Hacker News Reader

Comment by gjvc | Hacker News Reader

i am a big fan of .venv/ -- except when it takes ~45 mins to compile the native extension code in question -- then I want it all pre-packaged.

View on HN

At this stage [0], uncompiled native extensions are not yet a bug, but a definite oversight of the maintainer. They should come as precompiled wheels

[0]: https://pythonwheels.com

Honestly I don't think I've ever used a precompiled package in Python. Every single C stuff seems to take ages and requires all that fun stuff of installing native system dependencies.

Edit: skimming through this page, precompiling seems like an afterthought, and the linked packages don't even seem to mention how to integrate third-party libraries. So I guess I can see why it doesn't deliver on its promises.

Probably a function of the specific set of packages you use, or the pip options you specify. Pretty much all the major C packages come as wheels these days.

They all come as wheels, they just aren't precompiled.

I honestly can't remember the last time I had to compile anything, and I am on Windows.

Can you link one that comes as a wheel but is really a source distribution?

You can try pip install pillow for a good example of how it works. I suspect there's a strong survivorship bias here, as you'd only notice the packages that don't ship with wheels.

Yeah, perhaps. One I remember from last year is the cryptography and numpy package, for instance. Now they do seem to ship with binary wheels, at least for my current Python and Linux version.

Kerberos and Hadoop stuff obviously still doesn't, though. I guess the joke's on me for being stuck in this stack...

In order for a wheel to be used instead of a source distribution there needs to be one that matches your environment. For numpy you can take a look at the wheels for their latest release[1]. The filename of a wheel specifies where it can be used. Let's take an example:

numpy-1.25.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

This specifies cpython 3.9, linux, glibc 2.17 or higher, and x86_64 cpu. Looking through the list you will see that the oldest cpython supported is 3.9. So if you are running with an older version of python you will have to build from source.

I just learned a bit more about this recently because I could not figure out why PyQt6 would not install on my computer. It turned out my glibc was too old. Finally upgraded from Ubuntu 18.04.

[1] https://pypi.org/project/numpy/1.25.2/#files

Try `--only-binary :all:` to force pip to ignore sdist packages, might help avoid those slow compilations.

It's a good idea to be caching sdists and wheels — for resilience against PyPI downtime, for left-pad scenarios, and even just good netiquette — and for packages that don't have a wheel for your environment, you can fairly easily build that wheel yourself and stick it into the cache.

Reply on news.ycombinator.com