You can use PyTorch for more than ML. No need to use backprop. Thinks of it as GPU accelerated NumPy.

I would like to see OctaneRender done in Pytorch. /s

Sure, but if the OctaneRender folk wanted to support AMD, then I highly doubt they'd be interested in a CUDA compatability layer either - they'd want to be using the lowest level API possible (Vulkan?) to get close to the metal and optimize performance.

See, that is where you got all wrong, they dropped Vulkan for CUDA, and even made a talk about it at GTC.

https://www.cgchannel.com/2023/11/otoy-releases-first-public...

https://www.cgchannel.com/2023/11/otoy-unveils-the-octane-20...

And then again, there are plenty of other cases where Pytorch makes absolute no sense in GPU, which was the whole starting point.

> See, that is where you got all wrong

I said that if they wanted to support AMD they would use the closest-to-metal API possible, and your links prove that this is exactly their mindset - preferring a lower level more performant API to a higher level more portable one.

For many people the tradeoffs are different and ability to write code quickly and iterate on design makes more sense.