docs: update the CUDA section with how to use the `nvidia-container-toolkit` (#344188)

| 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

```