I am not going to walk you through the whole story of using GPU-supported OpenCV in Python on the WSL2, because I have not been a very good storyteller. But rather I am going to tell you the interesting passages of was being a Fedora user with a spring in my step. Let us get ready to fly towards the sky!
Installing the Requirements
Please note, it is important to understand how it works as shown on the NVIDIA blog.
First, at the time of writing, there is no GPU support for WSL2 by default. So, we had to sign-up as a Windows Insider participant, make an update from the System Settings and then reboot our machine.
Second, we had to sign-in to our NVIDIA Developer account, install the NVIDIA driver for CUDA on WSL and then reboot one more time.
> nvidia-smi.exe Fri Nov 20 23:08:43 2020 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 465.12 Driver Version: 465.12 CUDA Version: 11.2 | |-------------------------------+----------------------+----------------------+ | GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 GeForce GTX 1050 WDDM | 00000000:01:00.0 Off | N/A | | N/A 50C P8 N/A / N/A | 75MiB / 4096MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ ...
Third, we had to install the CUDA toolkit along with the cuDNN and then add it into the Fedora system path;
$ curl https://developer.download.nvidia.com/compute/cuda/11.1.1/local_installers/cuda_11.1.1_455.32.00_linux.run -o cuda_11.1.1_455.32.00_linux.run ... $ sudo sh cuda_11.1.1_455.32.00_linux.run ... $ curl https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.0.5/11.1_20201106/RHEL8_1-x64/libcudnn-188.8.131.52-1.cuda11.1.x86_64.rpm -o libcudnn8-184.108.40.206-1.cuda11.1.x86_64.rpm ... $ sudo rpm -ivh libcudnn8-220.127.116.11-1.cuda11.1.x86_64.rpm ... $ echo 'export PATH="/usr/local/cuda-11.1/bin:$PATH"' >> ~/.zshrc # or .bashrc if you are using BASH $ echo 'export LD_LIBRARY_PATH="/usr/local/cuda-11.1/lib64"' >> ~/.zshrc $ source ~/.zshrc
Fourth, we had to make sure that we have installed all the following necessary build tools;
$ sudo dnf install -y cmake python-devel ffmpeg-devel gstreamermm-devel ...
Fifth, it is highly recommended to install OpenCV into a virtual environment rather than directly to the system. So, we need to create one and do not forget to install the
numpy module in it;
$ python3.8 -m pip install virtualenv # Fedora 33 has made Python 3.9 the default ... $ pwd # just to see where we are /home/naru $ cd Researches # or wherever you have decided $ python3.8 -m virtualenv venv # or whatever name you have preferred $ source venv/bin/activate $ pip install numpy ...
Sixth, we had to download OpenCV with the extra modules source codes and extracted them;
$ cd .. $ mkdir Downloads $ cd Downloads $ curl -L https://github.com/opencv/opencv/archive/4.5.0.tar.gz | tar xz ... $ curl -L https://github.com/opencv/opencv_contrib/archive/4.5.0.tar.gz | tar xz ...
Once everything was set up, we were ready for the real journey starting from here!
Seventh, we are going to compile all needed. But we need to adjust the arguments to suit our system:
CUDA_ARCH_BINspecifies the NVIDIA GPU architecture version is used. See yours on the official website.
OPENCV_EXTRA_MODULES_PATHspecifies the path to the source code of the extra modules relative to the
builddirectory. It is also possible to use an absolute path.
PYTHON_EXECUTABLEdetermines which Python interpreter we are going to use.
PYTHON3_NUMPY_INCLUDE_DIRSdetermines where the
numpymodule is installed.
CMAKE_INSTALL_PREFIXdefines the prefix installation path where the files to be compiled are placed.
../opencv-4.5.0defines the path to the source code of the main modules.
$ mkdir --parents build $ cd build $ cmake -D CUDA_ARCH_BIN=6.1 \ > -D WITH_CUDA=ON \ > -D WITH_CUDNN=ON \ > -D OPENCV_DNN_CUDA=ON \ > -D ENABLE_FAST_MATH=1 \ > -D CUDA_FAST_MATH=1 \ > -D WITH_CUBLAS=1 \ > -D OPENCV_ENABLE_NONFREE=ON \ > -D OPENCV_EXTRA_MODULES_PATH=../opencv_contrib-4.5.0/modules \ > -D PYTHON_EXECUTABLE=$HOME/Researches/venv/bin/python \ > -D PYTHON3_NUMPY_INCLUDE_DIRS=$HOME/Researches/venv/lib64/python3.8/site-packages/numpy/core/include \ > -D CMAKE_INSTALL_PREFIX=$HOME/Researches/venv \ > -D CUDNN_LIBRARY=/usr/lib64/libcudnn.so.8 \ > -D CUDNN_INCLUDE_DIR=/usr/include \ > ../opencv-4.5.0 ... $ make ...
Get a cup of coffee or two. But I would have preferred tea to coffee. Hahaha... Once it has done, eighth, install it using a simple command;
$ sudo make install ...
