it is confusing to see scientists use vendor specific tools over neural ones. this promotion of one corporation over the rest. IMHO this is against the spirit of academia. I will personally not use Pytorch or any tool based on CUDA.
That said, many projects are compute bound and the quality may be limited by speed of hardware, thus vendor dependent.
because PyTorch is still quite pleasant to use in CPU mode. it should also have support for AMD chips quite soon (and AMD's CUDA-equivalent, ROCm, at least appears to be open source).
For the case of science, depending on closed products is obviously a malpractice.
Closed products are the norm, in science and otherwise. 'Obviously malpractice' is a silly assertion to make, as sometimes there is no other choice.
Put another way: if your goal is to not ever harm your child, how can you responsibly feed them applesauce or let doctors administer them medicine when you can't be certain what went into the making of the applesauce or of the medicine?
With a very rigorous standard, you can't. With the most rigorous standard, you can't even if the chain of trust has a single link, and you'd only use food or medicine that you, yourself, produced. But we know that few, if any people, use such rigorous standards, and that, if they did, they'd be much worse off. It's not a perfect analogy for software or hardware, but it's certainly a salient one. With your standards, it's malpractice all the way down[0].