August 6, 2018

Multi-stage Docker builds for Python projects

Multi-stage builds can help reduce your Docker image sizes in production. This has many benefits: Development dependencies may potentially expose extra security holes in your system (I've yet to see this happen, but why not be cautious if it's easy to be so?), but mostly by reducing image size you make it faster for others to docker pull it.

The concept of multi-stage builds is simple: Install development dependencies, build all the stuff you need, then copy over just the stuff you need to run in production in a brand new image without installing development dependencies not needed to run the application.

Here's an example Dockerfile using the official Python Docker images, which are based on Debian - but you can easily apply the same principle when building from Debian, Ubuntu, CentOS, or Alpine images: Have one stage where build/development dependencies are installed and the application is built, and another where runtime dependencies are installed and the application is ran.

FROM python:3.7-stretch AS build
RUN python3 -m venv /venv

# example of a development library package that needs to be installed
RUN apt-get update && apt-get install libldap2-dev && \
    rm -rf /var/cache/apt/* /var/lib/apt/lists/*

# install requirements separately to prevent pip from downloading and
# installing pypi dependencies every time a file in your project changes
ADD ./requirements /project/requirements
RUN /venv/bin/pip install -r /project/requirements/$REQS.txt

# install the project, basically copying its code, into the virtualenv.
# this assumes the project has a functional
ADD . /project
RUN /venv/bin/pip install /project

# this won't have any effect on our production image, is only meant for
# if we want to run commands like pytest in the build image
WORKDIR /project

# the second, production stage can be much more lightweight:
FROM python:3.7-slim-stretch AS production
COPY --from=build /venv /venv

# install runtime libraries (different from development libraries!)
RUN apt-get update && apt-get install libldap-2.4-2 && \
    rm -rf /var/cache/apt/* /var/lib/apt/lists/*

# remember to run python from the virtualenv
CMD ["/venv/bin/python3", "-m", "myproject"]

Copying the virtual environment is by far the easiest approach to this problem. Python purists will say that virtual environments shouldn't be copied, but when the underlying system is the same and the path is the same, it makes literally no difference (plus virtual environments are a dirty hack to begin with, one more dirty hack doesn't make a difference).

There are alternate approaches such as downloading pypi packages or building dependencies as wheels and then copy those over, but it's more complicated and doesn't really have any benefits.

In our example, we install both project dependencies and the project itself into the virtualenv. This means we don't even need the project root directory in the production image, which is also nice (no risk of leaking example configuration files, git history etc.).

To build the image and run our project, assuming it's a webserver listening on port 5000, these commands should let you visit http://localhost:5000 in your browser:

$ docker build --tag=myproject .
$ docker run --rm -it -p5000:5000 myproject

Running tests

What if we want to build an image for running tests, which require some extra development dependencies? That's where the purpose of our ARG REQS comes in. By setting this build argument when running docker build, we can control which requirements file is read. Combine that with the --target argument to docker run and this is how you build a development/testing image:

$ docker build --target=build --build-arg REQS=dev --tag=myproject-dev .

And let's say you want to run some commands using that image:

$ docker run --rm -it myproject-dev /venv/bin/pytest
$ docker run --rm -it myproject-dev bash

Development in Docker

Note that you'll have to re-build the image any time code changes. I don't care too much about this since I do all my development locally anyway, and only use Docker for production and continuous integration, but if it's important to you, you'll have to:

  1. Change pip install /project to pip install -e /project
  2. Copy the entire /project directory into the production image as well
  3. Mount the project's root directory as /project with docker run --volume=$PWD:/project

Example project

If you want a functional example to play around with, I've made a git repository following these steps, which you can clone and play around with: