Introduction
Developers often use Docker to build, ship, and run applications in a consistent environment. And the integration of Docker with VSCode provides a streamlined development experience for them. In this article, we’ll show you how to use Docker within VS Code to build, run, and debug Python applications.
Installing the necessary extensions
First, we need to install two VS Code extensions:
- ms-vscode-remote.remote-containers (to have a full-featured VS Code environment inside a Docker container)
- ms-azuretools.vscode-docker (to manage Docker images and containers directly from the editor)
Once you’ve installed these extensions, you should see a new Docker icon in the left-hand Activity Bar of VS Code:
- Containers
- Images
Task with Container:
Ctrl+Shift+P→ > Dev Containers: …- Or, Open a Remote Windown (Status Bar) →
Building and running a Docker container
To build a Docker container for our Python project, we need to create a Dockerfile that specifies the container’s environment. In this example, we’ll create a simple Dockerfile that installs Python 3.9:
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# Dockerfile
FROM ubuntu:20.04
RUN apt-get update && \
apt-get install -y python3.9 python3.9-dev
COPY . /home
WORKDIR /home
In the same directory as our Dockerfile, we also have a Python file called test.py.
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$ tree
├── Dockerfile
└── test.py
To build the Docker image, we can
- Right-click on the Dockerfile → Build Image → Set image tag name (e.g. testdocker:39)
- Or, we can run the following command in the terminal
docker build -t testdocker:39
Once we’ve built the image, we can run a container using the following command:
- Or, Run container by right click on the built image (e.g. testdocker:39) (IMAGES < Docker < Activity Bar) → Run Interactive → Right click on the running container (e.g. testdocker:39) (CONTAINERS < Docker < Activity Bar) → Attach Visual Studio Code
- Or,
Ctrl+Shift+P→ > Dev Containers: Reopen in container - Or,
docker run -it testdocker:39→ Right click on the running container (e.g. testdocker:39) (CONTAINERS < Docker < Activity Bar) → Attach Visual Studio Code
Debugging inside Docker container
Once we’re inside the container, we can open the project folder in VS Code by using the Open Folder (Ctrl+O). Then, we can install the ms-python.python (→ Install in Dev Container: Existing Dockerfile) extension for Python and set up a launch configuration .vscode/launch.json for debugging.
Here’s an example launch configuration for a Python file called test.py:
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// .vscode/launch.json
{
"version": "0.2.0",
"configurations": [
{
"name": "Python project",
"type": "python",
"request": "launch",
"program": "test.py",
"pythonPath": "/usr/bin/python3.9",
"console": "integratedTerminal",
"cwd": "${workspaceFolder}",
// "args": ["", ""]
}
]
}
With the launch configuration set up, we can use VS Code’s debugging tools to step through our Python code and troubleshoot any issues.
Conclusion
By using Docker with VS Code, we can build, run, and debug Python applications in a consistent environment that’s isolated from our host machine. This approach can save time and effort, and it helps ensure that our applications will run correctly in any environment.
