4.11.0.86

OpenCV python wheels built against CUDA 12.6, Nvidia Video Codec SDK 12.2 and cuDNN 9.6.0.

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 v12.6 is required for import cv2 to work and cuDNN 9.6.0 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 download

1. the cuDNN Tarball (Version->Tarball) and extract its contents to your CUDA directory, or

2. the installer (Version->exe (local)) and the 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/v12.6'), 'bin')
   os.path.join('C:/Program Files/NVIDIA/CUDNN/v9.6/bin/12.6')
   ] + BINARIES_PATHS

Build Summary

Windows

Nvidia CMake configuration output

NVIDIA CUDA: YES (ver 12.6, CUFFT CUBLAS NVCUVID NVCUVENC)
NVIDIA GPU arch: 50 52 60 61 70 75 80 86 89 90
NVIDIA PTX archs: 90

cuDNN: YES (ver 9.6.0)

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 12.6, 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 9.6.0)

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