llvm-mingw VS Poetry

> Doing Python with a C plugin, or just compiling a command line C/C++ isn't really systems programming.

I care about a minimal set of tools in order to compile C/C++ programs. thats offered by:

https://github.com/mstorsjo/llvm-mingw/releases

and also MSYS2, and even the Zig C compiler. all less than 200 MB. meanwhile Visual Studio installing about 10 GB worth. If Microsoft can offer a similar experience then I am interested.

Pick a community-supported Clang-based Mingw-w64 distribution.

> AFAIK there is no native GCC compiler for Windows

might want to check your facts before spouting nonsense. there is, and has been for many, many years. more than one in fact:

https://github.com/mstorsjo/llvm-mingw

https://packages.msys2.org/base/mingw-w64-gcc

MinGW is notoriously slow to link compared to MSVC, unless using llvm-mingw with the link=lld SCons option. If using MSVC, make sure to use 2022 or at least 2019 if possible – recent linkers tend to be faster than older versions.

GCC doesn't support windows/arm64, but you should be able to do it with LLVM. I've never gotten it to work myself, but should be able to supply a cross toolchain: https://github.com/mstorsjo/llvm-mingw

Visual Studio is a bloated mess, and has been for many years. Its at least 10 times larger than other options, such as MinGW-LLVM:

https://github.com/mstorsjo/llvm-mingw

Sadly Qt ships MinGW 8.1 which is positively ancient (released in 2018). If you're starting a new project (which you likely are if you are installing an IDE aha) there's no reason not to go for more recent compilers - msys2 has GCC12 (https://packages.msys2.org/package/mingw-w64-x86_64-gcc) and Clang 14 (https://packages.msys2.org/package/mingw-w64-x86_64-clang) which just work better overall, have much more complete C++20 support, have less bugs, better compile times (especially clang with the various PCH options that appeared in the last few versions), better static analysis, etc.

Personally I use https://github.com/mstorsjo/llvm-mingw's releases directly which does not require MSYS but that's because I recompile all my libraries with specific options - if the MSYS libs as they are built are good for you there's no reason not to use them.

you can grab it here: https://github.com/mstorsjo/llvm-mingw/releases/tag/20211002

Clang (and lld) do support native TLS, and mingw-w64 does have the things that are needed. I think binutils also might have what's needed too, but AFAIK the thing that's missing is support for it in GCC.

Actually, (upstream) Clang defaults to native TLS instead of emulated TLS. In MSYS2, Clang is overridden to use emulated TLS by deafult to interoperate better with GCC built code and libstdc++ though.

The toolchain I maintain, https://github.com/mstorsjo/llvm-mingw, defaults to native TLS throughout.

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