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Serve: Scalable and Programmable Serving

Getting Started

Ray Serve is an easy-to-use scalable model serving library built on Ray. Ray Serve is:

  • Framework-agnostic: Use a single toolkit to serve everything from deep learning models built with frameworks like PyTorch, Tensorflow, and Keras, to Scikit-learn models, to arbitrary Python business logic.
  • Python-first: Configure your model serving declaratively in pure Python, without needing YAML or JSON configs.

Since Ray Serve is built on Ray, it allows you to easily scale to many machines, both in your datacenter and in the cloud.

Ray Serve can be used in two primary ways to deploy your models at scale:

  1. Have Python functions and classes automatically placed behind HTTP endpoints.
  2. Alternatively, call them from within your existing Python web server <serve-web-server-integration-tutorial> using the Python-native servehandle-api.

Installation

Ray Serve supports Python versions 3.6 through 3.8, with experimental support for Python 3.9. To install Ray Serve, run the following command:

pip install "ray[serve]"

Why Ray Serve?

There are generally two ways of serving machine learning applications, both with serious limitations: you can use a traditional web server---your own Flask app---or you can use a cloud-hosted solution.

The first approach is easy to get started with, but it's hard to scale each component. The second approach requires vendor lock-in (SageMaker), framework-specific tooling (TFServing), and a general lack of flexibility.

Ray Serve solves these problems by giving you a simple web server (and the ability to use your own <serve-web-server-integration-tutorial>) while still handling the complex routing, scaling, and testing logic necessary for production deployments.

Beyond scaling up your deployments with multiple replicas, Ray Serve also enables:

  • serve-model-composition---ability to flexibly compose multiple models and independently scale and update each.
  • serve-batching---built in request batching to help you meet your performance objectives.
  • serve-cpus-gpus---specify fractional resource requirements to fully saturate each of your GPUs with several models.

For more on the motivation behind Ray Serve, check out these [meetup slides][] and this [blog post][].

When should I use Ray Serve?

Ray Serve is a flexible tool that's easy to use for deploying, operating, and monitoring Python-based machine learning applications. Ray Serve excels when you want to mix business logic with ML models and scaling out in production is a necessity. This might be because of large-scale batch processing requirements or because you want to scale up a model pipeline consisting of many individual models with different performance properties.

If you plan on running on multiple machines, Ray Serve will serve you well!