Debugging Python in Docker Containers with VS Code: A Step-by-Step Guide

Introduction

Debugging Python applications can be challenging, especially when they are running in Docker containers. Visual Studio Code (VS Code) provides a robust set of tools for debugging Python applications, including those running in Docker containers. In this post, we will explore how to debug Python applications running in Docker containers using VS Code.

Prerequisites

Before we dive into the details, make sure you have the following prerequisites:

• VS Code installed on your machine
• Docker installed and running on your machine
• A Python application running in a Docker container
• The Python extension installed in VS Code

Setting up the Environment

To debug a Python application running in a Docker container, you need to set up the environment correctly. This involves creating a launch.json file in the .vscode directory of your project.

Creating the launch.json File

The launch.json file contains the configuration for the debugger. Here is an example of a launch.json file for debugging a Python application running in a Docker container:

1{
2    "version": "0.2.0",
3    "configurations": [
4        {
5            "name": "Docker: Python",
6            "type": "python",
7            "request": "launch",
8            "program": "${file}",
9            "console": "integratedTerminal",
10            "justMyCode": true,
11            "docker": {
12                "containerName": "my-python-app",
13                "dockerHost": "localhost"
14            }
15        }
16    ]
17}

In this example, we are telling VS Code to launch the debugger in the my-python-app container running on the local machine.

Installing the Necessary Extensions

To debug Python applications running in Docker containers, you need to install the following extensions in VS Code:

• Python extension
• Docker extension

You can install these extensions by searching for them in the Extensions panel in VS Code.

Configuring the Docker Container

To debug a Python application running in a Docker container, you need to configure the container to allow the debugger to attach to it. This involves setting the docker configuration in the launch.json file.

Setting the Container Name

You need to set the containerName property in the docker configuration to the name of the container running your Python application.

Setting the Docker Host

You need to set the dockerHost property in the docker configuration to the host running your Docker container. This is usually localhost.

Debugging the Application

Once you have set up the environment and configured the Docker container, you can start debugging your Python application.

Starting the Debugger

To start the debugger, click on the Run icon in the left sidebar or press F5. VS Code will launch the debugger and attach it to the Docker container.

Setting Breakpoints

You can set breakpoints in your Python code by clicking in the gutter next to the line number. When the debugger hits a breakpoint, it will pause execution and allow you to inspect the variables and the call stack.

Inspecting Variables and the Call Stack

You can inspect the variables and the call stack by using the Variables and Call Stack panels in the left sidebar.

Common Pitfalls and Mistakes to Avoid

Here are some common pitfalls and mistakes to avoid when debugging Python applications running in Docker containers:

• Make sure the container is running and the application is listening on the correct port.
• Make sure the docker configuration in the launch.json file is correct.
• Make sure the Python extension is installed and enabled in VS Code.

Best Practices and Optimization Tips

Here are some best practices and optimization tips to keep in mind when debugging Python applications running in Docker containers:

• Use a consistent naming convention for your containers and images.
• Use a Docker Compose file to manage your containers and services.
• Use a dockerignore file to exclude unnecessary files from the container.

Conclusion

Debugging Python applications running in Docker containers can be challenging, but with the right tools and configuration, it can be efficient and effective. By following the steps outlined in this post, you can set up VS Code to debug Python applications running in Docker containers and improve your productivity and debugging skills.