sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install nvidia-current
先安裝內核頭文件:python
sudo apt-get install linux-headers-$(uname -r)
安裝cuda(官網下載deb文件):linux
sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb sudo apt-get update sudo apt-get install cuda
環境變量:git
export PATH=/usr/local/cuda-7.5/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH
測試:github
cuda-install-samples-7.5.sh ~ cd ~/NVIDIA_CUDA-7.5_Samples cd 1_Utilities/deviceQuery make
執行deviceQuery,若是成功結尾會是Result = PASSapp
cuda環境配置:測試
sudo nano /etc/ld.so.conf.d/cuda.conf /usr/local/cuda/lib64 /lib
完成lib文件的連接操做,執行:ui
sudo ldconfig -v
sudo apt-get install libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
#[compiler] sudo apt-get install build-essential #[required] sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev #[optional] sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
能夠下載opencv的包解壓,也能夠用最新代碼:google
git clone https://github.com/Itseez/opencv.git cd ~/opencv mkdir build cd build
這裏能夠用下載的ippicvlinux20141027.tgz放進~/opencv/3rdparty/ippicv/downloads/linux-8b449a536a2157bcad08a2b9f266828b/ (cmake以前沒這個文件夾,camke的時候會執行下載,20+mb,網速快就不用管了)spa
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local #make -j 後面的數字是並行個數,cpu厲害就設大點。通常是用4 make -j7 sudo make install
解壓cudnn的包(官網得申請,用網盤搜索能找到最新的),有include和lib64,裏面文件複製到對應/usr/local/cuda/對應文件夾裏code
#進到對應文件夾 sudo cp cudnn.h /usr/local/cuda/include/ #進到對應文件夾 sudo cp lib* /usr/local/cuda/lib64/ #可能要再進行一次 sudo ldconfig -v
不知道這裏會不會有文件權限問題,暴力搞一下(這條可先不用)
sudo chmod 777 -R /usr/local/cuda/lib64
git clone https://github.com/BVLC/caffe.git cp Makefile.config.example Makefile.config
修改Makefile.config,去掉cudnn的註釋,其餘的在當前應用場景可不變。
make all make test make runtest
OK了。