manylinux VS Poetry

I use manylinux containers as the OS for compilation. It tries to ensure as much cross-os / libc / etc.. as much as possible for precompiled libraries. https://github.com/pypa/manylinux

Just to clarify for anyone who isn't aware, the "compiling issues", at least historically, have been that that Alpine uses musl, and PyPI's manylinux wheels are built against old glibc versions. So stuff like numpy that would trivially and quickly install from whl on glibc distros (like a bare-bones Ubuntu image) trigger compilations and the installation of build-only dependencies on Alpine.

That said, it looks like as of late-2021, at least some projects are offering musllinux wheels as well, per the discussion here: https://github.com/pypa/manylinux/issues/37 (not numpy, though: https://pypi.org/project/numpy/#files)

It's very hard. Incompatible glibc ABIs make this nigh impossible, there's a reason Steam installs a vcredistributable.dll for pretty much every game on Windows.

Look no further than the hoops you need jump through to distribute a Linux binary on PyPI [1]. Despite tons of engineering effort, and tons of hoop jumping from packagers, getting a non-trivial binary to run across all distros is still considered functionally impossible.

[1]: https://github.com/pypa/manylinux

As a user, if you build every python package from source, it's ok. But if you a maintainer of an OSS project and you need to publish binary packages for it, then you will hit the trouble. Binaries built on Ubuntu 20.04 can only support Ubuntu 20.04 and newer. So you'd better to choose an older Linux release to target broader users. Now most python packages choose CentOS 6 or 7. See https://github.com/pypa/manylinux/issues/1012 for more details. They need help!

I recently learned that Clang supports this kind of cross-compiling out of the box. https://mcilloni.ovh/2021/02/09/cxx-cross-clang/

The main difference is that Clang does not ship with headers/libraries for different platforms, as Zig appears to do. You need to give Clang a "sysroot" -- a path that has the headers/libraries for the platform you want to compile for.

If you create a bunch of sysroots for various architectures, you can do some pretty "easy" cross-compiling with just a single compiler binary. Docker can be a nice way of packaging up these sysroots (especially combined with Docker images like manylinux: https://github.com/pypa/manylinux). Gone are the days when you had to build a separate GCC cross-compiler for each platform you want to target.

Now you come and use manylinux to build. (https://github.com/pypa/manylinux) so you are based on the CentOS 7 toolchain (at best if you use manylinux2014) or Debian 9 toolchain (if you use manylinux_2_24).

I think the generally accepted way to do that would be a container image running a relatively old distribution. This is exactly what python packages do when they need to distribute binary packages on linux [0]. You are supposed to compile the package in a container (or VM) that runs CentOS 7 (or older if you want broader support), although now the baseline is moving gradually to Debian 9.

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

You can manage dependencies in Python with the package manager pip, which comes pre-installed with Python. Pip allows you to install and uninstall Python packages, and it uses a requirements.txt file to keep track of which packages your project depends on. However, pip does not have robust dependency resolution features or isolate dependencies for different projects; this is where tools like pipenv and poetry come in. These tools create a virtual environment for each project, separating the project's dependencies from the system-wide Python environment and other projects.

Poetry provides packaging and dependency management for Python. If you haven't already, install poetry via pip:

Poetry

The Semantify repository provides an example Astro.js project. Ensure you have poetry installed, then build the project from the root of the repository:

Has anyone else been paying attention to how hilariously hard it is to package PyTorch in poetry?

https://github.com/python-poetry/poetry/issues/6409

Based on this comment 5 days ago[0], it's working? I'm not sure didn't dig in too far but based on that comment it seems fair to say that it's not fully Poetry's fault because torch removed hashes (which poetry needs to be effective) for a while only recently adding it back in.

Not sure where I would stand if I fully investigated it tho.

[0] https://github.com/python-poetry/poetry/issues/6409#issuecom...

We will be running this project in Python 3.10 on Mac/Linux, and we will use Poetry to manage our dependencies. Later, we will bundle our app into a container using docker for deployment.

Here are the two main packaging issues I run into, specifically when using Poetry:

1) Lack of support for building extension modules (as mentioned by the article). There is a workaround using an undocumented feature [0], which I've tried, but ultimately decided it was not the right approach. I still use Poetry, but build the extension as a separate step in CI, rather than kludging it into Poetry.

2) Lack of support for offline installs [1], e.g. being able to download the dependencies, copy them to another machine, and perform the install from the downloaded dependencies (similar to using "pip --no-index --find-links=."). Again, you can work around this (by using "poetry export --with-credentials" and "pip download" for fetching the dependencies, then firing up pypiserver [2] to run a local PyPI server on the offline machine), but ideally this would all be a first class feature of Poetry, similar to how it is in pip.

I don't have the capacity to create Pull Requests for addressing these issues with Poetry, and I'm very grateful for the maintainers and those who do contribute. Instead, on the linked issues I share my notes on the matter, in the hope that it may at least help others and potentially get us closer to a solution.

Regardless, I'm sticking with Poetry for now. Though to be fair, the only other Python packaging tools I've used extensively are Pipenv and pip/setuptools. It's time consuming to thoroughly try out these other packaging tools, and is generally lower priority than developing features/fixing bugs, so it's helpful to read about the author's experience with these other tools, such as PDM and Hatch.

[0] https://github.com/python-poetry/poetry/issues/2740

[1] https://github.com/python-poetry/poetry/issues/2184

[2] https://pypi.org/project/pypiserver/

We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.

I started using poetry. The problem is poetry will not install if there is dependency conflict and there is no way to ignore: github