeployment.deploy(..., http_adapter=User)
# Or:
DAGDriver.bind(other_node, http_adapter=User)

List of Built-in Adapters

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

(serve-ndarray-schema)=

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