Getting Started

This tutorial will walk you through the process of deploying models with Ray Serve. It will show you how to

• expose your models over HTTP using deployments

• test your deployments over HTTP

• compose multiple-model machine learning models together into a single application

We’ll use two models in this tutorial:

• HuggingFace’s TranslationPipeline as a text-translation model

• HuggingFace’s SummarizationPipeline as a text-summarizer model

You can also follow along using your own models from any Python framework.

After deploying those two models, we’ll test them with HTTP requests.

Tip

If you have suggestions on how to improve this tutorial, please let us know!

To run this example, you will need to install the following:

pip install "ray[serve]" transformers requests torch

Model Example: Before Ray Serve

First, let’s take a look at our text-translation model. Here’s its code:

# File name: model.py
from transformers import pipeline


class Translator:
    def __init__(self):
        # Load model
        self.model = pipeline("translation_en_to_fr", model="t5-small")

    def translate(self, text: str) -> str:
        # Run inference
        model_output = self.model(text)

        # Post-process output to return only the translation text
        translation = model_output[0]["translation_text"]

        return translation


translator = Translator()

translation = translator.translate("Hello world!")
print(translation)

The Python file, called model.py, uses the Translator class to translate English text to French.

• The self.model variable inside Translator’s __init__ method stores a function that uses the t5-small model to translate text.

• When self.model is called on English text, it returns translated French text inside a dictionary formatted as [{"translation_text": "..."}].

• The Translator’s translate method extracts the translated text by indexing into the dictionary.

You can copy-paste this script and run it locally. It translates "Hello world!" into "Bonjour Monde!".

$ python model.py

Bonjour Monde!

Keep in mind that the TranslationPipeline is an example ML model for this tutorial. You can follow along using arbitrary models from any Python framework. Check out our tutorials on scikit-learn, PyTorch, and Tensorflow for more info and examples:

• Serving ML Models (Tensorflow, PyTorch, Scikit-Learn, others)

Converting to a Ray Serve Deployment

In this section, we’ll deploy the text translation model using Ray Serve, so it can be scaled up and queried over HTTP. We’ll start by converting Translator into a Ray Serve deployment that runs locally on your computer.

First, we open a new Python file and import ray and ray serve:

from starlette.requests import Request

import ray
from ray import serve

After these imports, we can include our model code from above:

from transformers import pipeline


@serve.deployment(num_replicas=2, ray_actor_options={"num_cpus": 0.2, "num_gpus": 0})
class Translator:
    def __init__(self):
        # Load model
        self.model = pipeline("translation_en_to_fr", model="t5-small")

    def translate(self, text: str) -> str:
        # Run inference
        model_output = self.model(text)

        # Post-process output to return only the translation text
        translation = model_output[0]["translation_text"]

        return translation

    async def __call__(self, http_request: Request) -> str:
        english_text: str = await http_request.json()
        return self.translate(english_text)

The Translator class has two modifications:

1. It has a decorator, @serve.deployment.

2. It has a new method, __call__.

The decorator converts Translator from a Python class into a Ray Serve Deployment object.

Each deployment stores a single Python function or class that you write and uses it to serve requests. You can scale and configure each of your deployments independently using parameters in the @serve.deployment decorator. The example configures a few common parameters:

• num_replicas: an integer that determines how many copies of our deployment process run in Ray. Requests are load balanced across these replicas, allowing you to scale your deployments horizontally.

• ray_actor_options: a dictionary containing configuration options for each replica.

  • num_cpus: a float representing the logical number of CPUs each replica should reserve. You can make this a fraction to pack multiple replicas together on a machine with fewer CPUs than replicas.

  • num_gpus: a float representing the logical number of GPUs each replica should reserve. You can make this a fraction to pack multiple replicas together on a machine with fewer GPUs than replicas.

All these parameters are optional, so feel free to omit them:

...
@serve.deployment
class Translator:
  ...

Deployments receive Starlette HTTP request objects 1. If your deployment stores a Python function, the function is called on this request object. If your deployment stores a class, the class’s __call__ method is called on this request object. The return value is sent back in the HTTP response body.

This is why Translator needs a new __call__ method. The method processes the incoming HTTP request by reading its JSON data and forwarding it to the translate method. The translated text is returned and sent back through the HTTP response. You can also use Ray Serve’s FastAPI integration to avoid working with raw HTTP requests. Check out FastAPI HTTP Deployments for more info about FastAPI with Serve.

Next, we need to bind our Translator deployment to arguments that Ray Serve can pass into its constructor. This will let Ray Serve initialize a Translator object that can serve requests. Since Translator’s constructor doesn’t take in any arguments, we can call the deployment’s bind method without passing anything in:

translator = Translator.bind()

With that, we can run our model on Ray Serve! Here’s the full Ray Serve script that we built:

# File name: serve_deployment.py
from starlette.requests import Request

import ray
from ray import serve

from transformers import pipeline


@serve.deployment(num_replicas=2, ray_actor_options={"num_cpus": 0.2, "num_gpus": 0})
class Translator:
    def __init__(self):
        # Load model
        self.model = pipeline("translation_en_to_fr", model="t5-small")

    def translate(self, text: str) -> str:
        # Run inference
        model_output = self.model(text)

        # Post-process output to return only the translation text
        translation = model_output[0]["translation_text"]

        return translation

    async def __call__(self, http_request: Request) -> str:
        english_text: str = await http_request.json()
        return self.translate(english_text)


translator = Translator.bind()

We can run our script with the serve run CLI command. This command takes in an import path to our deployment formatted as module:bound_deployment. Make sure to run the command from a directory containing a local copy of this script, so it can find the bound deployment:

$ serve run serve_deployment:translator

This command will start running Translator and then block. It can be killed with ctrl-C in the terminal.

