Install Python In Docker Container


Note: Removing a Container is FINAL. Delete a Single Container. Run docker ps -a and retrieve the container ID (an alphanumeric string, something like a39c259df462). Run docker rm a39c259df462 to remove just that container. Delete All Your Containers. To delete all your containers, run: $. Oct 13, 2020 The -docker option will generate a Dockerfile that we can edit to install custom libraries and dependencies in the Azure Functions app where the container will be deployed: func init OcrFunctionsProject -worker-runtime python -docker. Docker-python Kaggle Notebooks allow users to run a Python Notebook in the cloud against our competitions and datasets without having to download data or set up their environment. This repository includes our Dockerfiles for building the CPU-only and GPU image that runs Python Notebooks on Kaggle. Apr 15, 2020 To install packages in a docker container, the packages should be defined in the Dockerfile. If you want to install packages in the Container, use the RUN statement followed by exact download command. $ RUN pip install //IN Windows $ RUN apt-get install //in Ubuntu $ RUN yum install //CentOS/RHEL.

  1. Install Python In Docker Container Tote
  2. Install Python In Docker Container Tracking
  3. Install Python In Ubuntu Docker Container
  4. Install Python 3 In Docker Container

Dockerfiles enable you to create your own images. A Dockerfile describes the software that makes up an image. Dockerfiles contain a set of instructions that specify what environment to use and which commands to run.

Creating a Dockerfile

First, start with a fresh empty directory. In our example, we call this my_new_docker_build – but feel free to use whatever name you like. This directory defines the context of your build, meaning it contains all of the things you need to build your image.

Create a new text file in my_new_docker_build called Dockerfile (note no extension; on Windows, you may need to save the file as “All types” and put the filename in quotes to avoid automatically appending an extension); use whatever text file editor you already know (you might use Sublime, Notepad++, emacs, nano, or even vi). In our example, we use the basic Python 3 image as our launching point. Add the following line to your Dockerfile:

We want to run a basic Python script which we’ll call my_script.py. First, we need to add the script to the Dockerfile:

Our script depends on the Python pyStrich library (pyStrich generates 1D and 2D barcodes), so we need to make sure we install that before we run my_script.py! Add this line to your Dockerfile to install random:

Add this line to your Dockerfile to execute the script:

Install Python In Docker Container

Your Dockerfile should look like this:

  • FROM tells Docker which image you base your image on (in the example, Python 3).
  • RUN tells Docker which additional commands to execute.
  • CMD tells Docker to execute the command when the image loads.

The Python script my_script.py looks like the following:

Now you are ready to build an image from this Dockerfile. Run:

Run Your Image

After your image has been built successfully, you can run it as a container. In your terminal, run the command docker images to view your images. You should see an entry for “python-barcode”. Run the new image by entering:

You should see what looks like a large ASCII QR code.


If you only need to run a simple script (with a single file), you can avoid writing a complete Dockerfile. In the examples below, assume you store my_script.py in /usr/src/widget_app/, and you want to name the container my-first-python-script:

Python 3:


Install Python In Docker Container Tote

Python 2:

Further information

Creating a Dockerfile

Make sure you do not append an extension to the Dockerfile (i.e., Docker does not recognize Dockerfile.txt).

You do not have to read the contents of every Dockerfile you base yours on, but make sure to at least familiarize yourself with them; you can avoid trying to install redundant software (e.g., installing pip when the Python image already loads it), and you can make sure you write your RUN commands appropriately. Docker Hub does not enforce basing all images off only one distribution of Linux; if you use a Debian-based distribution (Debian, Ubuntu, Mint, etc.) you need to call apt-get to install software, and if you use a Red Hat-based distribution (Red Hat Enterprise Linux/RHEL, CentOS) you need to use yum. Gaining familiarity early prevents redoing your work and saves time.

You might end up starting with an unfamiliar base image (i.e., if you primarily use CentOS and want to run a Python installation, the Python image extends Debian Jessie, so you need to use caution in how you write your RUN directives). If you maintain familiarity with Ubuntu, using Debian does not offer too many challenges (Ubuntu came from an offshoot of Debian Linux).

Avoid putting any unused files in your build directory. Docker makes tarballs of everything in the current directory and sends that to the Docker daemon, so if you have unnecessary files, those are included.


Do not attempt to run a script requiring dependencies using the Alternative method, unless those dependencies come with the bare Python installation.

Deleting Docker Containers

Run the following command from your docker console to see a list of your containers:

Note: Removing a Container is FINAL.

Delete a Single Container

  1. Run docker ps -a and retrieve the container ID (an alphanumeric string, something like a39c259df462).
  2. Run docker rm a39c259df462 to remove just that container.

Delete All Your Containers

To delete all your containers, run:

  • -q prints only the container ID’s
  • -a prints all containers
  • passing all container IDs to xargs, docker rm deletes all containers

Deleting Docker Images

Delete a Single Image

  1. Retrieve the Image ID using docker images (The Image IDs should be in the third column.)
  2. Run docker rmi <image_id>

For example:

Delete All Untagged Images

This requires a little bit of Linux magic (like deleting all containers above). Docker marks images without tags with '<none>' so we need to process only those images. Run the following command from your terminal (the awkprogramming language gives you text manipulation tools):

Delete All Images

To delete all of your images, you can simplify the command above:

Delete Docker Containers


allAll containers
${id}The container corresponding to the container ID you pass

Delete Docker Images


allAll images
untaggedImages tagged with “' (untagged images)
${id}The image corresponding to the Image ID you pass

Next: Dockerize your Django Application
Get started with dockerizing your Django application.

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.


  • 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:

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

  • python-sample-vscode-django-tutorial, which is the result of following the Django Tutorial

  • python-sample-vscode-flask-tutorial, which is the result of following the Flask Tutorial

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:

  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. 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.

  5. 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.

  6. Select either Yes or No when prompted to include Docker Compose files. If you select Yes, you will need to verify the path to your wsgi.py file in the Dockerfile to run the Compose Up command successfully. Compose is typically used when running multiple containers at once.

  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.

  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. The Gunicorn entry point is overridden locally if your app is run with Python: Django or Python: Flask.

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:

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:

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

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:

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

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

  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:

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 Explorer

Install Python In Docker Container Tracking

The Docker Explorer 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 Explorer.

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

  3. The output will be displayed in the terminal.

Install Python In Ubuntu Docker Container

Next steps

Install Python 3 In Docker Container

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

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