ubuntu16.10+cuda8.0+cudnn8.0+opencv3.2+caffe+tensorflow
安装Nvidia显卡驱动: 1.在ubuntu下,安装的不一样,打开终端,输入sudo apt-get install linux-headers-generic,安装好之后再安装驱动。 2.安装CUDA9.0
首先去官网下载CUDA 9.0,这里因为写该博客时NVIDIA网站进不去了,就截了一个图,下载的是.run文件
下载完CUDA 9.0之后执行如下语句,运行.run文件
sudo sh cuda_9.0.176_384.98_linux.run
单击回车,一路往下运行,直到提示“是否为NVIDIA安装驱动nvidia-384?”,选择否,因为已经安装好驱动程序了,其他的全都是默认,不过要记住安装位置,默认是安装在/usr/local/cuda文件夹下。
配置环境变量,运行如下命令打开profile文件
sudo gedit /etc/profile
打开文件后在文件末尾添加路径,也就是安装目录,命令如下:
export PATH=/usr/local/cuda-9.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64$LD_LIBRARY_PATH
保存,然后重启电脑
sudo reboot
3.测试CUDA的Samples例子
cd /usr/local/cuda-9.0/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery
如果显示的是关于GPU的信息,则说明安装成功了。
如遇到登录界面无限循环,则表示显卡驱动未装好。则: sudo apt-get remove --purge nvidia-* sudo apt-get install ubuntu-desktop sudo rm /etc/X11/xorg.conf echo 'nouveau' | sudo tee -a /etc/modules 卸干净之后再运行 sudo reboot
如遇到安装cuda找不到linux kernels 则:sudo apt-get install linux-headers-generic
安装cudnn
$ sudo tar xvf cudnn-7.0-linux-x64-v4.0-prod.tgz $ cd cuda/include $ sudo cp .h /usr/local/include/ $ cd ../lib64 $ sudo cp lib /usr/local/lib/ $ cd /usr/local/lib $ sudo chmod +r libcudnn.so.4.0.4 $ sudo ln -sf libcudnn.so.4.0.4 libcudnn.so.4 $ sudo ln -sf libcudnn.so.4 libcudnn.so $ sudo ldconfig
安装opencv3.2 sudo apt-get install build-essential sudo apt-get install cmake git pkg-config libgtk2.0-dev libavcodec-dev libavformat-dev libswscale-dev sudo apt-get install python-dev python-opencv python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
$ cd opencv-3.2.0 $ mkdir build $ cd build $ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_CUDA=OFF .. $ make -j4 $ sudo make install
ippicv_linux_20151201.tgz文件替换
gcc/g++版本降级(一定是5版本)
sudo apt-get install gcc-5 gcc --version ls /usr/bin/gcc* sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 100 sudo update-alternatives --config gcc
sudo apt-get install g++-5 sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 100 sudo update-alternatives --config g++
安装caffe 1.配置文件:
USE_CUDNN := 1
OPENCV_VERSION := 3
CUDA_DIR := /usr/local/cuda-8.0
WITH_PYTHON_LAYER := 1
INCLUDE_DIRS :=
打开makefile文件,做如下修改:
NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)
替换为:
NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs
ifeq ($(USE_OPENCV), 1) LIBRARIES += opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs
- 依赖包:
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 libatlas-base-dev sudo apt-get install libhdf5-serial-dev sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
进入里面的PYTHON文件夹,然后输入 sudo apt install python-pip for req in $(cat requirements.txt); do pip install $req; done
sudo make all sudo make pycaffe
- 环境变量设置 sudo gedit ~/.bashrc export PYTHONPATH=/home/twj/caffe/python:$PYTHONPATH source ~/.bashrc
安装tensorflow-gpu pip install tensorflow-gpu
安装teamvieawer
第一步: 从官网下载ubuntu的deb安装包 第二步: 执行命令sudo dpkg --add-architecture i386 sudo apt-get update sudo apt-get -f install sudo dpkg -i teamviewer_12.0.85001_i386.deb
安装ubuntu chrome
sudo wget http://www.linuxidc.com/files/repo/google-chrome.list -P /etc/apt/sources.list.d/ wget -q -O - https://dl.google.com/linux/linux_signing_key.pub | sudo apt-key add - sudo apt-get update sudo apt-get install google-chrome-stable
安装ubuntu spyder sudo apt install spyder pip install qtconsole pip install nbconvert