Automate Fastapi Deployments With A Github Actions Pipeline

Join the DZone community and get the full member experience. Deploying FastAPI apps manually gets old fast. You SSH into a server, pull the latest code, restart the service, and hope nothing breaks. Maybe you remember to run tests first. Maybe you don't. One forgotten environment variable or skipped test, and your API is down.

Users get 500 errors. You're frantically SSHing back in to fix it. GitHub Actions can automate the entire deployment process. Push to your main branch, tests run automatically, and if they pass, your app deploys. No SSH. No manual steps.

No forgotten checks. We'll use a simple FastAPI app to demonstrate the pipeline. Your actual app will be more complex, but the deployment process stays the same. Tired of manually deploying your applications? What if you could push code to GitHub, run tests automatically, and deploy updates to your live server—without lifting a finger? That’s exactly what I did with my FastAPI project using CI/CD.

I’ll walk you through how I set up a Continuous Integration (CI) pipeline to automatically test my code... Continuous Deployment (CD) pipeline to push updates to an AWS EC2 instance. Individual developers / students You host your FastAPI app on GitHub, but you manually run tests, build Docker images, and deploy every time. → We aim for a “semi-automatic” workflow where pushing code automatically runs tests and deploys to the staging environment. Small teams (3–5 people) of web / backend engineers Reviews, tests, and staging deployments for each Pull Request tend to be ad hoc and person-dependent. → We make the GitHub Actions workflow the shared rule so tests and deployments become the team’s “standard process.”

Startup teams running FastAPI on Kubernetes You are already on Kubernetes, but you’re piled up with ad hoc scripts and manual kubectl apply, and nobody is sure which script is the source of truth. → We centralize “Build → Registry → K8s Deploy” into the workflow and unify the production release flow. Information structure The article progresses step by step: big picture up front → CI design → CD design → Kubernetes integration → environment & secrets management → operational tips → summary. Terminology Terms are briefly explained the first time they appear, then used consistently afterwards to avoid confusion. English terms are not introduced more than necessary. This post documents my experience building an automated CI/CD pipeline for a FastAPI application.

The goal was simple: push code to GitHub, and it automatically deploys to AWS EC2. What seemed straightforward turned into a multi-hour debugging session that taught me valuable lessons about Docker, async/sync database drivers, and SSH authentication. The pipeline architecture follows a standard CI/CD flow: The application uses two containers orchestrated by Docker Compose: Key Design Choice: The depends_on with service_healthy ensures the database is fully ready before the application starts. This prevents connection errors during startup.

My initial docker-compose.yaml only defined the application service. I assumed the database would be handled separately, but the application expected a container named db on the same Docker network. Automate your deployment workflow with Github Actions Github Actions is a new workflow automation tool for building, testing, and deploying your code directly from Github. If you’re already using Github for your code repository, there’s no need to integrate a third-party tool to automate common developer workflows. Simply place a Github Actions .yml file in your repository, designed to run in response to specific events.

(push, pull requests, etc) Today we’re going to go through an example workflow using Python to build a barebones API, with FastAPI as the framework of choice. Welcome to the FastAPI-Docker-GitHub-Actions Lab! This repository serves as an educational example of a CI/CD project that demonstrates the usage of GitHub Actions for deploying a simple FastAPI server. Key components include: The core idea is to have a CI/CD pipeline that illustrates the automation with Continuous Integration, however, you can follow these steps to configure/run the project locally:

Begin by cloning this repository to your local machine: To manage Python dependencies and create a virtual environment, install Poetry following the instructions for your preferred installation method: Once Poetry is installed, execute the following commands for its setup: In this guide, you’ll discover how to configure a GitHub Actions CI pipeline for a Python FastAPI application, emphasizing Taskfile automation and the strategic use of pre-jobs. We’ll walk you through setting up your project environment, automating routine tasks, managing dependencies effectively, and optimizing the CI workflow for better performance. Gain valuable insights into leveraging Taskfile and pre-job configurations in GitHub Actions, along with practical advice to refine your CI pipeline and improve workflow productivity.

In the ever-evolving landscape of software development, ensuring the quality, reliability, and speed of your applications is paramount. This is where Continuous Integration (CI) becomes indispensable. CI is not just a buzzword; it’s a critical practice that enables teams to catch bugs early, streamline development workflows, and maintain high standards of code quality. Especially when working with modern frameworks like FastAPI, a robust CI pipeline can be the difference between smooth, rapid development and a chaotic, error-prone process. This lesson is the 2nd in a 4-part series on GitHub Actions: To learn how to set up a CI pipeline for Python FastAPI applications with GitHub Actions, leveraging Taskfile automation and pre-jobs, just keep reading.

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