2.1 KiB
CUDA
CUDA-only packages are stored in the cudaPackages packages set. This set
includes the cudatoolkit, portions of the toolkit in separate derivations,
cudnn, cutensor and nccl.
A package set is available for each CUDA version, so for example
cudaPackages_11_6. Within each set is a matching version of the above listed
packages. Additionally, other versions of the packages that are packaged and
compatible are available as well. For example, there can be a
cudaPackages.cudnn_8_3_2 package.
To use one or more CUDA packages in an expression, give the expression a cudaPackages parameter, and in case CUDA is optional
cudaSupport ? false
cudaPackages ? {}
When using callPackage, you can choose to pass in a different variant, e.g.
when a different version of the toolkit suffices
mypkg = callPackage { cudaPackages = cudaPackages_11_5; }
If another version of say cudnn or cutensor is needed, you can override the
package set to make it the default. This guarantees you get a consistent package
set.
mypkg = let
cudaPackages = cudaPackages_11_5.overrideScope' (final: prev: {
cudnn = prev.cudnn_8_3_2;
}});
in callPackage { inherit cudaPackages; };
The CUDA NVCC compiler requires flags to determine which hardware you want to target for in terms of SASS (real hardware) or PTX (JIT kernels).
Nixpkgs tries to target support real architecture defaults based on the CUDA toolkit version with PTX support for future hardware. Experienced users may optimize this configuration for a variety of reasons such as reducing binary size and compile time, supporting legacy hardware, or optimizing for specific hardware.
You may provide capabilities to add support or reduce binary size through
config using cudaCapabilities = [ "6.0" "7.0" ]; and
cudaForwardCompat = true; if you want PTX support for future hardware.
Please consult GPUs supported for your specific card(s).
Library maintainers should consult NVCC Docs and release notes for their software package.