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assman/NVIDIA GPU CUDA 10.1 Ubuntu 18.md

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Created July 9, 2020 04:54
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All the stuff to get CUDA 10.1 working with NVIDIA GPUs on Ubuntu 18.04. My notes.
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NVIDIA GPU CUDA 10.1 Ubuntu 18.md

All the stuff to get CUDA 10.1 working with NVIDIA GPUs on Ubuntu 18.04. My notes.

Step 1: Install NVIDIA Driver

  • sudo apt install nvidia-driver-430
  • reboot
  • run nvidia-smi. If it does not show your GPU, stop, fix. If this doesn't work, nothing else will (the rest of the stuff will compile, but won't work)

Step 2: Install CUDA

For me it was: wget https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda-repo-ubuntu1804-10-1-local-10.1.168-418.67_1.0-1_amd64.deb

  • Now install it
sudo dpkg -i cuda-repo-ubuntu1804-10-1-local-10.1.168-418.67_1.0-1_amd64.deb # change package name if yours is different
sudo apt-key add /var/cuda-repo-10-1-local-10.1.168-418.67/7fa2af80.pub # change package name if yours is different
sudo apt-get update
sudo apt install cuda-toolkit-10-1
  • Make sure CUDA paths are set correctly for all users on log-in

Create a new file, /etc/profile.d/cuda.sh

Add the following (make sure paths are correct in your system)

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/lib:$LD_LIBRARY_PATH
export CUDADIR=/usr/local/cuda
export CUDA_HOME=/usr/local/cuda
  • Update cuda config
sudo mv /etc/ld.so.conf.d/cuda-10-1.conf  /etc/ld.so.conf.d/cuda-10-1.conf-orig

Create new cuda.conf:

echo "/usr/local/cuda/lib64" > /etc/ld.so.conf.d/cuda.conf

sudo ldconfig

Get the paths imported in your curent system:

source /etc/profile.d/cuda.sh

Install deps (you may not need all of these if you don't plan to install all the ML stuff I needed later)

sudo apt-get install build-essential cmake libjpeg-dev libpng-dev libtiff-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev libgtk-3-dev libatlas-base-dev gfortran python3-dev pkg-config unzip ffmpeg qtbase5-dev  python3-dev python3-numpy libhdf5-dev libgtk-3-dev libdc1394-22 libdc1394-22-dev libjpeg-dev libtiff5-dev libtesseract-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer-plugins-base1.0-0 libgstreamer-plugins-base1.0-dev libpng16-16 libpng-dev libv4l-dev libtbb-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev v4l-utils libleptonica-dev

download & install cuDNN development & runtime library from NVIDIA runtime. For my system it was :

Runtime:
https://developer.nvidia.com/compute/machine-learning/cudnn/secure/7.6.5.32/Production/10.1_20191031/Ubuntu18_04-x64/libcudnn7_7.6.5.32-1%2Bcuda10.1_amd64.deb

Development:
https://developer.nvidia.com/compute/machine-learning/cudnn/secure/7.6.5.32/Production/10.1_20191031/Ubuntu18_04-x64/libcudnn7-dev_7.6.5.32-1%2Bcuda10.1_amd64.deb

After downloading, install them:

gdebi libcudnn7_7.6.5.32-1+cuda10.1_amd64.deb 
gdebi libcudnn7-dev_7.6.5.32-1+cuda10.1_amd64.deb

Compiling OpenCV master with GPU support (if you need it)

(Let's assume my cwd is /home/pp/fiddle/opencv)

git clone https://github.com/opencv/opencv
git clone https://github.com/opencv/opencv_contrib

cd opencv
mkdir build
cd build

cmake -D CMAKE_BUILD_TYPE=RELEASE \
	-D INSTALL_PYTHON_EXAMPLES=ON \
	-D INSTALL_C_EXAMPLES=OFF \
	-D OPENCV_ENABLE_NONFREE=ON \
	-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_EXTRA_MODULES_PATH=/home/pp/fiddle/opencv//opencv_contrib/modules  \
	-D HAVE_opencv_python3=ON  \
	-D PYTHON_EXECUTABLE=/usr/bin/python3 \
	-D BUILD_EXAMPLES=ON ..
make -j$(nproc)
sudo make install

Note that according to Adrian's pyimagesearch article, you also need to add -D CUDA_ARCH_BIN=XX where XX is your GPU architecture version. However, in my tests, this is not needed and is automatically detected.

