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doc/languages-frameworks/cuda.section.md

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@@ -149,3 +149,104 @@ All new projects should use the CUDA redistributables available in [`cudaPackage 
| Find libraries | `buildPhase` or `patchelf` | Missing dependency on a `lib` or `static` output | Add the missing dependency | The `lib` or `static` output typically contain the libraries |



In the scenario you are unable to run the resulting binary: this is arguably the most complicated as it could be any combination of the previous reasons. This type of failure typically occurs when a library attempts to load or open a library it depends on that it does not declare in its `DT_NEEDED` section. As a first step, ensure that dependencies are patched with [`autoAddDriverRunpath`](https://search.nixos.org/packages?channel=unstable&type=packages&query=autoAddDriverRunpath). Failing that, try running the application with [`nixGL`](https://github.com/guibou/nixGL) or a similar wrapper tool. If that works, it likely means that the application is attempting to load a library that is not in the `RPATH` or `RUNPATH` of the binary.



## Running Docker or Podman containers with CUDA support {#running-docker-or-podman-containers-with-cuda-support}



It is possible to run Docker or Podman containers with CUDA support. The recommended mechanism to perform this task is to use the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/index.html).



The NVIDIA Container Toolkit can be enabled in NixOS like follows:



```nix

{

  hardware.nvidia-container-toolkit.enable = true;

}

```



This will automatically enable a service that generates a CDI specification (located at `/var/run/cdi/nvidia-container-toolkit.json`) based on the auto-detected hardware of your machine. You can check this service by running:



```ShellSession

$ systemctl status nvidia-container-toolkit-cdi-generator.service

```



::: {.note}

Depending on what settings you had already enabled in your system, you might need to restart your machine in order for the NVIDIA Container Toolkit to generate a valid CDI specification for your machine.

:::



Once that a valid CDI specification has been generated for your machine on boot time, both Podman and Docker (> 25) will use this spec if you provide them with the `--device` flag:



```ShellSession

$ podman run --rm -it --device=nvidia.com/gpu=all ubuntu:latest nvidia-smi -L

GPU 0: NVIDIA GeForce RTX 4090 (UUID: <REDACTED>)

GPU 1: NVIDIA GeForce RTX 2080 SUPER (UUID: <REDACTED>)

```



```ShellSession

$ docker run --rm -it --device=nvidia.com/gpu=all ubuntu:latest nvidia-smi -L

GPU 0: NVIDIA GeForce RTX 4090 (UUID: <REDACTED>)

GPU 1: NVIDIA GeForce RTX 2080 SUPER (UUID: <REDACTED>)

```



You can check all the identifiers that have been generated for your auto-detected hardware by checking the contents of the `/var/run/cdi/nvidia-container-toolkit.json` file:



```ShellSession

$ nix run nixpkgs#jq -- -r '.devices[].name' < /var/run/cdi/nvidia-container-toolkit.json

0

1

all

```



### Specifying what devices to expose to the container {#specifying-what-devices-to-expose-to-the-container}



You can choose what devices are exposed to your containers by using the identifier on the generated CDI specification. Like follows:



```ShellSession

$ podman run --rm -it --device=nvidia.com/gpu=0 ubuntu:latest nvidia-smi -L

GPU 0: NVIDIA GeForce RTX 4090 (UUID: <REDACTED>)

```



You can repeat the `--device` argument as many times as necessary if you have multiple GPU's and you want to pick up which ones to expose to the container:



```ShellSession

$ podman run --rm -it --device=nvidia.com/gpu=0 --device=nvidia.com/gpu=1 ubuntu:latest nvidia-smi -L

GPU 0: NVIDIA GeForce RTX 4090 (UUID: <REDACTED>)

GPU 1: NVIDIA GeForce RTX 2080 SUPER (UUID: <REDACTED>)

```



::: {.note}

By default, the NVIDIA Container Toolkit will use the GPU index to identify specific devices. You can change the way to identify what devices to expose by using the `hardware.nvidia-container-toolkit.device-name-strategy` NixOS attribute.

:::



### Using docker-compose {#using-docker-compose}



It's possible to expose GPU's to a `docker-compose` environment as well. With a `docker-compose.yaml` file like follows:



```yaml

services:

  some-service:

    image: ubuntu:latest

    command: sleep infinity

    deploy:

      resources:

        reservations:

          devices:

          - driver: cdi

            device_ids:

            - nvidia.com/gpu=all

```



In the same manner, you can pick specific devices that will be exposed to the container:



```yaml

services:

  some-service:

    image: ubuntu:latest

    command: sleep infinity

    deploy:

      resources:

        reservations:

          devices:

          - driver: cdi

            device_ids:

            - nvidia.com/gpu=0

            - nvidia.com/gpu=1

