mu1 VS Poetry

I got sick of juggling code that migrated from one category to the other, so I wrote a little script that deals with chopping up a large source file into multiple TUs before feeding them to the compiler.

https://github.com/akkartik/mu1/blob/master/build2

More details: https://news.ycombinator.com/item?id=33574154#33575045

This really speaks to me. Modern software is too hard to assemble from source. If you're shipping sources, every moving part you add increases the odds of something going wrong on other people's computers.

It's worth having some skepticism of tools. By making some operations easy, tools encourage them. Build systems make it easy to bloat software. Package managers make it easy to bloat dependencies. This dynamic explains why Python in particular has such a terrible package management story. It's been around longer than Node or Rust, so if they seem better -- wait 10 years!

For many of my side projects I try to minimize moving parts for anyone (usually the '1' is literally true) who tries them out. I work in Unix, and one thing I built is a portable shell script that acts like a build system while being much more transparent about what it does: https://codeberg.org/akkartik/basic-build

When I use this script my build instructions are more verbose, but I think that's a good thing. They're more explicit for newcomers, and they also impose costs that nudge me to keep my programs minimalist.

You can see this build system evolve to add partial builds and parallel builds in one of my projects:

https://github.com/akkartik/mu1/blob/master/build0

https://github.com/akkartik/mu1/blob/master/build1

https://github.com/akkartik/mu1/blob/master/build2

https://github.com/akkartik/mu1/blob/master/build3

https://github.com/akkartik/mu1/blob/master/build4

Each of these does the same thing for this one repo -- build it -- but adding successively more bells and whistles.

I think providing just the most advanced version, build4, would do my users a disservice. It's also the most likely to break, where build0 is rock solid. If my builds do break for someone, they can poke around and downgrade to a simpler version.

Totally agreed!

Here's a prototype from a few years ago where I tried to make this easier: https://github.com/akkartik/mu1#readme (read just the first few paragraphs)

I still think the full answer lies in this direction.

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