Essentials: Use Docker for Local Development with Python

Develop Smarter with Docker and Python

Setting up a Python development environment can be tricky — dependencies, versions, and system configurations often cause headaches. That’s where Docker steps in.

Docker allows developers to create lightweight, isolated environments called containers, ensuring that your application runs the same everywhere — from your laptop to production servers.

At AI Computer Classes – Indore, learners gain hands-on experience using Docker for local Python development, setting up containers, managing dependencies, and streamlining workflows.

💡 What Is Docker?

Docker is an open-source platform that automates the deployment of applications inside containers — lightweight, portable units that package your code, libraries, and system tools together.

Think of it as a “mini virtual environment” that guarantees your app behaves the same way on every system.

Why it matters:

• No “works on my machine” issues
• Easier team collaboration
• Faster setup for new projects
• Cleaner development environments

💡 Learn from Experts at AI Computer Classes – Indore!

Get practical lessons in Python, Docker, and DevOps tools through hands-on examples.

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📍 Located in Old Palasia, Indore

⚙️ Step 1: Install Docker

Before diving in, you’ll need to install Docker on your system:

• Windows/Mac: Download and install Docker Desktop from docker.com.
• Linux: Run:

sudo apt install docker.io

Once installed, verify the setup:

docker --version

If you see the version number, you’re ready to go!

🧱 Step 2: Create a Python Project

Let’s create a simple Python project to test Docker.

mkdir docker-python-demo
cd docker-python-demo
echo "print('Hello from Docker!')" > app.py

Your project now contains one file — app.py.

📦 Step 3: Write a Dockerfile

A Dockerfile defines the environment your application will run in.

Create a file named Dockerfile:

# Use an official Python base image
FROM python:3.12-slim

# Set working directory
WORKDIR /app

# Copy project files
COPY . .

# Run the app
CMD ["python", "app.py"]

This tells Docker to:

1. Use a Python base image
2. Copy your code inside a container
3. Run it using the python command

🚀 Step 4: Build and Run the Container

Now, let’s build and run the container.

docker build -t python-demo .
docker run python-demo

You should see:

Hello from Docker!

Congratulations 🎉 — you’ve just containerized your first Python app!

🧰 Step 5: Add Dependencies with requirements.txt

If your project has dependencies, list them in a requirements.txt file:

flask
requests

Update your Dockerfile:

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "app.py"]

Rebuild and run again:

docker build -t python-demo .
docker run -p 5000:5000 python-demo

Your Flask app will now run inside Docker — isolated, consistent, and production-ready.

🔁 Step 6: Use Docker Compose for Multi-Container Apps

For apps requiring multiple services (like a web app + database), use Docker Compose.

Create a docker-compose.yml:

version: '3'
services:
  web:
    build: .
    ports:
      - "5000:5000"
  db:
    image: postgres
    environment:
      POSTGRES_USER: user
      POSTGRES_PASSWORD: password

Then start both containers with one command:

docker-compose up

Now you’re running a multi-container environment with a Python web app and a PostgreSQL database — effortlessly.

🧩 Step 7: Connect Docker with Python Tools

At AI Computer Classes – Indore, students learn to connect Dockerized apps with real-world tools like:

• Power BI → for analytics dashboards
• Figma → for UI/UX collaboration
• ChatGPT → for debugging and code explanations

This cross-tool integration helps developers work efficiently in modern, multi-tool workflows.

🧑‍💻 Step 8: Debugging Inside Containers

To debug inside a running container, use:

docker exec -it <container_id> /bin/bash

You’ll enter the container shell, where you can explore files, test scripts, or install packages temporarily.

For Python apps, integrate VS Code’s Remote Containers extension to debug directly from your IDE.

🧠 Step 9: Save and Share Your Docker Image

To share your environment with others:

docker tag python-demo yourusername/python-demo
docker push yourusername/python-demo

Now your teammates can pull and run your image anywhere — instantly replicating your setup.

🚀 Why Learn Docker at AI Computer Classes – Indore

Docker is one of the most in-demand skills in software development, especially for backend engineers and DevOps professionals.

At AI Computer Classes – Indore, you’ll learn:

• How to containerize Python apps
• How to use Docker Compose for multi-service setups
• Real-world DevOps workflows
• Integration with tools like Power BI and Figma

📘 Perfect for:

Developers, students, and professionals aiming to modernize their Python workflow and streamline deployment.

🌟 Final Thoughts

Docker transforms how developers build, test, and deploy applications. By learning Docker for local development with Python, you ensure every project is portable, efficient, and reliable.

At AI Computer Classes – Indore, our hands-on approach helps you not only understand Docker but use it like a pro — bridging theory with real-world implementation.

🚀 Start building your next Python project the smart way — with Docker!

📞 Contact AI Computer Classes – Indore

✉ Email: hello@aicomputerclasses.com

📱 Phone: +91 91113 33255

📍 Address: 208, Captain CS Naidu Building, near Greater Kailash Road, opposite School of Excellence For Eye, Opposite Grotto Arcade, Old Palasia, Indore, Madhya Pradesh 452018

🌐 Website: www.aicomputerclasses.com