Setting Up Continuous Deployment (CD) for Full Stack Python Projects

Continuous Deployment (CD) automates the process of building, testing, and deploying your application whenever new changes reach your main branch. This ensures that updates to your backend, frontend, or database are released quickly and consistently without manual intervention.

1. CD Overview for Full Stack Python Projects

A typical full stack Python project includes:

Backend: Python framework (Flask, Django, FastAPI)

Frontend: JavaScript framework (React, Vue, Angular)

Database: PostgreSQL, MySQL, SQLite, MongoDB, etc.

Deployment target: Cloud VM, Docker, Kubernetes, PaaS (Heroku, Render, Railway), or serverless (AWS Lambda)

CD automates the final step: deploying to production after successful CI checks.

2. Recommended CD Workflow

Developer pushes code → GitHub/GitLab/Bitbucket

CI pipeline runs tests and linting

CD pipeline builds artifacts (Docker images, frontend build output)

CD deploys to target environment

Application is automatically updated and restarted

3. Tools Commonly Used for CD

Platforms

GitHub Actions (most popular)

GitLab CI/CD

Bitbucket Pipelines

Jenkins

CircleCI

Azure DevOps

Deployment Targets

Docker + cloud VM (AWS EC2, DigitalOcean, etc.)

Heroku / Render / Railway

AWS ECS / ECR

Google Cloud Run

Kubernetes (EKS / AKS / GKE)

4. Example CD Setup Using GitHub Actions + Docker (Most Common Pattern)

Assume:

Backend: Python (FastAPI/Django/Flask)

Frontend: React

App runs inside Docker containers

Deployment target: Virtual Machine or container service

A. Folder Structure (Typical)

project/

│── backend/

│ ├── app.py

│ ├── requirements.txt

│ └── Dockerfile

│

│── frontend/

│ ├── src/

│ ├── package.json

│ └── Dockerfile

│

│── docker-compose.yml

│── .github/workflows/cd.yml

B. GitHub Actions CD Workflow Example

Create .github/workflows/cd.yml:

name: CD Pipeline

on:

push:

branches:

- main

jobs:

deploy:

runs-on: ubuntu-latest

steps:

- name: Checkout repo

uses: actions/checkout@v3

- name: Set up Python

uses: actions/setup-python@v4

with:

python-version: "3.11"

- name: Install backend dependencies

run: |

cd backend

pip install -r requirements.txt

- name: Build frontend

run: |

cd frontend

npm install

npm run build

- name: Build Docker images

run: |

docker build -t myapp-backend ./backend

docker build -t myapp-frontend ./frontend

- name: Deploy using SSH

uses: appleboy/ssh-action@v0.1.8

with:

host: ${{ secrets.SERVER_IP }}

username: ${{ secrets.SERVER_USER }}

key: ${{ secrets.SERVER_KEY }}

script: |

cd /var/www/myapp

git pull

docker compose down

docker compose up --build -d

What this does

Automatically triggers on push to main

Builds backend + frontend

Builds Docker images

SSHs into your server

Pulls new code

Rebuilds and restarts your containers

5. CD for Python Projects Without Docker

If you deploy directly on the server:

A. GitHub Actions Example

- name: Deploy to server

uses: appleboy/ssh-action@v0.1.8

with:

host: ${{ secrets.SERVER_IP }}

username: ${{ secrets.SERVER_USER }}

key: ${{ secrets.SERVER_KEY }}

script: |

cd /var/www/myapp

git pull

source venv/bin/activate

pip install -r requirements.txt

systemctl restart gunicorn

This works for:

Gunicorn + Nginx

Django or Flask apps

Virtual environment deployments

6. CD for Serverless Python (AWS Lambda)

Use AWS SAM or Serverless Framework.

GitHub Actions → AWS Lambda

Steps:

Build Lambda artifact

Zip it

Upload using aws-cli

Trigger Lambda version update

7. CD for Kubernetes Deployments

Pipeline steps:

Build Docker image

Push to registry (ECR, Docker Hub)

Update Kubernetes deployment

Example snippet:

- name: Apply K8s changes

run: |

kubectl set image deployment/myapp myapp=myrepo/myapp:${{ github.sha }}

8. CI/CD Integration for Full Stack Python Apps

Backend (Python)

Run unit tests

Lint with flake8, black, pylint

Run framework-specific tests (pytest, Django test suite)

Frontend

Install dependencies

Run tests: npm test

Build production bundle

9. Secrets & Environment Variables

Never store keys in code.

Use:

GitHub Secrets

GitLab Variables

.env file on server

AWS Systems Manager Parameter Store

Docker secrets

Example secure loading in Python:

import os

DATABASE_URL = os.getenv("DATABASE_URL")

10. Best Practices for CD in Full Stack Python Projects

✔ Keep production and staging separate

✔ Always run tests before deploying

✔ Use Docker for predictable deployments

✔ Roll back automatically if deployment fails

✔ Monitor logs and errors during deployment

✔ Use health checks before finalizing rollout

✔ Only deploy from a protected branch

11. Summary

Setting up Continuous Deployment for full stack Python projects involves:

Choosing a CI/CD tool (GitHub Actions, GitLab, Jenkins, etc.)

Automating backend & frontend builds

Automating tests and checks

Deploying automatically to a server, container platform, or PaaS

Managing secrets securely

Ensuring rollback and monitoring support

Whether you use Docker, serverless, VMs, or Kubernetes, CD ensures your application is always up-to-date, stable, and consistent with every code change.

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November 28, 2025