forked from DGNum/lab-infra
80 lines
1.8 KiB
Nix
80 lines
1.8 KiB
Nix
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{
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config,
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lib,
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pkgs,
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meta,
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name,
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...
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}:
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lib.extra.mkConfig {
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enabledModules = [
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# INFO: This list needs to stay sorted alphabetically
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];
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enabledServices = [
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# INFO: This list needs to stay sorted alphabetically
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# Machine learning API machine
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"microvm-ml01"
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"microvm-router01"
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"nvidia-tesla-k80"
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"proxmox"
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];
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extraConfig = {
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microvm = {
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host.enable = true;
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};
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dgn-hardware = {
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useZfs = true;
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zfsPools = [
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"dpool"
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"ppool0"
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];
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};
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services.netbird.enable = true;
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# We are going to use CUDA here.
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nixpkgs.config.cudaSupport = true;
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hardware.graphics.enable = true;
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environment.systemPackages = [
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((pkgs.openai-whisper-cpp.override { cudaPackages = pkgs.cudaPackages_11; }).overrideAttrs (old: {
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src = pkgs.fetchFromGitHub {
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owner = "ggerganov";
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repo = "whisper.cpp";
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rev = "v1.7.1";
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hash = "sha256-EDFUVjud79ZRCzGbOh9L9NcXfN3ikvsqkVSOME9F9oo=";
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};
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env = {
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WHISPER_CUBLAS = "";
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GGML_CUDA = "1";
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};
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# We only need Compute Capability 3.7.
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CUDA_ARCH_FLAGS = [ "sm_37" ];
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# We are GPU-only anyway.
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patches = (old.patches or [ ]) ++ [
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./no-weird-microarch.patch
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./all-nvcc-arch.patch
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];
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}))
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];
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services = {
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ollama = {
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enable = true;
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host = meta.network.${name}.netbirdIp;
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package = pkgs.callPackage ./ollama.nix {
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cudaPackages = pkgs.cudaPackages_11;
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# We need to thread our nvidia x11 driver for CUDA.
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extraLibraries = [ config.hardware.nvidia.package ];
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};
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};
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};
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networking.firewall.interfaces.wt0.allowedTCPPorts = [ config.services.ollama.port ];
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};
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root = ./.;
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}
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