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Dockerizing A Fastapi Crud App Automating Builds And Pushes With

Leo Migdal
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dockerizing a fastapi crud app automating builds and pushes with

FastAPI is a high-performance Python framework for building APIs, and Docker allows us to containerize applications to make them easier to deploy. In this guide, we'll containerize a FastAPI app with Docker, and automate the build and push of the Image to a private container registry using GitHub actions. I built the image with the Dockerfile (as a best practice, I copied the project's dependencies in the requirements file before copying other application files needed, to leverage Docker's caching) in the project directory. The Dockerfile contains the instructions to create the Docker image. Automate Image build and push with GitHub Actions After adding secrets, this is what it should look like.

Last modified: Dec 01, 2025 By Alexander Williams FastAPI is a top Python framework. It is fast and modern. Docker is a container platform. It packages apps with all dependencies. Together, they create a powerful deployment solution.

This guide shows you how to deploy FastAPI with Docker. We will cover everything from a basic setup to production-ready configurations. Docker solves the "it works on my machine" problem. It creates a consistent environment. This environment runs anywhere. 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:

Docker is a platform that allows you to package an application and all its dependencies into a container. A container is a lightweight, standalone, and executable unit of software that runs the same way, regardless of the environment (development, staging, or production). 1. Environment ConsistencyDocker ensures your FastAPI app runs the same on every machine—no more "works on my machine" problems. 2. Easy DeploymentPackage your app with all dependencies, then deploy it anywhere (cloud, VM, server) with just one command.

3. IsolationYour app runs in its own container, isolated from other services or apps on the system. 4. ScalabilityDocker works seamlessly with container orchestration tools (like Kubernetes), making it easier to scale FastAPI apps. Posted on Jul 11, 2023 • Originally published at sweetcode.io Docker is one of the most powerful and popular containerization tools that leverage OS virtualization.

It creates Docker Containers which run in an isolated environment from the original application. Docker containers contain all the configuration and files to run an application. They can easily be shared among different developers when they are hosted in the Docker Hub registry. In this tutorial, you will learn how to Dockerize and Deploy a Fast API application to Kubernetes. We will first create and run a Fast API application locally. We will Dockerize the Fast API application and run it as a Docker container.

We will finally deploy the Dockerized Fast API application to Kubernetes. Kubernetes is an open-source platform that automatically manages the deployment of Docker containers. Kubernetes orchestrates application containers and hosts them in a Kubernetes cluster. A Kubernetes Cluster has nodes (Master and worker nodes) that run and manage the hosted/deployed container applications. The deployed containers run in a Kubernetes environment known as pods. A pod is the smallest deployable unit that runs in a Kubernetes cluster.

In this tutorial, we will deploy the Dockerized Fast API application to a local Kubernetes Cluster known as Minikube. We will talk more about Minikube later. Let's start working on our project!

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FastAPI is a high-performance Python framework for building APIs, and Docker allows us to containerize applications to make them easier to deploy. In this guide, we'll containerize a FastAPI app with Docker, and automate the build and push of the Image to a private container registry using GitHub actions. I built the image with the Dockerfile (as a best practice, I copied the project's dependencie...

Last Modified: Dec 01, 2025 By Alexander Williams FastAPI Is

Last modified: Dec 01, 2025 By Alexander Williams FastAPI is a top Python framework. It is fast and modern. Docker is a container platform. It packages apps with all dependencies. Together, they create a powerful deployment solution.

This Guide Shows You How To Deploy FastAPI With Docker.

This guide shows you how to deploy FastAPI with Docker. We will cover everything from a basic setup to production-ready configurations. Docker solves the "it works on my machine" problem. It creates a consistent environment. This environment runs anywhere. FastAPI has quickly become one of Python's most popular web frameworks due to its high performance, easy-to-use syntax, and automatic API docum...

When Combined With Docker, It Creates A Powerful, Portable Solution

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...

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Docker is a platform that allows you to package an application and all its dependencies into a container. A container is a lightweight, standalone, and executable unit of software that runs the same way, regardless of the environment (development, staging, or production). 1. Environment ConsistencyDocker ensures your FastAPI app runs the same on every machine—no more "works on my machine" problems...