Build your Python image



Now that we have a good overview of containers and the Docker platform, let’s take a look at building our 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:

Sample application

Let’s create a simple Python application using the Flask framework that we’ll use as our example. Create a directory in your local machine named python-docker and follow the steps below to create a simple web server.

$ 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

Now, let’s add some code to handle simple web requests. Open this working directory in your favorite IDE and enter the following code into the file.

from flask import Flask
app = Flask(__name__)

def hello_world():
    return 'Hello, Docker!'

Test the application

Let’s start our application and make sure it’s running properly. 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 properly, open a new browser and navigate to http://localhost:5000.

Switch back to the terminal where our server is running and you should see the following requests in the server logs. The data and timestamp will be different on your machine. - - [22/Sep/2020 11:07:41] "GET / HTTP/1.1" 200 -

Create a Dockerfile for Python

Now that our application is running properly, let’s take a look at creating a Dockerfile.

Next, we need to add a line in our Dockerfile that tells Docker what base image we would like to use for our application.

# syntax=docker/dockerfile:1

FROM python:3.8-slim-buster

Docker images can be inherited from other images. Therefore, instead of creating our own base image, we’ll use the official Python image that already has all the tools and packages that we need 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 rest of our commands, let’s create a working directory. This instructs Docker to use this path as the default location for all subsequent commands. By doing this, we do not have to type out full file paths but can use relative paths based on the working directory.


Usually, the very first thing you do once you’ve downloaded a project written in Python is to install pip packages. This ensures that your application has all its dependencies installed.

Before we can run pip3 install, we need to get our requirements.txt file into our image. We’ll use the COPY command to do this. The 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 you want that file(s) to be copied to. We’ll copy the requirements.txt file into our working directory /app.

COPY requirements.txt requirements.txt

Once we have our requirements.txt file inside the image, we can use the RUN command to execute the command pip3 install. This works exactly the same as if we were running pip3 install locally on our machine, but this time the modules are installed into the image.

RUN pip3 install -r requirements.txt

At this point, we have an image that is based on Python version 3.8 and we have installed our dependencies. The next step is to add our source code into the image. We’ll use the COPY command just like we did with our requirements.txt file above.

COPY . .

This COPY command takes all the files located in the current directory and copies them into the image. Now, all we have to do is to tell Docker what command we want to run when our image is executed inside a container. We do this using the CMD command. Note that we need to make the application externally visible (i.e. from outside the container) by specifying --host=

CMD [ "python3", "-m" , "flask", "run", "--host="]

Here’s the complete Dockerfile.

# syntax=docker/dockerfile:1

FROM python:3.8-slim-buster


COPY requirements.txt requirements.txt
RUN pip3 install -r requirements.txt

COPY . .

CMD [ "python3", "-m" , "flask", "run", "--host="]

Directory structure

Just to recap, we created a directory in our local machine called python-docker and created a simple Python application using the Flask framework. We also used the requirements.txt file to gather our requirements, and created a Dockerfile containing the commands to build an image. The Python application directory structure would now look like:

|____ requirements.txt
|____ Dockerfile

Build an image

Now that we’ve created our Dockerfile, let’s build our image. To do this, we 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 is used to set the name of the image and an optional tag in the format name:tag. We’ll leave off the optional tag for now to help simplify things. If you do not pass a tag, Docker uses “latest” as its default tag.

Let’s build our first 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
 => [1/6] FROM
 => [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="]
 => exporting to image
 => => exporting layers
 => => writing image sha256:8cae92a8fbd6d091ce687b71b31252056944b09760438905b726625831564c4c
 => => naming to

View local images

To see a list of images we have on our local machine, we have two options. One is to use the CLI and the other is to use Docker Desktop. As we are currently working in the terminal let’s take a look at listing images using the CLI.

To list images, simply 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, the image we just built python-docker:latest.

Tag images

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 is defined as 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. Do not 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 we built and take a look at its layers.

To create a new tag for the image we’ve built above, run the following command.

$ docker tag python-docker:latest python-docker:v1.0.0

The docker tag command creates a new tag for an image. It does not 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 our 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 we have two images that start with python-docker. We know they are 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.

Let’s remove the tag that we just created. To do this, we’ll 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 us that the image has not been removed but only “untagged”. 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

Our image that was tagged with :v1.0.0 has been removed, but we still have the python-docker:latest tag available on our machine.

Next steps

In this module, we took a look at setting up our example Python application that we will use for the rest of the tutorial. We also created a Dockerfile that we used to build our Docker image. Then, we took a look at tagging our images and removing images. In the next module we’ll take a look at how to:

Run your image as a container


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