gh-action-pypi-publish VS pip

> Recently I've seen someone on Reddit trying to automate the creation of PyPI projects through GitHub Actions. The person was complaining that the first deployment couldn't use an API key for that project since it didn't exist. So I'm not surprised some people are trying to do the same for malicious purposes.

Sorry for the tangent, but: you can do this now! If you use trusted publishing, you can register a "pending publisher" for a project that doesn't exist yet. When the trusted publisher (like GitHub Actions) is used, it'll create the project[1].

All of this is supported transparently by the official publishing action for GitHub Actions[2].

[1]: https://docs.pypi.org/trusted-publishers/creating-a-project-...

[2]: https://github.com/pypa/gh-action-pypi-publish

In the documentation example, I see that the action yaml file contains the line uses: pypa/gh-action-pypi-publish@release/v1. I have never done this before and almost went with that, but I am not sure why the example shows v1 hardcoded, so I don't think I actually want this to happen. It doesn't seem to be well explained though, and the pypi-publish action repo was also quiet on this. Is this saying that it will create a release branch in my repo and call the release v1? Or how will this appear after I've done it? Will I have to manually change this v1 to v0.1.1 in the actions file AND the pyproject.toml?

Yeah, you're uploading to PyPi in your pipeline, great. The custom github action still uses twine because the stdlib falls short on BASIC security. https://github.com/pypa/gh-action-pypi-publish/blob/unstable/v1/twine-upload.sh

I never bothered with pypi myself but I hope the nudge into github actions helps you. I've found the following promising github action: https://github.com/pypa/gh-action-pypi-publish

Whenever you are working on a Python project that has external dependencies installed with pip, it is strongly recommended to first create a virtual environment.

Unfortunately that feature is easy to break: https://github.com/pypa/pip/issues/9644

I think I did my part https://github.com/pypa/pip/issues/6409

$ sudo pip install y$ sudo pip install yaml WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip. Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue. To avoid this problem you can invoke Python with '-m pip' instead of running pip directly. ERROR: Could not find a version that satisfies the requirement yaml (from versions: none) ERROR: No matching distribution found for yaml

What are you implying will happen?

Using the build-in tools, you can save the exact versions of dependencies (i.e. a lock file) using "pip freeze >dependencies.txt". This should give you the exact same set of packages in two years' time.

If you want to be even more sure, you can also store hashes in the lock file. This has to be generated by a separate tools at the moment [1][2] but can be consumed by the built-in tools [3], so "pip install -r requirements.txt" is still all you need in two years' time.

[1] https://github.com/pypa/pip/issues/4732

[2] https://pip-tools.readthedocs.io/en/latest/#using-hashes

[3] https://pip.pypa.io/en/stable/topics/secure-installs/#hash-c...

Here’s the issue, https://github.com/pypa/pip/issues/11664. I think the idea would be to have some file/json description of environment that could be passed to pip to allow it to fully cross compile. They are open to supporting it just needs contributor to be found to implement it and go through review/discussion.

To be fair the only alternative is fixing Python, and even then you still would have to wait a good 5 years at least for all the old Python versions to dwindle.

It doesn't look like the fixing effort is progressing very quickly: https://github.com/pypa/pip/issues/8606

To their credit, at least they didn't close it "works as intended" which I imagine a lot of projects would.

Another good benchmark to trying to resolve apache-airflow[all]==1.10.13 using the state of PyPi on 2020-12-02, I give instructions here on how to reproduce that workflow: https://github.com/pypa/pip/issues/11836. Including a benchmark how how many extra packages your resolver should visit.