Scientific Python
The scientific Python world has its own packaging history — conda, pixi, CUDA wheels, and large binary dependencies. The handbook explains where uv fits, when conda or pixi are still the right call, and how to install ML packages reliably.
Get started with data science tooling
GPU and ML workflows
Migrate to modern tools
More Scientific Python pages
Everything else tagged scientific-python, grouped by section. Pages featured above are not repeated here.
How To
Explanation
Reference
From the blog
Posts tagged scientific-python.
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Your Python Wheels Still Target 2009 CPUs
The wheel format cannot describe a CPU's instruction set, so default wheels compile for the lowest common denominator. Wheel variants would end that.
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In 2012, Guido Had No Idea NumPy Had Its Own Packaging System
A 2012 panel discussion between Guido van Rossum and the scientific Python community reveals how deep the disconnect on packaging ran.
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One Line Command to Launch a Notebook with Pytorch
Launch a Jupyter notebook with PyTorch using a single uv run command that handles Python, dependencies, and isolation.
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Scientific Python Library Development Guide
The Scientific Python Library Development Guide offers topical guides on packaging, GitHub Actions, and more for research software.