Open thomasjpfan opened 3 months ago
I can speak on this topic too as I'm presenting on it at EuroSciPy this Thursday.
You have compiled code mixed with NumPy-like code? Use the Array API for the NumPy code and dispatch for the compiled code Use dlpack to do zero copy transfer Dispatching is still being developed: NetworkX has a dispatching mechanism
Some notes on this from discussion at EuroSciPy: https://hackmd.io/@seberg/spatch
Thanks for proposing the topic, @thomasjpfan!
This is scheduled in Main Room 1. Starts @ 14:40.
Check the schedule here: https://www.nfsummit24.com/schedule.
Scikit-learn and SciPy have been incrementally adding support for the Array API standard. The Array API standard enables libraries to write code in a standard and operate with different array libraries such as NumPy, PyTorch, CuPy, or JAX.
For this unconference session, we will discuss the process of adopting the Array API standard and whether the Array API standard adoption makes sense for your library.
When adopting Array API standard, here are some of the common issues that came up:
x[[0, 3, 4], :] = 3