Unfortunately in many (most?) cases I've seen the prescribed deployment practice for Python software is to use virtualenv (or some variant of it). Python dependencies are to be handled exclusively via pip (requirements.txt, install_requires in setup.py, etc.) and using distro-packaged Python modules is frowned upon because they are considered to be outdated.

I'm not saying that's the best practice. Personally I like to use distro-packaged stuff as much as possible because it's less volatile and tends to go through some more review than things on PyPI. But the virtualenv/pip exclusive method does seem to prevail in the field in my experience, so the advice to just use Alpine packages isn't useful in many cases.

Absolutely agree. Which is why I don't use Alpine linux for python projects and wouldn't recommend anyone else does.

In the article, they are only using pandas and not using a requirements.txt file. So it's a pretty perfect case to just use apk. In cases where you don't need many packages, you can still use Alpine if you would like without most of the downsides mentioned in the article.

I write a decent amount of python on alpine, in general I stick to the standard library and occasionally something like pyserial, sqlite, or numpy for specific things. IMO this attitude of pulling in dozens of packages with pip is pretty dangerous, I don’t care which language you’re using.

What's the alternative?

Removing features, moving them into micro services, and sometimes writing a class or two yourself.

That just sounds like distributing the dependencies. You'll still install them all, just not in the same spot.

It is best practice, but you probably don’t need virtualenv if you’re deploying a single application in a docker container.

Messing with the system python without using some form of virtual environment is a recipe for disaster.

In the docker container you ARE the system python version. In fact many docker best practices would say that preferably only 1 application is running, and that would be your application.

It's still not safe. For example there was a conflict one day between awscli, urllib and some system tool (apt?) which led to a broken (apt fails) system if you just "pip install awscli" globally. Even in docker image, you still need virtualenv to be safe (or use only global distro packages).

Could you elaborate on why? I worked at a co that almost exclusively ran Alpine containers with a single Python app. No use of virtualenv. Never experienced any hangups with this.

Same here, and I'm curious to know why some think virtualenv is required in a container, as opposed to just starting with am image containing a clean python install and adding what you need.

I linked the example here https://news.ycombinator.com/item?id=22186046

But tlrd is: if you "apt install some-utility", then "pip install something else", you may have upgraded packages that some-utility relies on but is not compatible with the new version anymore.

Pip and apt install to different folders already. In the very unlikely event there’s an issue in a container the solution is simple, modify the sys.path. The other 99% of the time avoiding the venv is a win.

Sure it's unlikely, but it happened at least once now, and that's exactly the situation venv is designed to avoid. Pip and apt install to different places (by default/convention), but you end up importing the pip version by default. You can change sys.path to work around it of course... but that's pretty much what virtualenv does anyway. Utilities by default do not change that path which leads to issues like the linked one.

I'm not saying never install globally, just let's keep in mind that this can lead to real issues which may be very surprising / hard to debug once in production. Unless you understand exactly what and how is delivered with every change, defaulting to venv is a safer option.

Complexity on top of complexity makes things harder to debug in my opinion. I’ve never had trouble debugging library issues and they’ve never happened to me in a container. Even easier is to not mix tools in a container anyway. Less is more.

It can be a tool used as a dependency of your app. And I feel like you can make the same claim about locks. They just introduce complexity and make debugging harder, race condition never happened to me anyway... It's about ensuring edge cases don't bite you when you don't expect it.

If you can prove it doesn't apply, then sure, why not install globally.

Looked up the issue and it was cloud-init, not apt that failed. Here are the details: https://github.com/aws/aws-cli/issues/3678

In one sentence: "Installing the latest aws-cli on an image is preventing new AMIs based on that image from booting due to the above issue with urllib3."

While this specific issue wouldn't affect docker images since you normally don't run cloud-init on them, it's just luck that it wasn't some other utility affected instead. Next time it can affect docker images too.

Why do you have cloud-init in a container?

It's a docker box, you're already inside some form of virtual environment

Not in docker. The entire point of docker is isolation so when you use something like python3.7-slim, its fine to use the system python. Thats the whole point of using the image.

Now you still want to run and install pip packages as a non-root user of course, but you don't need a virtualenv in docker.

Debian distros have a separate dist-packages directory for system libs. You can do whatever you want in site-packages. It is effectively like a default virtualenv when operating in a container.

For those like me who have no idea what "wheels" is in this context, I think it is referring to https://github.com/pypa/wheel

This looks like some sort of binary Python packaging thingy. I think @dkarp is saying that there exists pre-existing binary builds for glibc for many things, but not for musl libc so they are forced to be built explicitly when using pip to manage Python dependencies.

I could not figure out where this repository of binary packages is or who maintains it. Any corrections would be appreciated.

You've got it. Wheels in Python are like gems in ruby. (Or eggs in [python minus a few years].) The official Python package index has binary images, but they're compiled against glibc, and therefore don't work with Alpine. So when you `pip install` a package with binary dependencies on Alpine, it needs to compile them. Which can lead to a bit of a trip down a rabbit hole.

As others have mentioned, Alpine's repository does include Alpine-compatible binary versions of many popular Python packages. So you can often save yourself a lot of trouble by using apk instead of pip to get those.

> The official Python package index has binary images, but they're compiled against glibc, and therefore don't work with Alpine.

That’s a false statement (depending on interpretation). Whether a PyPI package has wheels is entirely at the discretion of the package maintainer, who is responsible for compiling all source and binary distributions; PyPI does not compile anything and only accepts uploads, verbatim. If the maintainer doesn’t upload wheels or doesn’t upload wheels for your platform, then no wheels for you (not from PyPI). In general compiling statically linked wheels (when you have C extensions and external dependencies) is a fairly involved process.

At the moment there are six platform tags for Linux wheels supported by PyPI: manylinux{1,2010,2014}_{x86_64,i686}. Each is based on a CentOS release with very old glibc. See

https://github.com/pypa/manylinux

PEP 513 - maynylinux1 https://www.python.org/dev/peps/pep-0513/

PEP 571 - manylinux2010 https://www.python.org/dev/peps/pep-0571/

PEP 599 - manylinux2014 https://www.python.org/dev/peps/pep-0599/

The wheels are in regular PyPI. They are uploaded by package maintainers. See for example here: https://pypi.org/project/pandas/#files

Pre-built binary distributions ("wheels") and source distributions (which sometimes need a compilation step) just live side by side on pypi.org, the public python package index.

> If you just need pandas, then you can install it with apk along with many other packages and it'll be much quicker: https://pkgs.alpinelinux.org/packages?page=1&branch=edge&nam....

And so again need to rely on something "non-standard".

> Alpine are doing what they can, by maintaining common python packages on apk. It is pip that is tied to glibc

The "manylinux" tag means glibc by necessity, because nobody has driven a PEP for tagging, detecting and managing musl-based wheels to completion[0]. The issue is not restricted to the choice of libc, or even linux alone, either. But again, that requires people actually put in the work to chip at and fix the issue[1].

[0] https://github.com/pypa/manylinux/issues/37

[1] https://github.com/pypa/packaging-problems/issues/69

Not everyone uses pip. Many data scientists use conda. Using you distribution's package manager is a legitimate solution.

The point is though, that this is a python packaging issue. Not an Alpine issue and not an issue that Alpine can do much about. As you linked, pypa have a big task to make packages work on every possible platform. Unfortunately, this comes as an unexpected surprise for anyone who wants to try out Alpine - maybe pip should show a warning when wheels aren't available because of musl.