Looks like this is just tweaking some defaults and commenting out some code that enables cuda. It also switches to something called gloo, which I'm not familiar with. Seems like an alternate backend.
Lol, all my best work has been when I don’t know what I’m doing and it’s refreshing to see someone moving the ball forward and feeling the same way. Kudos
Usually you just trivially have the model run on cpu or gpu by simply writing .cpu() at specific places, so he's wondering why this isn't the case here.
that's literally all I did (plus switching the tensor type). I'd imagine people are posting and upvoting this not because it's actually interesting code but rather just because it runs unexpectedly fast on consumer CPUs and it's not something they considered feasible before.
That is vastly underestimating how tricky it is to make novel pieces of software run. There is a huge fringe of people who know how to click things but not use the terminal and a large fringe of people who know how to run "./execute.bat" but not how to write syntactically correct Python.
probably pytorch is very optimized to x86. It's likely using lots of SIMD and whatnot. I'm sure it's possible to get similar performance on m1 macs, but not with the current version of pytorch.
Looks like this is just tweaking some defaults and commenting out some code that enables cuda. It also switches to something called gloo, which I'm not familiar with. Seems like an alternate backend.