Python Developer Tooling Handbook – Scientific-Python

Your Python Wheels Still Target 2009 CPUs

Tim Hopper — Wed, 15 Apr 2026 00:00:00 +0000

Intel shipped AVX2 in 2013. AMD reached it with Excavator-era parts around 2015. Eleven years later, the default NumPy wheel pip downloads on an x86_64 Linux box is still compiled to run on processors from roughly 2003, the year AMD launched the Opteron and defined the AMD64 baseline. Every SIMD instruction that has shipped since is off-limits to the compiler that produced that wheel.

The reason is structural. A wheel filename encodes three pieces of compatibility metadata: Python version, ABI, and platform. “Platform” for mainstream Linux wheels is typically manylinux_2_17_x86_64, which says “works on any glibc 2.17+ x86-64 distro” and nothing about which CPU instruction sets the machine supports. When a project publishes one binary for all of those users, it has to target the lowest common denominator.

In 2012, Guido Had No Idea NumPy Had Its Own Packaging System

Tim Hopper — Tue, 07 Apr 2026 00:00:00 +0000

In a recently resurfaced 2012 PyData panel discussion, Fernando Perez, Travis Oliphant, and David Cournapeau sat down with Guido van Rossum to discuss the scientific Python community’s needs. The conversation wandered through operator overloading (which eventually produced the @ matrix multiplication operator in Python 3.5) and import performance on supercomputers. The packaging segment is the one worth revisiting.

David Cournapeau, who maintained NumPy’s build infrastructure, explained that NumPy had written its own packaging layer on top of distutils that was “as much code as distutils itself.” NumPy had to override nearly every piece of distutils to handle Fortran compilers, C++ extensions, Cython, cross-platform compiler flags, and builds on exotic hardware like IBM Blue Gene supercomputers.

One Line Command to Launch a Notebook with Pytorch

Tim Hopper — Wed, 15 Jan 2025 14:18:00 +0000

uv is changing how accessible Python is for new users.

If you want to try out pytorch in a Jupyter Notebook, you can install uv and then run this single line command:

uv run --python 3.12 --with "torch~=2.4" --with jupyter --with "numpy<2" jupyter lab

uv will grab all your dependencies (including Python 3.12, if you don’t have it installed), install them in an isolated environment, and launch Jupyter Lab.

Scientific Python Library Development Guide

Tim Hopper — Mon, 12 Feb 2024 00:00:00 +0000

Along with the Python Packaging User Guide, another excellent resource for learning about Python development practice is the Scientific Python Library Development Guide, a “guide is maintained by the scientific Python community for the benefit of fellow scientists and research software engineers”.

I especially like their topical guides on topics like packaging and getting started with Github Actions.