aarch64 organization is deprecated in favor of the more-specific
arm64v8 organization, as per https://github.com/docker-library/official-images#architectures-other-than-amd64. Please adjust your usages accordingly.
Supported tags and respective
THESE IMAGES ARE VERY EXPERIMENTAL; THEY ARE PROVIDED ON A BEST-EFFORT BASIS WHILE docker-library/official-images#2289 IS STILL IN-PROGRESS (which is the first step towards proper multiarch images)
PLEASE DO NOT USE THEM FOR IMPORTANT THINGS
This image is built from the source of the official image of the same name (
python). Please see that image's description for links to the relevant
If you are curious about specifically how this image differs, see the Jenkins Groovy DSL scripts in the
tianon/jenkins-groovy GitHub repository, which are responsible for creating the Jenkins jobs which build them.
Where to file issues:
the Docker Community
Supported Docker versions:
the latest release (down to 1.6 on a best-effort basis)
What is Python?
Python is an interpreted, interactive, object-oriented, open-source programming language. It incorporates modules, exceptions, dynamic typing, very high level dynamic data types, and classes. Python combines remarkable power with very clear syntax. It has interfaces to many system calls and libraries, as well as to various window systems, and is extensible in C or C++. It is also usable as an extension language for applications that need a programmable interface. Finally, Python is portable: it runs on many Unix variants, on the Mac, and on Windows 2000 and later.
How to use this image
Dockerfile in your Python app project
FROM python:3 WORKDIR /usr/src/app COPY requirements.txt ./ RUN pip install --no-cache-dir -r requirements.txt COPY . . CMD [ "python", "./your-daemon-or-script.py" ]
or (if you need to use Python 2):
FROM python:2 WORKDIR /usr/src/app COPY requirements.txt ./ RUN pip install --no-cache-dir -r requirements.txt COPY . . CMD [ "python", "./your-daemon-or-script.py" ]
You can then build and run the Docker image:
$ docker build -t my-python-app . $ docker run -it --rm --name my-running-app my-python-app
Run a single Python script
For many simple, single file projects, you may find it inconvenient to write a complete
Dockerfile. In such cases, you can run a Python script by using the Python Docker image directly:
$ docker run -it --rm --name my-running-script -v "$PWD":/usr/src/myapp -w /usr/src/myapp python:3 python your-daemon-or-script.py
or (again, if you need to use Python 2):
$ docker run -it --rm --name my-running-script -v "$PWD":/usr/src/myapp -w /usr/src/myapp python:2 python your-daemon-or-script.py
python images come in many flavors, each designed for a specific use case.
This is the defacto image. If you are unsure about what your needs are, you probably want to use this one. It is designed to be used both as a throw away container (mount your source code and start the container to start your app), as well as the base to build other images off of. This tag is based off of
buildpack-deps is designed for the average user of docker who has many images on their system. It, by design, has a large number of extremely common Debian packages. This reduces the number of packages that images that derive from it need to install, thus reducing the overall size of all images on your system.
This image does not contain the common packages contained in the default tag and only contains the minimal packages needed to run
python. Unless you are working in an environment where only the
python image will be deployed and you have space constraints, we highly recommend using the default image of this repository.
This image is based on the popular Alpine Linux project, available in the
alpine official image. Alpine Linux is much smaller than most distribution base images (~5MB), and thus leads to much slimmer images in general.
This variant is highly recommended when final image size being as small as possible is desired. The main caveat to note is that it does use musl libc instead of glibc and friends, so certain software might run into issues depending on the depth of their libc requirements. However, most software doesn't have an issue with this, so this variant is usually a very safe choice. See this Hacker News comment thread for more discussion of the issues that might arise and some pro/con comparisons of using Alpine-based images.
To minimize image size, it's uncommon for additional related tools (such as
bash) to be included in Alpine-based images. Using this image as a base, add the things you need in your own Dockerfile (see the
alpine image description for examples of how to install packages if you are unfamiliar).
This image feeds your
requirements.txt file automatically to
pip in order to make building derivative images easier. For most use cases, creating a
Dockerfile in the base of your project directory with the line
FROM python:onbuild will be enough to create a stand-alone image for your project.
onbuild variant is really useful for "getting off the ground running" (zero to Dockerized in a short period of time), it's not recommended for long-term usage within a project due to the lack of control over when the
ONBUILD triggers fire (see also
Once you've got a handle on how your project functions within Docker, you'll probably want to adjust your
Dockerfile to inherit from a non-
onbuild variant and copy the commands from the
Dockerfile (moving the
ONBUILD lines to the end and removing the
ONBUILD keywords) into your own file so that you have tighter control over them and more transparency for yourself and others looking at your
Dockerfile as to what it does. This also makes it easier to add additional requirements as time goes on (such as installing more packages before performing the previously-
This image is based on Windows Server Core (
microsoft/windowsservercore). As such, it only works in places which that image does, such as Windows 10 Professional/Enterprise (Anniversary Edition) or Windows Server 2016.
For information about how to get Docker running on Windows, please see the relevant "Quick Start" guide provided by Microsoft: