ChaosKit VS Poetry

Paul Bourke's website is great; don't forget to check his other articles!

Over 10 years ago, it inspired me to play with strange attractors, which eventually ended with me writing https://github.com/chaoskit/chaoskit.

It was fun and I learned a lot, but it's definitely a deep rabbit hole. I've moved on since then.

I'm not the parent, but I did something similar. It's an editor for fractal flames where the UI is done in QML and rendering and generation is done in C++ and OpenGL. https://github.com/chaoskit/chaoskit

I liked that QML allowed me to iterate quickly on the UI. It was really quick to just compose a bunch of components together and have something working. I also enjoyed the integration with the C++ side. Overall I found QML pretty solid. If I'd build a desktop app again, I'd definitely consider it.

https://github.com/chaoskit/chaoskit

Demo video: https://youtu.be/ZSz3zN14NTQ

It's an editor and renderer for Fractal Flames[1] written in C++17 and with a UI in Qt/QML. Other software that renders Fractal Flames is e.g. Electric Sheep[2] or Apophysis[3].

It's a project that I've been working on and off for 10 years and it's still not ready… Reimplementing it several times certainly didn't help, but I learned a lot in the process! It's grown from a simple for loop to basically a language interpreter.

Sorry for the lack of README or license, but this is still half-baked ;)

[1] https://flam3.com/

[2] https://electricsheep.org/

[3] https://en.wikipedia.org/wiki/Apophysis_(software)

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