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When building something that I want to run on both CPU and GPU, depending, I’ve found it much easier to use PyTorch than some combination of NumPy and CuPy. I don’t have to fiddle around with some global replacing of numpy.* with cupy.*, and PyTorch has very nearly all the functions that those libraries have.

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Interesting. Any links to examples or docs on how to use PyTorch as a general linear algebra library for this purpose? Like a “SciPy to PyTorch” transition guide if I want to do the same?

Mentioned above:

https://data-apis.org/array-api-compat/

It's typically just importing torch and s/np/torch, not too different from NumPy -> CuPy. Try it in your own code and see!

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