4.10.0.82

OpenCV python wheels built against CUDA 12.5, Nvidia Video Codec SDK 12.2 and cuDNN 9.1.1.

Suitable for all devices of compute capability >= 5.0 with binary compatible code for devices of compute capability 5.0-9.0.

Nvidia GPU Computing Toolkit v12.5 is required for import cv2 to work and cuDNN 9.1.1 for accelerated inference when using the dnn module.

Note Windows OS: This wheel relies on cuDNN being installed in the CUDA Toolkit directory. Therefore you can either

1. Download the cuDNN Tarball (not the installer) and extract its contents to your CUDA directory, or

2. Add the path to the bin folder inside the cuDNN installation directory to your PATH_TO_PYTHON_DIST/Lib/site-packages/cv2/config.py file. e.g.

   import os

   BINARIES_PATHS = [
   os.path.join('D:/build/opencv/install', 'x64/vc17/bin'),
   os.path.join(os.getenv('CUDA_PATH', 'C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.5'), 'bin')
   os.path.join('C:/Program Files/NVIDIA/CUDNN/v9.1/bin/12.5')
   ] + BINARIES_PATHS

Build Summary

Windows

Nvidia CMake configuration output

NVIDIA CUDA: YES (ver 12.5, CUFFT CUBLAS NVCUVID NVCUVENC)
NVIDIA GPU arch: 50 52 60 61 70 75 80 86 89 90
NVIDIA PTX archs: 90

cuDNN: YES (ver 9.1.1)

Build commands

set "CMAKE_ARGS=-DWITH_CUDA=ON -DCUDA_ARCH_BIN=5.0;5.2;6.0;6.1;7.0;7.5;8.0;8.6;8.9;9.0 -DCUDA_ARCH_PTX=9.0"
set ENABLE_CONTRIB=1
python.exe setup.py bdist_wheel --py-limited-api=cp37

Ubuntu 22.04

Nvidia CMake configuration output

NVIDIA CUDA: YES (ver 12.5, CUFFT CUBLAS NVCUVID NVCUVENC)
NVIDIA GPU arch: 50 52 53 60 61 62 70 72 75 80 86 87 89 90
NVIDIA PTX archs: 90

cuDNN: YES (ver 9.1.1)

Build commands

export CMAKE_ARGS="-DWITH_CUDA=ON -DCUDA_ARCH_BIN=5.0;5.2;5.3;6.0;6.1;6.2;7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0 -DCUDA_ARCH_PTX=9.0"
export ENABLE_CONTRIB=1
python setup.py bdist_wheel --py-limited-api=cp37