infrastructure/machines/krz01/_configuration.nix

71 lines
1.6 KiB
Nix

{
config,
lib,
pkgs,
...
}:
lib.extra.mkConfig {
enabledModules = [
# INFO: This list needs to stay sorted alphabetically
];
enabledServices = [
# INFO: This list needs to stay sorted alphabetically
"microvm-router01"
"nvidia-tesla-k80"
"proxmox"
# Machine learning API machine
"microvm-ml01"
];
extraConfig = {
microvm = {
host.enable = true;
};
dgn-hardware = {
useZfs = true;
zfsPools = [
"dpool"
"ppool0"
];
};
services.netbird.enable = true;
# We are going to use CUDA here.
nixpkgs.config.cudaSupport = true;
hardware.graphics.enable = true;
environment.systemPackages = [
((pkgs.openai-whisper-cpp.override { cudaPackages = pkgs.cudaPackages_11; }).overrideAttrs (old: {
src = pkgs.fetchFromGitHub {
owner = "ggerganov";
repo = "whisper.cpp";
rev = "v1.7.1";
hash = "sha256-EDFUVjud79ZRCzGbOh9L9NcXfN3ikvsqkVSOME9F9oo=";
};
env = {
WHISPER_CUBLAS = "";
GGML_CUDA = "1";
};
# We only need Compute Capability 3.7.
CUDA_ARCH_FLAGS = [ "sm_37" ];
# We are GPU-only anyway.
patches = (old.patches or [ ]) ++ [
./no-weird-microarch.patch
./all-nvcc-arch.patch
];
}))
];
services.ollama = {
enable = true;
package = pkgs.callPackage ./ollama.nix {
cudaPackages = pkgs.cudaPackages_11;
# We need to thread our nvidia x11 driver for CUDA.
extraLibraries = [ config.hardware.nvidia.package ];
};
};
};
root = ./.;
}