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1# CUDA {#cuda}
2
3CUDA-only packages are stored in the `cudaPackages` packages set. This set
4includes the `cudatoolkit`, portions of the toolkit in separate derivations,
5`cudnn`, `cutensor` and `nccl`.
6
7A package set is available for each CUDA version, so for example
8`cudaPackages_11_6`. Within each set is a matching version of the above listed
9packages. Additionally, other versions of the packages that are packaged and
10compatible are available as well. For example, there can be a
11`cudaPackages.cudnn_8_3` package.
12
13To use one or more CUDA packages in an expression, give the expression a `cudaPackages` parameter, and in case CUDA is optional
14```nix
15{ config
16, cudaSupport ? config.cudaSupport
17, cudaPackages ? { }
18, ...
19}:
20```
21
22When using `callPackage`, you can choose to pass in a different variant, e.g.
23when a different version of the toolkit suffices
24```nix
25mypkg = callPackage { cudaPackages = cudaPackages_11_5; }
26```
27
28If another version of say `cudnn` or `cutensor` is needed, you can override the
29package set to make it the default. This guarantees you get a consistent package
30set.
31```nix
32mypkg = let
33 cudaPackages = cudaPackages_11_5.overrideScope (final: prev: {
34 cudnn = prev.cudnn_8_3;
35 }});
36in callPackage { inherit cudaPackages; };
37```
38
39The CUDA NVCC compiler requires flags to determine which hardware you
40want to target for in terms of SASS (real hardware) or PTX (JIT kernels).
41
42Nixpkgs tries to target support real architecture defaults based on the
43CUDA toolkit version with PTX support for future hardware. Experienced
44users may optimize this configuration for a variety of reasons such as
45reducing binary size and compile time, supporting legacy hardware, or
46optimizing for specific hardware.
47
48You may provide capabilities to add support or reduce binary size through
49`config` using `cudaCapabilities = [ "6.0" "7.0" ];` and
50`cudaForwardCompat = true;` if you want PTX support for future hardware.
51
52Please consult [GPUs supported](https://en.wikipedia.org/wiki/CUDA#GPUs_supported)
53for your specific card(s).
54
55Library maintainers should consult [NVCC Docs](https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/)
56and release notes for their software package.
57
58## Adding a new CUDA release {#adding-a-new-cuda-release}
59
60> **WARNING**
61>
62> 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).
63
64The 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.
65
66All 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.
67
68### Updating CUDA redistributables {#updating-cuda-redistributables}
69
701. Go to NVIDIA's index of CUDA redistributables: <https://developer.download.nvidia.com/compute/cuda/redist/>
712. Copy the `redistrib_*.json` corresponding to the release to `pkgs/development/compilers/cudatoolkit/redist/manifests`.
723. Generate the `redistrib_features_*.json` file by running:
73
74 ```bash
75 nix run github:ConnorBaker/cuda-redist-find-features -- <path to manifest>
76 ```
77
78 That command will generate the `redistrib_features_*.json` file in the same directory as the manifest.
79
804. Include the path to the new manifest in `pkgs/development/compilers/cudatoolkit/redist/extension.nix`.
81
82### Updating the CUDA Toolkit runfile installer {#updating-the-cuda-toolkit}
83
84> **WARNING**
85>
86> While the CUDA Toolkit runfile installer is still available in Nixpkgs as the [`cudaPackages.cudatoolkit`](https://search.nixos.org/packages?channel=unstable&type=packages&query=cudaPackages.cudatoolkit) attribute, its use is not recommended and should it be considered deprecated. Please migrate to the CUDA redistributables provided by the [`cudaPackages`](https://search.nixos.org/packages?channel=unstable&type=packages&query=cudaPackages) package set.
87>
88> To ensure packages relying on the CUDA Toolkit runfile installer continue to build, it will continue to be updated until a migration path is available.
89
901. Go to NVIDIA's CUDA Toolkit runfile installer download page: <https://developer.nvidia.com/cuda-downloads>
912. Select the appropriate OS, architecture, distribution, and version, and installer type.
92
93 - For example: Linux, x86_64, Ubuntu, 22.04, runfile (local)
94 - NOTE: Typically, we use the Ubuntu runfile. It is unclear if the runfile for other distributions will work.
95
963. Take the link provided by the installer instructions on the webpage after selecting the installer type and get its hash by running:
97
98 ```bash
99 nix store prefetch-file --hash-type sha256 <link>
100 ```
101
1024. Update `pkgs/development/compilers/cudatoolkit/versions.toml` to include the release.
103
104### Updating the CUDA package set {#updating-the-cuda-package-set}
105
1061. Include a new `cudaPackages_<major>_<minor>` package set in `pkgs/top-level/all-packages.nix`.
107
108 - NOTE: Changing the default CUDA package set should occur in a separate PR, allowing time for additional testing.
109
1102. Successfully build the closure of the new package set, updating `pkgs/development/compilers/cudatoolkit/redist/overrides.nix` as needed. Below are some common failures:
111
112| Unable to ... | During ... | Reason | Solution | Note |
113| --- | --- | --- | --- | --- |
114| Find headers | `configurePhase` or `buildPhase` | Missing dependency on a `dev` output | Add the missing dependency | The `dev` output typically contain the headers |
115| Find libraries | `configurePhase` | Missing dependency on a `dev` output | Add the missing dependency | The `dev` output typically contain CMake configuration files |
116| 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 |
117
118In 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 [`cudaPackages.autoAddOpenGLRunpath`](https://search.nixos.org/packages?channel=unstable&type=packages&query=cudaPackages.autoAddOpenGLRunpath). 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.