```

nixos/modules/installer/tools/nix-fallback-paths.nix

+6 −6
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{

  x86_64-linux = "/nix/store/vi6fh1mhzl5m0knn3y056wnl07sri6c5-nix-2.24.8";

  i686-linux = "/nix/store/s4wdfq4dzii2jhy1mv2h7b5hpzhf40hm-nix-2.24.8";

  aarch64-linux = "/nix/store/g50zn4kdcnlgkwbvyi9f9icj9i2x83i5-nix-2.24.8";

  riscv64-linux = "/nix/store/8ws83k3wc9a639hp6dyprsmvb24fd14w-nix-riscv64-unknown-linux-gnu-2.24.8";

  x86_64-darwin = "/nix/store/1dhc9a68j5lcnkgdrcm2kbydnbzrlldg-nix-2.24.8";

  aarch64-darwin = "/nix/store/7gv39q83hm8b7cwcpx1vlcs424qmp67p-nix-2.24.8";

  x86_64-linux = "/nix/store/2nhrwv91g6ycpyxvhmvc0xs8p92wp4bk-nix-2.24.9";

  i686-linux = "/nix/store/idaxj9ji6ggpn1h47a35mf0c8ns4ma39-nix-2.24.9";

  aarch64-linux = "/nix/store/7b5q44l2p70bf6m6dprr8f0587ypwq1z-nix-2.24.9";

  riscv64-linux = "/nix/store/mgw3il1qk59750g5hbf02km79rgyx00y-nix-riscv64-unknown-linux-gnu-2.24.9";

  x86_64-darwin = "/nix/store/rp8rc0pfgham7d7spj5s9syzb138dmmd-nix-2.24.9";

  aarch64-darwin = "/nix/store/1n95r340s7p3vdwqh7m94q0a42crahqq-nix-2.24.9";

}

nixos/modules/programs/gpu-screen-recorder.nix

+9 −9
Original line number Diff line number Diff line 
{ config, lib, pkgs, ... }:

{

  config,

  lib,

  pkgs,

  ...

}:



let

  cfg = config.programs.gpu-screen-recorder;

  package = cfg.package.override {

    inherit (config.security) wrapperDir;

  };

in {

in

{

  options = {

    programs.gpu-screen-recorder = {

      package = lib.mkPackageOption pkgs "gpu-screen-recorder" { };
@@ -28,12 +34,6 @@ in { 
      capabilities = "cap_sys_admin+ep";

      source = "${package}/bin/gsr-kms-server";

    };

    security.wrappers."gpu-screen-recorder" = {

      owner = "root";

      group = "root";

      capabilities = "cap_sys_nice+ep";

      source = "${package}/bin/gpu-screen-recorder";

    };

  };



  meta.maintainers = with lib.maintainers; [ timschumi ];

nixos/modules/services/hardware/nvidia-container-toolkit/default.nix

+57 −44
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@@ -77,15 +77,24 @@ 


  };



  config = {

  config = lib.mkIf config.hardware.nvidia-container-toolkit.enable {

    virtualisation.docker = {

      daemon.settings = lib.mkIf

        (lib.versionAtLeast config.virtualisation.docker.package.version "25") {

          features.cdi = true;

        };



    virtualisation.docker.daemon.settings = lib.mkIf

      (config.hardware.nvidia-container-toolkit.enable &&

      rootless.daemon.settings = lib.mkIf

        (config.virtualisation.docker.rootless.enable &&

         (lib.versionAtLeast config.virtualisation.docker.package.version "25")) {

           features.cdi = true;

         };

    };



    hardware.nvidia-container-toolkit.mounts = let

    hardware = {

      graphics.enable = lib.mkIf (!config.hardware.nvidia.datacenter.enable) true;



      nvidia-container-toolkit.mounts = let

        nvidia-driver = config.hardware.nvidia.package;

      in (lib.mkMerge [

        [{ hostPath = pkgs.addDriverRunpath.driverLink;
@@ -119,8 +128,12 @@ 
           { hostPath = "${lib.getLib nvidia-driver}/lib";

             containerPath = "/usr/local/nvidia/lib64"; }])

      ]);

    };



    services.xserver.videoDrivers = lib.mkIf

      (!config.hardware.nvidia.datacenter.enable) [ "nvidia" ];



    systemd.services.nvidia-container-toolkit-cdi-generator = lib.mkIf config.hardware.nvidia-container-toolkit.enable {

    systemd.services.nvidia-container-toolkit-cdi-generator = {

      description = "Container Device Interface (CDI) for Nvidia generator";

      wantedBy = [ "multi-user.target" ];

      after = [ "systemd-udev-settle.service" ];

nixos/tests/installed-tests/xdg-desktop-portal.nix

+16 −3
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@@ -3,7 +3,20 @@ 
makeInstalledTest {

  tested = pkgs.xdg-desktop-portal;



  # Ton of breakage.

  # https://github.com/flatpak/xdg-desktop-portal/pull/428

  meta.broken = true;

  # Red herring

  # Failed to load RealtimeKit property: GDBus.Error:org.freedesktop.DBus.Error.ServiceUnknown: The name org.freedesktop.RealtimeKit1 was not provided by any .service files

  # Maybe a red herring, enabling PipeWire doesn't fix the location test.

  # Failed connect to PipeWire: Couldn't connect to PipeWire

  testConfig = {

    environment.variables = {

      TEST_IN_CI = 1;

      XDG_DATA_DIRS = "${pkgs.xdg-desktop-portal.installedTests}/share/installed-tests/xdg-desktop-portal/share";

    };

    # Broken, see comment in the package file.

    #services.geoclue2 = {

    #  enable = true;

    #  enableDemoAgent = true;

    #};

    #location.provider = "geoclue2";

  };

}