self-contained-runnable-python-package-template VS Nuitka

The approach I prefer is to not mess with setuptools etc at all in the first place, and simply make a nice executable package.

e.g. https://github.com/tpapastylianou/self-contained-runnable-py...

The title should be: How to create a "Python DISTRIBUTION package".

The term "python package" means something entirely different (or at the very least is ambiguous in a pypi/distribution context).

To add to the confusion, creating a totally normal, runnable python package in a manner that makes it completely self-contained such that it can be "distributed" in a standalone manner, while still being a totally normal boring python package, is also totally possible (if not preferred, in my view).

Shameless plug: https://github.com/tpapastylianou/self-contained-runnable-py...

Shameless plug: I use my own template, which organises things as runnable projects.

https://github.com/tpapastylianou/self-contained-runnable-py...

It serves my purposes very well (which is creating projects that represent standalone experiments).

Sharing in case someone else here finds it useful.

More recently I've modified this a bit to also generate nice html reports straight from the __main__.py file, independently of the underlying python code, and use this as lab books (where each lab book contains a single analysis and its report). I'll upload this template separately when I find the time.

This is a good place to mention https://nuitka.net/ which aims to compile python programs into standalone binaries.

For Python, you could make a proper deployment binary using Nuitka (in standalone mode – avoid onefile mode for this). I'm not pretending it's as easy as building a Go executable: you may have to do some manual hacking for more unusual unusual packages, and I don't think you can cross compile. I think a key element you're getting at is that Go executables have very few dependencies on OS packages, but with Python (once you've sorted the actual Python dependencies) you only need the packages used for manylinux [2], which is not too onerous.

[1] https://nuitka.net/

[2] https://peps.python.org/pep-0599/#the-manylinux2014-policy

glee is rich in blogging features but has some drawbacks. One of the main drawbacks is its compatibility with multiple operating systems and system architectures. We lost one potential customer due to glee incompatibility in macOS. Another major issue is the deployment time. We built the first version of glee entirely in Python and used nuitka, nuitka compiles Python programs into a single executable binary file. We need to create three separate stages for creating executable binaries for Windows, Mac, and Linux in deployment, and it takes around 20 minutes to complete.

There is already an AOT compiler for Python: Nuitka[0]. But I don't think it's much faster.

And then there is mypyc[1] which uses mypy's static type annotations but is only slightly faster.

And various other compilers like Numba and Cython that work with specialized dialects of Python to achieve better results, but then it's not quite Python anymore.

[0] https://nuitka.net/

[1] https://github.com/python/mypy/tree/master/mypyc

Nuitka deals pretty well with those in general: https://nuitka.net/

Hi HN,

Has anyone explored Nuitka [1] and developed understanding from a blank slate?

Is there any toy version of this, so that one can start playing with the language translation concepts?

Is there any underlying theory/inspiration upon which this project is built?

Are there any similar projects, in say other languages?

[1] https://github.com/Nuitka/Nuitka

That's more of cultural problem in the Python community.

If I provide an end user software to my client written an Python (so not a backend, not a lib...), I will compile it with nuitka (https://github.com/Nuitka/Nuitka) and hide the stack trace (https://www.bitecode.dev/p/why-and-how-to-hide-the-python-st...) to provide a stand alone executable.

This means the users don't have to know it's made with Python or install anything, and it just works.

However, Python is not like Go or Rust, and providing such an installer requires more than work, so a huge part of the user base (which have a lot of non professional coders) don't have the skill, time or resources to do it.

And few people make the promotion of it.

I should write an article on that because really, nobody wants to setup python just to use a tool.

As for a simpler option, you could use a "compiler": https://github.com/Nuitka/Nuitka

> Your comment would make sense if Python code could be compiled into x86 or ARM assembly in the first place.

It can actually be compiled (or transpiled) into C code [1] with few limitations, so I can't see why not.

[1] https://github.com/Nuitka/Nuitka