3 ms·

You are telling me what is supposedly a multi trillion dollar business being pursued by the brightest minds at Google and others would rather join the queue for

You are telling me what is supposedly a multi trillion dollar business being pursued by the brightest minds at Google and others would rather join the queue for a hardware drop from NVIDIA than enjoy a substantial competitive advantage being able to run on AMD hardware? For something that supposedly breaks down into such simple steps everyone is also building hardware to do it? Something doesn’t quite add up for this dependence on CUDA. Sure, I’m installing some dependency hellscape Python research project from GitHub I reasonably expect idiosyncratic hardware dependencies, but surely the companies in this space are beyond that on the maturity timeline.

weebull 3y ago
> Something doesn’t quite add up for this dependence on CUDA. Sure, I’m installing some dependency hellscape Python research project from GitHub I reasonably expect idiosyncratic hardware dependencies, but surely the companies in this space are beyond that on the maturity timeline. I think part of the problem is something you're touching on here. The ML software space is horrifically put together. It's a bunch of research projects cobbled together. Hence the dependency hell, and very few people who actually know how to port it. Cuda siits at the bottom of the stack with its design assumptions baked into everything above it (see PyTorch for a good example of that). Nobody wrote a hardware agnostic layer over the top, and now the thought of pulling out a Jenga block at the bottom scares everyone. Cuda is an intentionally leaky abstraction layer over the hardware, and its worked.