Prerequisite: conda and/or miniconda are already installed
- Create a conda environment.
$ conda create -n dlib python=3.8 cmake ipython- Activate the environment.
$ conda activate dlib- Install CUDA and cuDNN with
condausing nvidia channel
$ conda install cuda cudnn -c nvidiaThen find the path to the nvcc of this environment. We will use this path for the build step below
$which nvcc
/path/to/your/miniconda3/envs/dlib/bin/- Install dlib. Clone and build dlib from source
$ git clone https://github.com/davisking/dlib.git
$ cd dlib
$ mkdir build
$ cd build
$ cmake .. -DDLIB_USE_CUDA=1 -DUSE_AVX_INSTRUCTIONS=1 -DCUDAToolkit_ROOT=/path/to/your/miniconda3/envs/dlib/bin/
$ cmake --build .
$ cd ..
$ python setup.py install --set DLIB_USE_CUDA=1- Test dlib
(dlib) $ ipython
Python 3.8.12 (default, Oct 12 2021, 13:49:34)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.27.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import dlib
In [2]: dlib.DLIB_USE_CUDA
Out[2]: True
In [3]: print(dlib.cuda.get_num_devices())
1
i've added some tweaks in my build
-install cuda 12.8
-install vs_BuildTools ->install Clang
-install intel onemkl
-install OpenBLAS
cmake .. -DDLIB_USE_MKL_WITH_TBB=1 -DDLIB_USE_BLAS=1 -DDLIB_USE_CUDA=1 -DUSE_AVX_INSTRUCTIONS=1 -DUSE_SS2_INSTRUCTIONS=1 -DUSE_SS4_INSTRUCTIONS=1 -DCUDAToolkit_ROOT=E:\Source_Codes\WhoIsShe.conda\Library\bin\
activate conda environment
pip uninstall dlib #uninstall previous dlib
python setup.py install --set DLIB_USE_CUDA=1
DLIB CUDA is available
DLIB BLAS is available
DLIB LAPACK is available
DLIB AVX is available
... and now face_recognition webcam face blur works flawlessly in my GTX 970