I have a (rather different!) application that’s released as a Flatpak, and GPU acceleration is CUDA-only there, too. It supports ROCm when compiled locally, but ROCm just can’t work through the sandbox at this point, unfortunately. Not for lack of trying.
If you have an example of a Flatpak where it does work, I’d love to see their manifest so I can learn from it.
I did buy a (secondhand) nvidia card specifically for AI worlkloads because yes, I realised that this is what the AI dev community has settled on, and if I try to avoid nvidia I will be making life very hard for myself.
But that doesn’t change the fact that it still absolutely sucks that nvidia have this dominance in the space, and that it is largely due to what tooling the community has decided to use, rather than any unique hardware capability which nvidia have.
Seems to only support cpu or cuda, not AMD sadly.
https://github.com/ladaapp/lada
For the prepackaged flathub or docker installations.
Great news, you can run this software with an AMD GPU fine, just follow the installation instructions in your link and install pytorch for ROCm. https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html
I have a (rather different!) application that’s released as a Flatpak, and GPU acceleration is CUDA-only there, too. It supports ROCm when compiled locally, but ROCm just can’t work through the sandbox at this point, unfortunately. Not for lack of trying.
If you have an example of a Flatpak where it does work, I’d love to see their manifest so I can learn from it.
@PlantPowerPhysicist @HappyFrog f(l)atpak is workaround, not solution. It should not work in non-default configuration
You do realize people don’t care if it uses extra 200mb of space, if they don’t have to build it from GitHub or break their repos.
That’s pretty much the standard for a lot of video processing applications
I did buy a (secondhand) nvidia card specifically for AI worlkloads because yes, I realised that this is what the AI dev community has settled on, and if I try to avoid nvidia I will be making life very hard for myself.
But that doesn’t change the fact that it still absolutely sucks that nvidia have this dominance in the space, and that it is largely due to what tooling the community has decided to use, rather than any unique hardware capability which nvidia have.
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You also could pickup a CPU with a ton of memory bandwidth.