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Linux is a family of open source Unix-like operating systems based on the Linux kernel, an operating system kernel first released on September 17, 1991 by Linus Torvalds. Linux is typically packaged in a Linux distribution (or distro for short).

Distributions include the Linux kernel and supporting system software and libraries, many of which are provided by the GNU Project. Many Linux distributions use the word "Linux" in their name, but the Free Software Foundation uses the name GNU/Linux to emphasize the importance of GNU software, causing some controversy.

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Using an AMD GPU for NN training/inference? (self.linux)

submitted 1 year ago* (last edited 1 year ago) by leakybits to c/[email protected]

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I'm looking to buy a new GPU. My main use case will be training and running neural nets (tensorflow+pytorch); gaming isn't really a priority.

Thing is, I use wayland (via sway), and so I'd really prefer to get an AMD GPU. Nvidia doesn't seem very linux friendly at the moment, especially when it comes to wayland unfortunately.

On the other hand, Nvidia seems to be the clear frontrunner right now when it comes to NN acceleration. I'm worried that if I got an AMD GPU to accelerate my NN work, I'd just be wasting my money.

What do you all think?

Edit: I've used GPUs to accelerate NN models in the past, but they weren't my own, they were provided by my uni's research infra and/or google collab. So this would be the first time I'd be using my own GPU hardware for this purpose.

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[–] [email protected] 1 points 1 year ago* (last edited 1 year ago)

Edit: Wrote this on mobile. The mobile U/I is not always clear as to the source magazine where the post came from, so I missed the Linux in there. Things are not as dire on Linux as on Windows for AMD, so my assessment may be a bit pessimistic. With AMD's focus on the data centre for machine learning, the linux driver stack seems fairly well supported.

I spent the last few days getting stable defusion and pytorch working on my Radeon 6800 XT in windows. The machineml distribution of stable diffusion runs at about 1/4 of the speed of raw rocm when I compare it to the shark tooling, which supports rocm via docker on windows.

Expect tooling to be clinky and that you will need to compile everything yourself on linux. Prebuilt stuff will all be for Nvidia.

Amd is pushing hard into the ai space, but aiming at datacenter users. They are rumoured to be building rocm for their windows drivers, but when that will ship is anyone's guess.

So right now, if you need to hit the ground running for your academic work, I would recommend NVidia, as much as it pains me, a long time AMD user.