If you plan to compile FFMPEG from source to support CUDA you need to copy the package config

sudo cp unix-install/opencv4.pc /usr/lib/pkgconfig/

Compiling Tensorflow with GPU support (if you need it)

sudo apt-get install libcupti-dev
add to LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
wget https://github.com/bazelbuild/bazel/releases/download/0.26.1/bazel-0.26.1-installer-linux-x86_64.sh

install bazel via the script above install tensorflow RT (needs free NVIDIA login)

Compiling Darknet with GPU support (if you need it)

https://github.com/AlexeyAB/darknet (take Alexy's fork, pjreddie main repo has OCV4 compile issues, PR open)

Make file mods:

GPU=1
OPENCV=1
LIBSO=1

change pkg-config to pkg-config --libs opencv4

YoloV3 modify cfg for 4GB, otherwise trying to load YoloV3 into a 4GB card will seg fault. With these changes, YoloV3 model takes 1.6GB memory

[net]
batch=1
subdivisions=1
width=416
height=416

Compiling Dlib with GPU support (if you need it)

Easiest of the lot. After you have everything running, GPU working fine, etc. just do

git clone https://github.com/davisking/dlib
cd dlib
python ./setup.py install

That's all. It will auto configure everything. Make sure its confiured with GPU

In a python shell:

>>> import dlib
>>> dlib.DLIB_USE_CUDA
True
>>> print (dlib.cuda.get_num_devices())
1

Installing face-recognition with GPU support (if you need it)

Just do a pip install face-recognition after you have installed a GPU version of dlib.

FFMPEG with CUDA (if you need it)

Follow instructions at https://developer.nvidia.com/ffmpeg If you need hardware encode support, download and install https://developer.nvidia.com/nvidia-video-codec-sdk first

Here were all my ffmpeg build flags: (note that the link above has lesser flags, I needed more libs for other software)

./configure --enable-cuda --enable-cuvid --enable-nvenc --enable-nonfree --enable-libnpp --enable-vaapi --extra-cflags=-I/usr/local/cuda/include --extra-ldflags=-L/usr/local/cuda/lib64 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls  --enable-libass   --enable-libcaca --enable-libcdio --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi  --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libx264 --enable-shared --enable-nvdec

Then do:

make
sudo make install

FFMPEG with CUDA with ZoneMinder (if you need it)

Finally, I use the following macro to build ZM from source, so it builds/links with the right libs:

alias zm_cmake='git submodule update --init --recursive && cmake -DCMAKE_INSTALL_PREFIX=/usr  -DCMAKE_SKIP_RPATH=ON  -DCMAKE_VERBOSE_MAKEFILE=OFF  -DCMAKE_COLOR_MAKEFILE=ON  -DZM_RUNDIR=/var/run/zm  -DZM_SOCKDIR=/var/run/zm  -DZM_TMPDIR=/var/tmp/zm  -DZM_LOGDIR=/var/log/zm  -DZM_WEBDIR=/usr/share/zoneminder/www  -DZM_CONTENTDIR=/var/cache/zoneminder  -DZM_CGIDIR=/usr/lib/zoneminder/cgi-bin  -DZM_CACHEDIR=/var/cache/zoneminder/cache  -DZM_WEB_USER=www-data  -DZM_WEB_GROUP=www-data  -DCMAKE_INSTALL_SYSCONFDIR=etc/zm  -DZM_CONFIG_DIR=/etc/zm  -DCMAKE_BUILD_TYPE=Release'

So I do:

sudo service zoneminder stop
zm_cmake (in the source root of ZM)
make
sudo make install
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