52bf1163fa
In preparation to eliminate the lib output for the unwrapped clang, use `lib.getLib` to access the `lib` output. |
||
---|---|---|
.. | ||
cuda | ||
cuda-library-samples | ||
cuda-samples | ||
cudatoolkit | ||
cudnn | ||
cutensor | ||
generic-builders | ||
modules | ||
nccl | ||
nccl-tests | ||
saxpy | ||
setup-hooks | ||
tensorrt | ||
tests/opencv-and-torch | ||
aliases.nix | ||
backend-stdenv.nix | ||
flags.nix | ||
gpus.nix | ||
nvcc-compatibilities.nix | ||
README.md | ||
write-gpu-test-python.nix |
Cuda modules
Note
This document is meant to help CUDA maintainers understand the structure of the CUDA packages in Nixpkgs. It is not meant to be a user-facing document. For a user-facing document, see the CUDA section of the manual.
The files in this directory are added (in some way) to the cudaPackages
package set by cuda-packages.nix.
Top-level files
Top-level nix files are included in the initial creation of the cudaPackages
scope. These are typically required for the creation of the finalized
cudaPackages
scope:
backend-stdenv.nix
: Standard environment for CUDA packages.flags.nix
: Flags set, or consumed by, NVCC in order to build packages.gpus.nix
: A list of supported NVIDIA GPUs.nvcc-compatibilities.nix
: NVCC releases and the version range of GCC/Clang they support.
Top-level directories
cuda
: CUDA redistributables! Provides extension tocudaPackages
scope.cudatoolkit
: monolithic CUDA Toolkit run-file installer. Provides extension tocudaPackages
scope.cudnn
: NVIDIA cuDNN library.cutensor
: NVIDIA cuTENSOR library.generic-builders
:- Contains a builder
manifest.nix
which operates on theManifest
type defined inmodules/generic/manifests
. Most packages are built using this builder. - Contains a builder
multiplex.nix
which leverages the Manifest builder. In short, the Multiplex builder adds multiple versions of a single package to single instance of the CUDA Packages package set. It is used primarily for packages likecudnn
andcutensor
.
- Contains a builder
modules
: Nixpkgs modules to check the shape and content of CUDA redistributable and feature manifests. These modules additionally use shims provided by some CUDA packages to allow them to re-use thegenericManifestBuilder
, even if they don't have manifest files of their own.cudnn
andtensorrt
are examples of packages which provide such shims. These modules are further described in the Modules documentation.nccl
: NVIDIA NCCL library.nccl-tests
: NVIDIA NCCL tests.saxpy
: Example CMake project that uses CUDA.setup-hooks
: Nixpkgs setup hooks for CUDA.tensorrt
: NVIDIA TensorRT library.
Distinguished packages
Cuda compatibility
Cuda Compatibility,
available as cudaPackages.cuda_compat
, is a component which makes it possible
to run applications built against a newer CUDA toolkit (for example CUDA 12) on
a machine with an older CUDA driver (for example CUDA 11), which isn't possible
out of the box. At the time of writing, Cuda Compatibility is only available on
the Nvidia Jetson architecture, but Nvidia might release support for more
architectures in the future.
As Cuda Compatibility strictly increases the range of supported applications, we try our best to enable it by default on supported platforms.
Functioning
cuda_compat
simply provides a new libcuda.so
(and associated variants) that
needs to be used in place of the default CUDA driver's libcuda.so
. However,
the other shared libraries of the default driver must still be accessible:
cuda_compat
isn't a complete drop-in replacement for the driver (and that's
the point, otherwise, it would just be a newer driver).
Nvidia's recommendation is to set LD_LIBRARY_PATH
to points to cuda_compat
's
driver. This is fine for a manual, one-shot usage, but in general setting
LD_LIBRARY_PATH
is a red flag. This is global state which short-circuits most
of other dynamic libraries resolution mechanisms and can break things in
non-obvious ways, especially with other Nix-built software.
Cuda compat with Nix
Since cuda_compat
is a known derivation, the easy way to do this in Nix would
be to add cuda_compat
as a dependency of CUDA libraries and applications and
let Nix does its magic by filling the DT_RUNPATH
fields. However,
cuda_compat
itself depends on libnvrm_mem
and libnvrm_gpu
which are loaded
dynamically at runtime from /run/opengl-driver
. This doesn't please the Nix
sandbox when building, which can't find those (a second minor issue is that
addOpenGLRunpathHook
prepends the /run/opengl-driver
path, so that would
still take precedence).
The current solution is to do something similar to addOpenGLRunpathHook
: the
addCudaCompatRunpathHook
prepends to the path to cuda_compat
's libcuda.so
to the DT_RUNPATH
of whichever package includes the hook as a dependency, and
we include the hook by default for packages in cudaPackages
(by adding it as a
inputs in genericManifestBuilder
). We also make sure it's included after
addOpenGLRunpathHook
, so that it appears before in the DT_RUNPATH
and
takes precedence.