Build your Python image
- Build images
- Run your image as a container
- Use containers for development
- Configure CI/CD
- Deploy your app
- You understand basic Docker concepts.
- You’re familiar with the Dockerfile format.
- You have enabled BuildKit on your machine.
Now that you have a good overview of containers and the Docker platform, let’s take a look at building your first image. An image includes everything needed to run an application - the code or binary, runtime, dependencies, and any other file system objects required.
To complete this tutorial, you need the following:
- Python version 3.8 or later. Download Python
- Docker running locally. Follow the instructions to download and install Docker
- An IDE or a text editor to edit files. We recommend using Visual Studio Code.
The sample application uses the popular Flask framework.
Create a directory on your local machine named
python-docker and follow the steps below to activate a Python virtual environment, install Flask as a dependency, and create a Python code file.
$ cd /path/to/python-docker $ python3 -m venv .venv $ source .venv/bin/activate (.venv) $ python3 -m pip install Flask (.venv) $ python3 -m pip freeze > requirements.txt (.venv) $ touch app.py
Add code to handle simple web requests. Open the
python-docker directory in your favorite IDE and enter the following code into the
from flask import Flask app = Flask(__name__) @app.route('/') def hello_world(): return 'Hello, Docker!'
Test the application
Start the application and make sure it’s running. Open your terminal and navigate to the working directory you created.
$ cd /path/to/python-docker $ source .venv/bin/activate (.venv) $ python3 -m flask run
To test that the application is working, open a new browser and navigate to
Switch back to the terminal where the server is running and you should see the following requests in the server logs. The data and timestamp will be different on your machine.
127.0.0.1 - - [22/Sep/2020 11:07:41] "GET / HTTP/1.1" 200 -
Create a Dockerfile for Python
Now that the application is running, you can create a Dockerfile from it.
python-docker directory create a
Dockerfile and add a line that tells Docker what base image to use for the application.
# syntax=docker/dockerfile:1 FROM python:3.8-slim-buster
Docker images can inherit from other images. Therefore, instead of creating your own base image, you can use the official Python image that has all the tools and packages needed to run a Python application.
To learn more about creating your own base images, see Creating base images.
To make things easier when running the remaining commands, create a working directory. This instructs Docker to use this path as the default location for all subsequent commands. This means you can use relative file paths based on the working directory instead of full file paths.
Usually, the first thing you do with a project written in Python is to install
pip packages to ensure the application has all its dependencies installed.
pip3 install, you need the
requirements.txt file into the image. Use the
COPY command to do this.
COPY command takes two parameters. The first parameter tells Docker what file(s) you would like to copy into the image. The second parameter tells Docker where to copy that file(s) to. For this example, copy the
requirements.txt file into the working directory
COPY requirements.txt requirements.txt
requirements.txt file inside the image, you can use the
RUN command to run
pip3 install. This works exactly the same as running
pip3 install locally on a machine, but this time pip installs the modules into the image.
RUN pip3 install -r requirements.txt
At this point, you have an image based on Python version 3.8 and have installed the dependencies. The next step is to add the source code into the image. Use the
COPY command as with the
requirements.txt file. This
COPY command takes all the files located in the current directory and copies them into the image.
COPY . .
Now, tell Docker what command to run when the image is executed inside a container using the
CMD command. Note that you need to make the application externally visible (i.e. from outside the container) by specifying
CMD ["python3", "-m" , "flask", "run", "--host=0.0.0.0"]
Here’s the complete Dockerfile.
