Python in a container

In this guide you will learn how to:

  • Create a Dockerfile file describing a simple Python container.
  • Build, run, and verify the functionality of a Django, Flask, or General Python app.
  • Debug the app running in a container.

Prerequisites

  • Docker Desktop and the VS Code Docker extension must be installed as described in the overview.
  • For Python development, complete all Getting started with Python steps
  • A runnable Python application

Create a Python project

If you don't have a Python project already, follow these commands sequentially from the terminal:

pip install django
django-admin startproject helloworld
cd helloworld
code .

If you want to containerize a complete Django or Flask web app, you can use one of the following samples:

After verifying your app runs properly, you can now Dockerize your application.

Add Docker files to the project

  1. Open the project folder in VS Code.

  2. Open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and use the Docker: Add Docker Files to Workspace... command:

    Add Dockerfile to a Python project

  3. When the prompt appears, select Python: Django, Python: Flask, or Python: General as the app type. For this tutorial, we will select Python: Django.

  4. Select either Yes or No when prompted to include Docker Compose files. Compose is typically used when running multiple containers at once.

  5. Enter the relative path to the app's entry point. This excludes the workspace folder you start from. According to official Django documentation, this path is commonly manage.py (root folder) or subfolder_name/manage.py. According to official Flask documentation, this is the path to where you create your Flask instance.

    Tip: You may also enter the path to a folder name as long as this folder includes a __main__.py file.

  6. If Python: Django or Python: Flask was selected, specify app port for local development. Django defaults to port 8000 while Flask defaults to port 5000; however, any unused port will work. We recommend selecting port 1024 or above to mitigate security concerns from running as a root user.

  7. With all of this information, the Docker extension creates the following files:

    • A Dockerfile. To learn more about IntelliSense in this file, refer to the overview.

    • A .dockerignore file to reduce the image size by excluding files and folders that aren't needed such as .git, .vscode, and __pycache__.

    • If Docker Compose was selected, a docker-compose.yml and docker-compose.debug.yml file.

    • If one does not already exist, a requirements.txt file for capturing all app dependencies.

    Important note: To use our setup, the Python framework (Django/Flask) and Gunicorn must be included in the requirements.txt file. If the virtual environment/host machine already has these prerequisites installed and is supposed to be identical to the container environment, ensure app dependencies are ported over by running pip freeze > requirements.txt in the terminal. This will overwrite your current requirements.txt file.

Add an environment variable to the image

The Docker Extension helps you author Dockerfiles by using IntelliSense to provide auto-completions and contextual help. To see this feature in action:

  1. Open the Dockerfile.

  2. Underneath the EXPOSE statement, type ⌃Space (Windows, Linux Ctrl+Space) to trigger IntelliSense and scroll to ENV.

    Adding environment variable to Dockerfile

  3. Press Tab or Enter to complete the statement, then set the key to VAR1 and the value to 10.

Gunicorn modifications for Django/Flask apps

To give Python Web Developers a great starting point, we chose to use Gunicorn as the default web server. Since it is referenced in the default Dockerfile, it is included as a dependency in the requirements.txt file.

Note: To use Gunicorn as your web server, it must be included in the requirements.txt file as an app dependency. It does not need to be installed in your virtual environment/host machine.

Django Apps

To use Gunicorn, it must bind to an application callable (what the application server uses to communicate with your code) as an entry point. This callable is declared in the wsgi.py file of a Django application. To accomplish this binding, the final line in the Dockerfile says:

CMD ["gunicorn", "--bind", "0.0.0.0:8000", "{workspace_folder_name}.wsgi"]`

If your project does not follow Django's default project structure (that is, a workspace folder and a wsgi.py file within a subfolder named the same as the workspace) you must overwrite the Gunicorn entry point in the Dockerfile to locate the correct wsgi.py file.

Tip: If your wsgi.py file is in the root folder, the final argument in the command above will be "wsgi". Within subfolders, the argument would be "subfolder1_name.subfolder2_name.wsgi".

Flask Apps

To use Gunicorn, it must bind to an application callable (what the application server uses to communicate with your code) as an entry point. This callable corresponds with the file location and variable name of your created Flask instance. According to official Flask Documentation, users generally create a Flask instance in the main module or in the __init__.py file of their package in this manner:

from flask import Flask
app = Flask(__name__) # Flask instance named app

To accomplish this binding, the final line in the Dockerfile says:

CMD ["gunicorn", "--bind", "0.0.0.0:5000", "{subfolder}.{module_file}:app"]

During the Docker: Add Docker Files to Workspace... command, you configure the path to the Flask instance, however, the Docker extension assumes your Flask instance variable is named app. If this is not the case, you must change the variable name in the Dockerfile.

Tip: If your main module was in the root folder as a file named main.py and had a Flask instance variable was named myapp, the final argument in the command above will be "main:myapp". Within subfolders, the argument would be "subfolder1_name.subfolder2_name.main:myapp".

Build, run, and debug the container

The Docker: Add Docker Files to Workspace... command automatically creates a Docker launch configuration to build and run your container in debug mode. To debug your Python app container:

  1. Navigate to the manage.py file and set a breakpoint on this line:

      os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'helloworld.settings')

    Note: If you have created an app project as shown in the Create a Django app section of the Django tutorial, you may set a breakpoint in views.py or wherever you choose.

  2. Navigate to Run and Debug and select Docker: Python - Django.

    Selected Docker debug configuration

  3. Start debugging using the F5 key.

    • The Docker image builds.
    • The Docker container runs.
    • The python debugger stops at the breakpoint in manage.py.
  4. Step over this line once.

  5. Navigate to the Debug Console and type os.environ["DJANGO_SETTINGS_MODULE"]

  6. Once you view the output, press continue.

The Docker extension will launch your browser to a randomly mapped port:

Django website launches

Tip: To modify your Docker build settings, such as changing the image tag, navigate to .vscode -> tasks.json under the dockerBuild attribute in the docker-build task. Use IntelliSense within the file (⌃Space (Windows, Linux Ctrl+Space)) to display all other valid directives.

Use the Docker view

The Docker view provides an interactive experience to examine and manage Docker assets such as containers, images, and so on. To see an example:

  1. Navigate to the Docker view.

  2. In the Containers tab, right-click on your container and choose View Logs.

    Viewing the logs of a container

  3. The output will be displayed in the terminal.

Next steps

You're done! Now that your container is ready, you may want to: