Serving Machine Learning Models

Request Batching

You can also have Ray Serve batch requests for performance, which is especially important for some ML models that run on GPUs. In order to do use this feature, you need to: 1. Set the max_batch_size in the config dictionary. 2. Modify your backend implementation to accept a list of requests and return a list of responses instead of handling a single request.

class BatchingExample:
    def __init__(self):
        self.count = 0

    def __call__(self, requests):
        responses = []
            for request in requests:
        return responses

config = {"max_batch_size": 5}
client.create_backend("counter1", BatchingExample, config=config)
client.create_endpoint("counter1", backend="counter1", route="/increment")

Please take a look at Batching Tutorial for a deep dive.

Model Composition

Ray Serve supports composing individually scalable models into a single model out of the box. For instance, you can combine multiple models to perform stacking or ensembles.

To define a higher-level composed model you need to do three things:

  1. Define your underlying models (the ones that you will compose together) as Ray Serve backends

  2. Define your composed model, using the handles of the underlying models (see the example below).

  3. Define an endpoint representing this composed model and query it!

In order to avoid synchronous execution in the composed model (e.g., it’s very slow to make calls to the composed model), you’ll need to make the function asynchronous by using an async def. You’ll see this in the example below.

That’s it. Let’s take a look at an example:

from random import random
import requests
import ray
from ray import serve

client = serve.start()

# Our pipeline will be structured as follows:
# - Input comes in, the composed model sends it to model_one
# - model_one outputs a random number between 0 and 1, if the value is
#   greater than 0.5, then the data is sent to model_two
# - otherwise, the data is returned to the user.

# Let's define two models that just print out the data they received.

def model_one(request):
    print("Model 1 called with data ", request.query_params.get("data"))
    return random()

def model_two(request):
    print("Model 2 called with data ", request.query_params.get("data"))
    return request.query_params.get("data")

class ComposedModel:
    def __init__(self):
        client = serve.connect()
        self.model_one = client.get_handle("model_one")
        self.model_two = client.get_handle("model_two")

    # This method can be called concurrently!
    async def __call__(self, starlette_request):
        data = await starlette_request.body()

        score = await self.model_one.remote(data=data)
        if score > 0.5:
            result = await self.model_two.remote(data=data)
            result = {"model_used": 2, "score": score}
            result = {"model_used": 1, "score": score}

        return result

client.create_backend("model_one", model_one)
client.create_endpoint("model_one", backend="model_one")

client.create_backend("model_two", model_two)
client.create_endpoint("model_two", backend="model_two")

# max_concurrent_queries is optional. By default, if you pass in an async
# function, Ray Serve sets the limit to a high number.
    "composed_backend", ComposedModel, config={"max_concurrent_queries": 10})
    "composed", backend="composed_backend", route="/composed")

for _ in range(5):
    resp = requests.get("", data="hey!")
# Output
# {'model_used': 2, 'score': 0.6250189863595503}
# {'model_used': 1, 'score': 0.03146855349621436}
# {'model_used': 2, 'score': 0.6916977560006987}
# {'model_used': 2, 'score': 0.8169693450866928}
# {'model_used': 2, 'score': 0.9540681979573862}

Framework-Specific Tutorials

Ray Serve seamlessly integrates with popular Python ML libraries. Below are tutorials with some of these frameworks to help get you started.