# syntax=docker/dockerfile:1 FROM python:3.8-slim-buster WORKDIR /app COPY requirements.txt requirements.txt RUN pip3 install -r requirements.txt COPY . . CMD ["python3", "-m" , "flask", "run", "--host=0.0.0.0"]
Just to recap, you created a directory on your local machine called
python-docker and created a simple Python application using the Flask framework. You used the
requirements.txt file to gather requirements, and created a Dockerfile containing the commands to build an image. The Python application directory structure should now look like the following:
python-docker |____ app.py |____ requirements.txt |____ Dockerfile
Build an image
Now that you’ve created the Dockerfile, let’s build the image. To do this, use the
docker build command. The
docker build command builds Docker images from a Dockerfile and a “context”. A build’s context is the set of files located in the specified PATH or URL. The Docker build process can access any of the files located in this context.
The build command optionally takes a
--tag flag. The tag sets the name of the image and an optional tag in the format
name:tag. Leave off the optional
tag for now to help simplify things. If you don’t pass a tag, Docker uses “latest” as its default tag.
Build the Docker image.
$ docker build --tag python-docker . [+] Building 2.7s (10/10) FINISHED => [internal] load build definition from Dockerfile => => transferring dockerfile: 203B => [internal] load .dockerignore => => transferring context: 2B => [internal] load metadata for docker.io/library/python:3.8-slim-buster => [1/6] FROM docker.io/library/python:3.8-slim-buster => [internal] load build context => => transferring context: 953B => CACHED [2/6] WORKDIR /app => [3/6] COPY requirements.txt requirements.txt => [4/6] RUN pip3 install -r requirements.txt => [5/6] COPY . . => [6/6] CMD ["python3", "-m", "flask", "run", "--host=0.0.0.0"] => exporting to image => => exporting layers => => writing image sha256:8cae92a8fbd6d091ce687b71b31252056944b09760438905b726625831564c4c => => naming to docker.io/library/python-docker
View local images
To see a list of images you have on your local machine, you have two options. One is to use the Docker CLI and the other is to use Docker Desktop. As you are working in the terminal already, take a look at listing images using the CLI.
To list images, run the
docker images command.
$ docker images REPOSITORY TAG IMAGE ID CREATED SIZE python-docker latest 8cae92a8fbd6 3 minutes ago 123MB
You should see at least one image listed, including the image you just built
As mentioned earlier, an image name is made up of slash-separated name components. Name components may contain lowercase letters, digits, and separators. A separator can include a period, one or two underscores, or one or more dashes. A name component may not start or end with a separator.
An image is made up of a manifest and a list of layers. Don’t worry too much about manifests and layers at this point other than a “tag” points to a combination of these artifacts. You can have multiple tags for an image. Let’s create a second tag for the image you built and take a look at its layers.
To create a new tag for the image you built, run the following command.
$ docker tag python-docker:latest python-docker:v1.0.0
docker tag command creates a new tag for an image. It doesn’t create a new image. The tag points to the same image and is just another way to reference the image.
Now, run the
docker images command to see a list of the local images.
$ docker images REPOSITORY TAG IMAGE ID CREATED SIZE python-docker latest 8cae92a8fbd6 4 minutes ago 123MB python-docker v1.0.0 8cae92a8fbd6 4 minutes ago 123MB python 3.8-slim-buster be5d294735c6 9 days ago 113MB
You can see that two images start with
python-docker. You know they’re the same image because if you take a look at the
IMAGE ID column, you can see that the values are the same for the two images.
Remove the tag you just created. To do this, use the
rmi command. The
rmi command stands for remove image.
$ docker rmi python-docker:v1.0.0 Untagged: python-docker:v1.0.0
Note that the response from Docker tells you that Docker didn’t remove the image, but only “untagged” it. You can check this by running the
docker images command.
$ docker images REPOSITORY TAG IMAGE ID CREATED SIZE python-docker latest 8cae92a8fbd6 6 minutes ago 123MB python 3.8-slim-buster be5d294735c6 9 days ago 113MB
Docker removed the image tagged with
:v1.0.0, but the
python-docker:latest tag is available on your machine.
This module looked at setting up an example Python application used for the rest of the tutorial, and created a Dockerfile used to build the Docker image. It also looked at tagging and removing images.
The next module will take a look at how to: