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.

Calling Deployments via HTTP#

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

import starlette.requests
import requests
from ray import serve


@serve.deployment
class Counter:
    def __call__(self, request: starlette.requests.Request):
        return request.query_params


serve.run(Counter.bind())
resp = requests.get("http://localhost:8000?a=b&c=d")
assert resp.json() == {"a": "b", "c": "d"}

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 simplify the request parsing.

import numpy as np
import requests
from ray import serve
from ray.serve.drivers import DAGDriver
from ray.serve.http_adapters import json_to_ndarray


@serve.deployment
class Model:
    def __call__(self, arr: np.ndarray):
        return arr.sum()


serve.run(DAGDriver.bind(Model.bind(), http_adapter=json_to_ndarray))
resp = requests.post("http://localhost:8000", json={"array": [[1, 2], [2, 3]]})
assert resp.json() == 8

Note

Serve provides a library of HTTP adapters to help you avoid boilerplate code. The later section dives deeper into how these works.

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 @serve.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
import requests
from fastapi import FastAPI
from ray import serve

app = FastAPI()


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


serve.run(MyFastAPIDeployment.bind())
resp = requests.get("http://localhost:8000/hello")
assert resp.json() == "Hello, world!"

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
import requests
from fastapi import FastAPI
from ray import serve

app = FastAPI()


@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}!"


serve.run(MyFastAPIDeployment.bind())
resp = requests.post("http://localhost:8000/hello/Serve")
assert resp.json() == "Hello from Serve!"

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

import ray
import requests
from fastapi import FastAPI
from ray import serve

app = FastAPI()


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


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


serve.run(FastAPIWrapper.bind())
resp = requests.get("http://localhost:8000/")
assert resp.json() == "Hello from the root!"

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.

Note

Serve currently does not support WebSockets. If you have a use case that requires it, please let us know!

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

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!

ray.serve.http_adapters.json_to_ndarray(payload: ray.serve.http_adapters.NdArray) → numpy.ndarray[source]#

Accepts an NdArray JSON from an HTTP body and converts it to a numpy array.

pydantic model ray.serve.http_adapters.NdArray[source]#

Schema for numeric array input.

Show JSON schema

{
   "title": "NdArray",
   "description": "Schema for numeric array input.",
   "type": "object",
   "properties": {
      "array": {
         "title": "Array",
         "description": "The array content as a nested list. You can pass in 1D to 4D array as nested list, or flatten them. When you flatten the array, you can use the `shape` parameter to perform reshaping.",
         "anyOf": [
            {
               "type": "array",
               "items": {
                  "type": "number"
               }
            },
            {
               "type": "array",
               "items": {
                  "type": "array",
                  "items": {
                     "type": "number"
                  }
               }
            },
            {
               "type": "array",
               "items": {
                  "type": "array",
                  "items": {
                     "type": "array",
                     "items": {
                        "type": "number"
                     }
                  }
               }
            },
            {
               "type": "array",
               "items": {
                  "type": "array",
                  "items": {
                     "type": "array",
                     "items": {
                        "type": "array",
                        "items": {
                           "type": "number"
                        }
                     }
                  }
               }
            }
         ]
      },
      "shape": {
         "title": "Shape",
         "description": "The shape of the array. If present, the array will be reshaped.",
         "type": "array",
         "items": {
            "type": "integer"
         }
      },
      "dtype": {
         "title": "Dtype",
         "description": "The numpy dtype of the array. If present, the array will be cast by `astype`.",
         "type": "string"
      }
   },
   "required": [
      "array"
   ]
}

Fields

array (Union[List[float], List[List[float]], List[List[List[float]]], List[List[List[List[float]]]]])
dtype (Optional[str])
shape (Optional[List[int]])

field array: Union[List[float], List[List[float]], List[List[List[float]]], List[List[List[List[float]]]]] [Required]#: The array content as a nested list. You can pass in 1D to 4D array as nested list, or flatten them. When you flatten the array, you can use the shape parameter to perform reshaping.

field dtype: Optional[str] = None#: The numpy dtype of the array. If present, the array will be cast by astype.

field shape: Optional[List[int]] = None#: The shape of the array. If present, the array will be reshaped.