Tada! We are done! You could run it and see if everything works! Well, I do not have enough experience to give you examples of using any CUDA OpenCV modules.
>>> import cv2 >>> cv2.cuda.getDevice() 0 >>> cv2.cuda.getCudaEnabledDeviceCount() 1
Ninth, unfortunately, at the moment, there is no audio and video support for WSL2 by default. But we could try to hack a few things to make it happen! There is a guide on the Ubuntu wiki for that. Just for now, I do not really need any kind of access to the audio/video input device. Might be sometime in the near future, I will make a post about it. But I seriously doubt that I will.
First, we could not rely on the
nvidia-smi but a few. It has been broken in many aspects both on Windows and WSL2. Anyway, if you want but could not find the
nvidia-smi.exe command, just add
C:\Program Files\NVIDIA Corporation\NVSMI into the Windows system path and then reopen the Windows Powershell or Command Prompt. Still indeed, there is no point having it on Windows!
Second, we might have become aware of an error message that has faced our world after installing the CUDA toolkit. It has told us that it has failed to install the CUDA driver. But there is no need to worry even a slightest bit. We do not need it and not ever will.
=========== = Summary = =========== Driver: Not Selected Toolkit: Installed in /usr/local/cuda-11.1/ Samples: Installed in /home/naru/, but missing recommended libraries Please make sure that - PATH includes /usr/local/cuda-11.1/bin - LD_LIBRARY_PATH includes /usr/local/cuda-11.1/lib64, or, add /usr/local/cuda-11.1/lib64 to /etc/ld.so.conf and run ldconfig as root To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-11.1/bin ***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 455.00 is required for CUDA 11.1 functionality to work. To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file: sudo <CudaInstaller>.run --silent --driver Logfile is /var/log/cuda-installer.log
Third, we might have been confused since we could not find any
libcudnn*.so.* files at
/usr/local/cuda-11.1/lib64 after installing the
libcudnn*.rpm file. The reason behind this is the RPM file has installed all compiled library files into a different directory, i.e.
$ rpm -qlp libcudnn8-18.104.22.168-1.cuda11.1.x86_64.rpm ... /usr/lib64/libcudnn.so.8 /usr/lib64/libcudnn.so.8.0.5 /usr/lib64/libcudnn_adv_infer.so.8 /usr/lib64/libcudnn_adv_infer.so.8.0.5 /usr/lib64/libcudnn_adv_train.so.8 /usr/lib64/libcudnn_adv_train.so.8.0.5 /usr/lib64/libcudnn_cnn_infer.so.8 /usr/lib64/libcudnn_cnn_infer.so.8.0.5 /usr/lib64/libcudnn_cnn_train.so.8 /usr/lib64/libcudnn_cnn_train.so.8.0.5 /usr/lib64/libcudnn_ops_infer.so.8 /usr/lib64/libcudnn_ops_infer.so.8.0.5 /usr/lib64/libcudnn_ops_train.so.8 /usr/lib64/libcudnn_ops_train.so.8.0.5
If we did not like this behaviour, we could download the archived file instead and then extract it manually;
$ curl https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.0.5/11.1_20201106/cudnn-11.1-linux-x64-v22.214.171.124.tgz -o cudnn-11.1-linux-x64-v126.96.36.199.tgz ... $ tar -xzvf cudnn-11.1-linux-x64-v188.8.131.52.tgz ... $ sudo cp cuda/include/cudnn*.h /usr/local/cuda-11.1/include $ sudo cp cuda/lib64/libcudnn* /usr/local/cuda-11.1/lib64 $ sudo chmod a+r /usr/local/cuda-11.1/include/cudnn*.h /usr/local/cuda-11.1/lib64/libcudnn*
Fourth, to list all available OpenCV build options as we might want to configure more, we could run
$ cmake -LA ... BUILD_CUDA_STUBS:BOOL=OFF BUILD_DOCS:BOOL=OFF BUILD_EXAMPLES:BOOL=OFF BUILD_IPP_IW:BOOL=ON BUILD_ITT:BOOL=ON BUILD_JASPER:BOOL=OFF BUILD_JAVA:BOOL=ON BUILD_JPEG:BOOL=OFF BUILD_LIST:STRING= BUILD_OPENEXR:BOOL=OFF BUILD_OPENJPEG:BOOL=OFF BUILD_PACKAGE:BOOL=ON BUILD_PERF_TESTS:BOOL=ON BUILD_PNG:BOOL=OFF BUILD_PROTOBUF:BOOL=ON BUILD_SHARED_LIBS:BOOL=ON BUILD_TBB:BOOL=OFF BUILD_TESTS:BOOL=ON BUILD_TIFF:BOOL=OFF BUILD_USE_SYMLINKS:BOOL=OFF BUILD_WEBP:BOOL=OFF BUILD_WITH_DEBUG_INFO:BOOL=OFF BUILD_WITH_DYNAMIC_IPP:BOOL=OFF BUILD_ZLIB:BOOL=OFF ...
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