nixpkgs/pkgs/development/cuda-modules/tests/opencv-and-torch/default.nix

82 lines
2.5 KiB
Nix

{
cudaPackages,
lib,
writeGpuTestPython,
# Configuration flags
openCVFirst,
useOpenCVDefaultCuda,
useTorchDefaultCuda,
}:
let
inherit (lib.strings) optionalString;
openCVBlock = ''
import cv2
print("OpenCV version:", cv2.__version__)
# Ensure OpenCV can access the GPU.
assert cv2.cuda.getCudaEnabledDeviceCount() > 0, "No CUDA devices found for OpenCV"
print("OpenCV CUDA device:", cv2.cuda.printCudaDeviceInfo(cv2.cuda.getDevice()))
# Ensure OpenCV can access the GPU.
print(cv2.getBuildInformation())
a = cv2.cuda.GpuMat(size=(256, 256), type=cv2.CV_32S, s=1)
b = cv2.cuda.GpuMat(size=(256, 256), type=cv2.CV_32S, s=1)
c = int(cv2.cuda.sum(cv2.cuda.add(a, b))[0]) # OpenCV returns a Scalar float object.
assert c == 2 * 256 * 256, f"Expected {2 * 256 * 256} OpenCV, got {c}"
'';
torchBlock = ''
import torch
print("Torch version:", torch.__version__)
# Set up the GPU.
torch.cuda.init()
# Ensure the GPU is available.
assert torch.cuda.is_available(), "CUDA is not available to Torch"
print("Torch CUDA device:", torch.cuda.get_device_properties(torch.cuda.current_device()))
a = torch.ones(256, 256, dtype=torch.int32).cuda()
b = torch.ones(256, 256, dtype=torch.int32).cuda()
c = (a + b).sum().item()
assert c == 2 * 256 * 256, f"Expected {2 * 256 * 256} for Torch, got {c}"
'';
content = if openCVFirst then openCVBlock + torchBlock else torchBlock + openCVBlock;
torchName = "torch" + optionalString useTorchDefaultCuda "-with-default-cuda";
openCVName = "opencv4" + optionalString useOpenCVDefaultCuda "-with-default-cuda";
in
# TODO: Ensure the expected CUDA libraries are loaded.
# TODO: Ensure GPU access works as expected.
writeGpuTestPython {
name = if openCVFirst then "${openCVName}-then-${torchName}" else "${torchName}-then-${openCVName}";
libraries =
# NOTE: These are purposefully in this order.
pythonPackages:
let
effectiveOpenCV = pythonPackages.opencv4.override (prevAttrs: {
cudaPackages = if useOpenCVDefaultCuda then prevAttrs.cudaPackages else cudaPackages;
});
effectiveTorch = pythonPackages.torchWithCuda.override (prevAttrs: {
cudaPackages = if useTorchDefaultCuda then prevAttrs.cudaPackages else cudaPackages;
});
in
if openCVFirst then
[
effectiveOpenCV
effectiveTorch
]
else
[
effectiveTorch
effectiveOpenCV
];
} content