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Deploy a Python API With Docker and Kubernetes
Autoscale compute-intensive workloads to keep up with changing demand
Kubernetes lets you deploy multi-container applications across a cluster — either your own machines or in the cloud. An API is the gateway to your application, the interface that users (and even other services) can use to interact with it. Building an API needn’t be hard work, however. With FastAPI you can have a working system in minutes, and it's simple to debug and test at each stage as you package it for deployment.
In this article we cover:
- Building an OpenAPI-compatible interface in Python using FastAPI
- Packaging the API inside a Docker container
- Deploying to a local Kubernetes cluster
- Load testing using Locust
- Deploying to the cloud with Google Cloud Kubernetes Engine (GKE)
Quickstart
If you just want to get up and running quickly, clone the companion repository and follow the instructions to deploy to a local Kubernetes cluster or Google cloud:
If you need a local cluster for development, follow the setup instructions for minikube.
Introduction
The API is the interface to your code, whether that is a slick new AI model that recommends new content or a long-running data processing service that crawls the web for answers. The API makes your code accessible so that you can share it with the world. Deploying an API in Python is easy if you use a framework that takes care of the boilerplate code.
In this article, we package our service up inside a Docker container. This creates a handy package that incorporates all of the required dependencies for running our code. It is also a convenient building block that we can use for horizontal scaling: If demand increases, deploy more copies of the container image. We use Kubernetes to manage the orchestration…
