How To Scale From Zero To Unicorn Fastapi Containerization And

If you’re ready to build real-world web apps with FastAPI, you’re also prepared to deploy them correctly. This guide shows you how to deploy FastAPI Docker applications using Uvicorn. We’ll walk you through a clean, easy, and secure way to get your FastAPI project production-ready. Let’s go step by step and keep it simple. FastAPI is one of the fastest web frameworks for Python. It’s built on top of Starlette and Pydantic.

That makes it super fast and super smart when handling data. But building a great app isn’t the full story. You also need to deploy it. That’s where Docker and Uvicorn come in. Docker helps you package your app and everything (like dependencies, settings, and libraries) into a container. A container is like a mini-computer that runs your app the same way every time—no surprises.

Docker packages your app with everything it needs, so it runs the same anywhere. FastAPI lets you build fast, efficient APIs with Python. Together, they make development simple and deployment reliable. FastAPI keeps things fast and easy, while Docker ensures consistency across environments. In this guide, you'll learn how to containerize a FastAPI application with Docker. Get notified with a radically better infrastructure monitoring platform.

Let's create a straightforward FastAPI project demonstrating key features while being Docker-friendly. When deploying FastAPI applications a common approach is to build a Linux container image. It's normally done using Docker. You can then deploy that container image in one of a few possible ways. Using Linux containers has several advantages including security, replicability, simplicity, and others. In a hurry and already know this stuff?

Jump to the Dockerfile below 👇. Containers (mainly Linux containers) are a very lightweight way to package applications including all their dependencies and necessary files while keeping them isolated from other containers (other applications or components) in the same system. Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system). In today’s microservices architecture, containerization has become crucial to application deployment. This guide will walk you through containerizing a FastAPI application using Docker, from basic setup to production-ready deployment.

Before we begin, ensure you have the following installed: Let’s start with a basic FastAPI project structure: my_fastapi_app/├── app/│ ├── init.py│ ├── main.py│ └── api/│ └── init.py├── Dockerfile├── docker-compose.yml└── requirements.txt Create a simple FastAPI application in app/main.py: Hi! Today we will learn how to do auto-scaling with Kubernetes.

We will use a FastAPI application as an example. In this project, we have 1 fastapi application and we will deploy it to Kubernetes with deployment object. Then, we create loadbalancer service to expose the application. to access that in local we need to tunnel the service using minikube tunnel. After we deploy the application, we will create a Horizontal Pod Autoscaler (HPA) to scale the application based on CPU usage. We will use locust to generate load to the application and see how the HPA works.

This endpoint will use to test the CPU usage. This code will generate random number 1 million times. This code will be used to test the CPU usage. fastapi-todo-demo | syinx/fastapi-todo-demo this repo contains the FastAPI application that we will use in this project. We shiped the image to Docker Hub with the name syinx/fastapi-todo-demo:v0.0.24.

You can pull the image from Docker Hub and run it locally. FastAPI has quickly become one of Python's most popular web frameworks due to its high performance, easy-to-use syntax, and automatic API documentation. When combined with Docker, it creates a powerful, portable solution for deploying applications. This article explores how to effectively dockerize FastAPI applications with a focus on project structure and virtualization benefits. Here's a basic Dockerfile for a FastAPI application: When managing multiple Python projects that share code, you have several approaches:

Create separate Python packages for shared functionality: Use multi-stage builds to manage shared code: Join the DZone community and get the full member experience. I am building a fleet of Python FastAPI microservices and aiming to scale them efficiently in a production environment. Each service runs inside a Docker container, which keeps it isolated and portable. I’m orchestrating these containers with Kubernetes, specifically using the lightweight K3s distribution on the Azure cloud.

In this article, I share my hands-on experience optimizing this setup for high performance and reliability in production. Containerization is a cornerstone of my microservices strategy. I package each FastAPI service with Docker, bundling its code and dependencies into an image. This ensures a modular, conflict-free architecture — each service effectively “lives” in its own environment, preventing dependency clashes and simplifying deployment. For example, a typical Dockerfile for one of my FastAPI microservices looks like: This produces a lean image: I start from a slim Python base, install only the required packages, copy in the application code, and launch Uvicorn (the ASGI server for FastAPI).

Running each service as a container means I can deploy many instances in parallel without worrying that one service’s dependencies will interfere with another’s.

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