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Does this mean Nvidia on Desktop Linux won't be a huge pain anymore? I'm on AMD because ultimately the Linux support just works reliably whatever I install with

by XorNot 4y ago

Does this mean Nvidia on Desktop Linux won't be a huge pain anymore? I'm on AMD because ultimately the Linux support just works reliably whatever I install with the open source drivers (different from great, but hey, I can run Stable Diffusion in podman so not terribly either). But the ML space is going to Nvidia, so I suspect my next card will be an NV - but not at the cost of my "work" space becoming unreliable.

wmf 4y ago
Yeah, give it four or five years and I expect Nvidia to be as well-supported as AMD and Intel.
- uluyol 4y ago
  It took much longer than that for the AMD driver's to get to where they are today. Newer drivers get to piggyback off the work done for others (yay open source) but I think it will likely take longer than 5 years before NVK and friends are competitive with the proprietary driver. I would love to be proven wrong though.
  - capableweb 4y ago
    NVIDIA probably has north of 70% of the discrete graph card market share, so a lot more developers and users use NVIDIA cards than AMD. Probably will be much faster to reach the same state as AMD.
lbotos 4y ago
I'm curious, how are you running SD on podman? I'm looking to build an ML rig, preferably linux, and AMD cards looked... like a hassle compared to Nvidia in my cloud GPU instance testing.
- XorNot 4y ago
  The trick is to use the PyTorch ROCm docker image (rocm/rocm-terminal) and then run with podman run -it --rm --device=/dev/kfd --device=/dev/dri --ipc=host --group-add keep-groups --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -p 127.0.0.1:7860:7860 $(cat .sd-image.iid) It has other wrinkles - i.e. your memory use is functionally doubled because you can't use float16's for whatever reason (seems to NaN bomb but I've not found an answer). So it does work, but yeah, it's a hassle compared to Nvidia where you can just run things...although, I did not need to install anything special into my host environment which I believe isn't quite as true with NV. Dockerfile I've been using lately is https://pastebin.com/BSYN8062 https://pastebin.com/BSYN8062 but I think I need to update it to incorporate the AUTOMATIC111 repo at some point.

by XorNot 4y ago

wmf 4y ago

Yeah, give it four or five years and I expect Nvidia to be as well-supported as AMD and Intel.

uluyol 4y ago
It took much longer than that for the AMD driver's to get to where they are today. Newer drivers get to piggyback off the work done for others (yay open source) but I think it will likely take longer than 5 years before NVK and friends are competitive with the proprietary driver. I would love to be proven wrong though.
- capableweb 4y ago
  NVIDIA probably has north of 70% of the discrete graph card market share, so a lot more developers and users use NVIDIA cards than AMD. Probably will be much faster to reach the same state as AMD.

lbotos 4y ago

I'm curious, how are you running SD on podman? I'm looking to build an ML rig, preferably linux, and AMD cards looked... like a hassle compared to Nvidia in my cloud GPU instance testing.

XorNot 4y ago
The trick is to use the PyTorch ROCm docker image (rocm/rocm-terminal) and then run with podman run -it --rm --device=/dev/kfd --device=/dev/dri --ipc=host --group-add keep-groups --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -p 127.0.0.1:7860:7860 $(cat .sd-image.iid) It has other wrinkles - i.e. your memory use is functionally doubled because you can't use float16's for whatever reason (seems to NaN bomb but I've not found an answer). So it does work, but yeah, it's a hassle compared to Nvidia where you can just run things...although, I did not need to install anything special into my host environment which I believe isn't quite as true with NV. Dockerfile I've been using lately is https://pastebin.com/BSYN8062 https://pastebin.com/BSYN8062 but I think I need to update it to incorporate the AUTOMATIC111 repo at some point.