$ 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 {#cuda-using-docker-compose}

It's possible to expose GPUs 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
```

## Contributing {#cuda-contributing}

::: {.warning}
This section of the docs is still very much in progress. Feedback is welcome in GitHub Issues tagging @NixOS/cuda-maintainers or on [Matrix](https://matrix.to/#/#cuda:nixos.org).
:::

### Package set maintenance {#cuda-package-set-maintenance}

The CUDA Toolkit is a suite of CUDA libraries and software meant to provide a development environment for CUDA-accelerated applications. Until the release of CUDA 11.4, NVIDIA had only made the CUDA Toolkit available as a multi-gigabyte runfile installer, which we provide through the [`cudaPackages.cudatoolkit`](https://search.nixos.org/packages?channel=unstable&type=packages&query=cudaPackages.cudatoolkit) attribute. From CUDA 11.4 and onwards, NVIDIA has also provided CUDA redistributables (“CUDA-redist”): individually packaged CUDA Toolkit components meant to facilitate redistribution and inclusion in downstream projects. These packages are available in the [`cudaPackages`](https://search.nixos.org/packages?channel=unstable&type=packages&query=cudaPackages) package set.

All new projects should use the CUDA redistributables available in [`cudaPackages`](https://search.nixos.org/packages?channel=unstable&type=packages&query=cudaPackages) in place of [`cudaPackages.cudatoolkit`](https://search.nixos.org/packages?channel=unstable&type=packages&query=cudaPackages.cudatoolkit), as they are much easier to maintain and update.

#### Updating redistributables {#cuda-updating-redistributables}

1. Go to NVIDIA's index of CUDA redistributables: <https://developer.download.nvidia.com/compute/cuda/redist/>
2. Make a note of the new version of CUDA available.
3. Run

   ```bash
   nix run github:connorbaker/cuda-redist-find-features -- \
      download-manifests \
      --log-level DEBUG \
      --version <newest CUDA version> \
      https://developer.download.nvidia.com/compute/cuda/redist \
      ./pkgs/development/cuda-modules/cuda/manifests
   ```

   This will download a copy of the manifest for the new version of CUDA.
4. Run

   ```bash
   nix run github:connorbaker/cuda-redist-find-features -- \
      process-manifests \
      --log-level DEBUG \
      --version <newest CUDA version> \
      https://developer.download.nvidia.com/compute/cuda/redist \
      ./pkgs/development/cuda-modules/cuda/manifests
   ```

   This will generate a `redistrib_features_<newest CUDA version>.json` file in the same directory as the manifest.