Tsukasa-credit-card-gag-scam VS Poetry

One workaround that I can think of would be to build everything using GitHub Actions, as then your own system would not matter at all. I have a great example project for that, all you really need to do is create a YAML file in a directory called .github/WORKFLOWS (the filename itself doesn't really matter), you can use this as a base. Just gotta swap out Nuitka for PyInstaller (if you want to), and change how the dependencies are installed. This makes it so that whenever you push a Git tag with a version number (say, v1.0.0), GitHub will then run this script, build executables (on any operating systems you want, no less), then create a release with them available for download. Mine also adds a changelog, but you can just remove that part.

If your company uses GitHub or GitLab, be it internal or the online version, you could create a release on the project page with your built binaries attached for download. One of my projects should work fine as an example. The releases page is linked on the sidebar. The neat thing with this is that you can automate the whole build and release process; I get a new release whenever I push a Git tag with a version number.

Ideally you'd make your project "installable", and use absolute imports for everything. This way, when your project is installed as a package, assuming there are no circular dependencies any part of it can import from any other part. Mainly this makes the job of your unit tests a lot easier. Either of these two examples will probably showcase that just fine.

However, if that's not the case for your project, such as if you have an extra src directory separating the repository root and the package(s), you'll need to be explicit. In another project I did exactly that:

I used it in this project as a test, before I made the decision to transition all my projects from Pylint and Flake8 to Ruff: https://github.com/Diapolo10/Tsukasa-credit-card-gag-scam

One of my own projects handles this with a function, which then gets used thorough the program:

Instead of keeping all the code at the repository root, maybe consider a more traditional project structure. As far as examples go, I've got this for an executable, and I think this works for a more complex project.

None of my current projects build platform-dependent releases, but I think this example is close enough. It would just take some tweaking.

I did something like that myself. I found Bleeplo's video about an attempt at recreating a certain meme image as a real tkinter program, and I enjoyed the idea so much I ended up making a fork of the project, improved upon the original, and even made a pull request to the original project with some of my cleanup. Forked projects always link back to the original, and all forks are visible from the original's settings.

pyproject.toml already lists the dependencies, requirements.txt is not needed nor used in the newer standard. In fact, it can list your development dependencies as well, like here for example.

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