Install Python On Docker Container

  1. The Docker Explorer provides an interactive experience to examine and manage Docker assets such as containers, images, and so on. To see an example: Install Python In Docker Container Bag. Navigate to the Docker Explorer. In the Containers tab, right-click on your container and choose View Logs. The output will be displayed in the terminal.
  2. Let’s get into how you can host Python on Docker. By the end of this tutorial, you’ll know how to create a docker container, add your app to the container, and keep it running without interfering with your system processes. If you’re a beginner in Python, go through this Python video course by following through the entire course map.
Ben Wilcock

Packaging your application code into Docker containers is a tricky business. Python code is no exception. There are a ton of best practices that you need to know about if you’re going to build a container that is safe, secure, and maintainable over the long term. Buildpacks codify these best practices, and they’re open-source, so they’re a great way to turn your application code into runnable containers.

And because buildpacks completely remove the need for a Dockerfile, they dramatically simplify the maintenance of your container images — particularly useful if you have multiple images to maintain. They’re no ‘flash-in-the-pan’ either. Developed by Heroku in 2011, and also used in Cloud Foundry, buildpacks have built and run millions of production workloads!

For Python developers, there are currently two implementations of the Buildpack standard which provide Python compatible buildpacks. One is from Google and the other is from Heroku. In this guide, you’ll learn how to use Heroku’s Python buildpack to create a container image for a sample Python application.

Using Selenium With Python in a Docker Container. — web-scraping, python, docker, selenium, testing — 3 min read. While web scraping, I came across many useful applications such as listing old prices of some financial assets or finding current news topics.

Before You Begin

There are a few things you need to do before getting started with Python Buildpacks:

  • Install Docker Desktop. The pack CLI requires the Docker daemon, so you’ll need to have that installed and running locally.

  • Check out Containers 101 on KubeAcademy, particularly if you’ve never worked with containers or Docker before.

  • Follow the documentation for installing pack in your local environment.

  • [Optional] If you are completely new to buildpacks, you might prefer to first read up on what are buildpacks?

Using Buildpacks With Python

Install python on docker container model

Buildpacks work the same way no matter what language the code is written in. In this guide you’ll use a simple Python application, but remember that the process works in the same way for Node.JS, Java, Go, PHP, and more.

Follow the steps below to quickly create a container image for a Python application using the Heroku Python Buildpack.

Get The Sample Python Application

Download the sample Python application from Github and make the sample application’s folder your current working directory as follows:

In the folder you will notice three text files, web.py, requirements.txt, and Procfile.

The file web.py contains a hello-world web application written in Python3 using the Flask and MarkupSafe libraries.

The file requirements.txt clarifies which libraries your application is dependent on, and their versions. Pip can generate the contents of this file for you using the command pip freeze.

The Procfile specifies the command-line used to execute the application at runtime. In this case the Procfile declares that the web.py file contains your FLASK_APP, calls flask run, and binds the web-server to the --host with the IP address 0.0.0.0. It is expected that the --port to bind the application to will be set as an environment variable using the name $PORT.

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Get The List Of Suggested Builders

There are many implementations of the Buildpacks standard. These implementations are called ‘builders’. To discover the very latest list of suggested builders, use the pack suggest-builders command as follows:

As you can see, both Heroku and Google Cloud Platform offer open-source Python compatible builders. For this exercise, you’ll use the Heroku builder, but you could just as easily use Google’s.

Set Heroku As Your Default Builder

Copy the name of the builder that you want to set as your default from the list above — in this case it’s heroku/buildpacks:18 — and use the pack set-default-builder command to set this buildpack as the default as shown below:

Use pack To Create Your Container Image

To run the builder and create your Python application container image, use the command pack build. Be sure to also specify an image name for the container in the format “<repository>/<container-name>:<tag>” as shown in the following example:

The process of building the image will now begin. The first time you do this, you will notice that docker is downloading a series of container image ‘layers.’ This is because buildpacks are also containers, so they must first be pulled by Docker before the buildpack can be run locally. Once these images are in your cache, the process is much quicker. The output looks something like this:

When you see the words “Successfully built image” the process is complete. Your new container image will now be available in your local Docker image repository. You can list the images in your local repository with the command docker images.

Note: Already we’re benefiting from buildpack engineering! Notice that the CREATED date says “40 years ago”. This is a best practice whereby the timestamps of layers are ‘zeroed’ to make container builds more reproducible, cache-able, and to avoid unnecessary image downloads. You can read more here.

Test The Container Image

Testing the container is no more difficult than running the image with the docker run command as follows:

Now the container image of your application is running in the background, simply query the http://localhost:8080 endpoint, either using a command-line tool like Httpie as shown below, or a regular web browser.

Your application will respond with the legend “Hello, World!” like so:

And you’re done! You built your sample Python application into an OCI compliant Docker container image without resorting to a Dockerfile. Heroku will take care of most of the underlying maintenance tasks on the operating system and the Python interpreter. All you need to do is re-run pack build and you’ll get a fresh image. If nothing changed, neither will your image.

Tidy Up

You can stop and remove the container from Docker as follows:

Keep Learning

Learn more about buildpacks right here on the Tanzu Developer Center with these great guides:

Find out more about what the pack tool can do by using the --help command and browsing the Buildpacks.io website.

If you liked this guide, you might find these others in our ‘Python Like A Pro’ series useful:

And if you’d prefer to see the buildpack in action without actually following the steps above, check out the accompanying YouTube video guide:

In this guide you will learn how to:

Install Python 3 Docker Container

  • 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.

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. 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. Docker Desktop and the VS Code Docker extension must be installed as described in the. FROM python:3.9.1 RUN apt-get update RUN apt-get -y install tesseract-ocr RUN apt-get install tesseract-ocr-ben ADD. /tesseract-python WORKDIR /tesseract-python RUN pip install -r requirements.txt. The From command is used to define the parent image. Here I’m using the pre-built official image of Python from Docker Hub.

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:

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. 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.

  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

Install Python 3.6 In Docker Container

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.

Install Python Into Docker Container

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

Docker Container Install Python 3

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.

Install Python Docker Container

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 3.7 In Docker Container

Install Python Docker Container Model

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 Docker Container Code

Install Python On Docker Container List

Next steps

Install Python 3.6 In Docker Container

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