Testing Ray Serve Deployments

We can now test our model over HTTP. It can be reached at the following URL:

http://127.0.0.1:8000/

Since the cluster is deployed locally in this tutorial, the 127.0.0.1:8000 refers to a localhost with port 8000 (the default port where you can reach Serve deployments).

We’ll send a POST request with JSON data containing our English text. Translator’s __call__ method will unpack this text and forward it to the translate method. Here’s a client script that requests a translation for “Hello world!”:

# File name: model_client.py
import requests

english_text = "Hello world!"

response = requests.post("http://127.0.0.1:8000/", json=english_text)
french_text = response.text

print(french_text)

To test our deployment, first make sure Translator is running:

$ serve run serve_deployment:translator

While Translator is running, we can open a separate terminal window and run the client script. This will get a response over HTTP:

$ python model_client.py

Bonjour monde!

Composing Machine Learning Models with Deployment Graphs

Ray Serve’s Deployment Graph API allows us to compose multiple machine learning models together into a single Ray Serve application. We can use parameters like num_replicas, num_cpus, and num_gpus to independently configure and scale each deployment in the graph.

For example, let’s deploy a machine learning pipeline with two steps:

1. Summarize English text

2. Translate the summary into French

Translator already performs step 2. We can use HuggingFace’s SummarizationPipeline to accomplish step 1. Here’s an example of the SummarizationPipeline that runs locally:

# File name: summary_model.py
from transformers import pipeline


class Summarizer:
    def __init__(self):
        # Load model
        self.model = pipeline("summarization", model="t5-small")

    def summarize(self, text: str) -> str:
        # Run inference
        model_output = self.model(text, min_length=5, max_length=15)

        # Post-process output to return only the summary text
        summary = model_output[0]["summary_text"]

        return summary


summarizer = Summarizer()

summary = summarizer.summarize(
    "It was the best of times, it was the worst of times, it was the age "
    "of wisdom, it was the age of foolishness, it was the epoch of belief"
)
print(summary)

You can copy-paste this script and run it locally. It summarizes the snippet from A Tale of Two Cities to it was the best of times, it was worst of times .

$ python summary_model.py

it was the best of times, it was worst of times .

Here’s a Ray Serve deployment graph that chains the two models together. The graph takes English text, summarizes it, and then translates it:

# File name: graph.py
from starlette.requests import Request

import ray
from ray import serve

from transformers import pipeline


@serve.deployment
class Translator:
    def __init__(self):
        # Load model
        self.model = pipeline("translation_en_to_fr", model="t5-small")

    def translate(self, text: str) -> str:
        # Run inference
        model_output = self.model(text)

        # Post-process output to return only the translation text
        translation = model_output[0]["translation_text"]

        return translation


@serve.deployment
class Summarizer:
    def __init__(self, translator):
        # Load model
        self.model = pipeline("summarization", model="t5-small")
        self.translator = translator

    def summarize(self, text: str) -> str:
        # Run inference
        model_output = self.model(text, min_length=5, max_length=15)

        # Post-process output to return only the summary text
        summary = model_output[0]["summary_text"]

        return summary

    async def __call__(self, http_request: Request) -> str:
        english_text: str = await http_request.json()
        summary = self.summarize(english_text)

        translation_ref = await self.translator.translate.remote(summary)
        translation = await translation_ref

        return translation


deployment_graph = Summarizer.bind(Translator.bind())

This script contains our Summarizer class converted to a deployment and our Translator class with some modifications. In this script, the Summarizer class contains the __call__ method since requests are sent to it first. It also takes in the Translator as one of its constructor arguments, so it can forward summarized texts to the Translator deployment. The __call__ method also contains some new code:

translation_ref = await self.translator.translate.remote(summary)
translation = await translation_ref

self.translator.translate.remote(summary) issues an asynchronous call to the Translator’s translate method. Essentially, this line tells Ray to schedule a request to the Translator deployment’s translate method, which can be fulfilled asynchronously. The line immediately returns a reference to the method’s output. The next line await translation_ref waits for translate to execute and returns the value of that execution.

We compose our graph in line 52:

deployment_graph = Summarizer.bind(Translator.bind())

Here, we bind Translator to its (empty) constructor arguments, and then we pass in the bound Translator as the constructor argument for the Summarizer. We can run this deployment graph using the serve run CLI command. Make sure to run this command from a directory containing a local copy of the graph.py code:

$ serve run graph:deployment_graph

We can use this client script to make requests to the graph:

# File name: graph_client.py
import requests

english_text = (
    "It was the best of times, it was the worst of times, it was the age "
    "of wisdom, it was the age of foolishness, it was the epoch of belief"
)
response = requests.post("http://127.0.0.1:8000/", json=english_text)
french_text = response.text

print(french_text)

While the graph is running, we can open a separate terminal window and run the client script:

$ python graph_client.py

c'était le meilleur des temps, c'était le pire des temps .

Deployment graphs are useful since they let you deploy each part of your machine learning pipeline, such as inference and business logic steps, in separate deployments. Each of these deployments can be individually configured and scaled, ensuring you get maximal performance from your resources. See the guide on model composition to learn more.

Next Steps

• Dive into the Key Concepts to get a deeper understanding of Ray Serve.

• View details about your Serve application in the Ray Dashboard: Serve View.

• Learn more about how to deploy your Ray Serve application to production: Production Guide.

• Check more in-depth tutorials for popular machine learning frameworks: Examples.

Footnotes

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1

Starlette is a web server framework used by Ray Serve. Its Request class provides a nice interface for incoming HTTP requests.