Ubuntu16.04+cuda9.0+matlab+opencv3.3+caffe服务器配置（附遇到的错误和解决方法）

1.具体安装前需要的依赖包：

ubuntu dependency:
sudo apt-get install –assume-yes libopencv-dev build-essential cmake git libgtk2.0-dev pkg-config python-dev python-numpy libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev libtbb-dev libqt4-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils unzip
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 libopenblas-dev liblapack-dev libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install libblas-dev
sudo apt install libatlas-base-dev
opencv dependency:
sudo apt-get install build-essential cmake git
sudo apt-get install ffmpeg libopencv-dev libgtk-3-dev python-numpy python3-numpy libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libv4l-dev libtbb-dev qtbase5-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils unzip

2. 安装Nvidia 显卡驱动:

安装文件：NVIDIA-Linux-x86_64-384.98.run (与Titan Xp 显卡配套)
命令： sudo sh NVIDIA-Linux-x86_64-384.98.run
检验方法：nvidia-smi 出现显卡信息

3. 安装Cuda 9.0:

安装deb文：cuda-repo-ubuntu1604-9-0-local-rc_9.0.103-1_amd64.deb（与Nvidia驱动配套）
命令： sudo dpkg -i cuda-repo-ubuntu1604-9-0-local-rc_9.0.103-1_amd64.deb
sudo apt-key add /var/cuda…/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda
声明环境变量：
sudo gedit ~/.bashrc
添加：export PATH=/usr/local/cuda-8.0/bin\({PATH:+:\){PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64\({ LD_LIBRARY_PATH:+:\)
{ LD_LIBRARY_PATH }}
检验方法：
cd /usr/local/cuda-9.0/samples/1_Utilities/deviceQuery
make
sudo ./deviceQuery 出现GPU信息

4.安装cuDNN:

安装文件：cuDNN v7.0.5
命令：进入include
sudo cp cudnn.h /usr/local/cuda/include/
进入lib64
sudo cp lib* /usr/local/cuda/lib64/
cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.7 #删除原有动态文件
sudo ln -s libcudnn.so.7.0.5 libcudnn.so.7 #生成软链接（注意这里要和自己下载的cudnn版本对应，可以在/usr/local/cuda/lib64下查看自己libcudnn的版本）
sudo ln -s libcudnn.so.7 libcudnn.so

5.安装opencv:

安装文件：opencv 3.3.0
为解决与cuda9.0不兼容的问题，用以下方法解决：
问题：CMake Error: The following variables are used in this project, but they are set to NOTFOUND.
Please set them or make sure they are set and tested correctly in the CMake files:
CUDA_nppi_LIBRARY (ADVANCED)
linked by target “opencv_cudev” in directory D:/Cproject/opencv/opencv/sources/modules/cudev
…
解决方案：http://blog.csdn.net/u014613745/article/details/78310916
1）.找到FindCUDA.cmake文件
找到行
find_cuda_helper_libs(nppi)
改为
find_cuda_helper_libs(nppial)
find_cuda_helper_libs(nppicc)
find_cuda_helper_libs(nppicom)
find_cuda_helper_libs(nppidei)
find_cuda_helper_libs(nppif)
find_cuda_helper_libs(nppig)
find_cuda_helper_libs(nppim)
find_cuda_helper_libs(nppist)
find_cuda_helper_libs(nppisu)
find_cuda_helper_libs(nppitc)

2）.找到行
set(CUDA_npp_LIBRARY “\({CUDA_nppc_LIBRARY};\){CUDA_nppi_LIBRARY};\({CUDA_npps_LIBRARY}”) 改为 set(CUDA_npp_LIBRARY “\){CUDA_nppc_LIBRARY};\({CUDA_nppial_LIBRARY};\){CUDA_nppicc_LIBRARY};\({CUDA_nppicom_LIBRARY};\){CUDA_nppidei_LIBRARY};\({CUDA_nppif_LIBRARY};\){CUDA_nppig_LIBRARY};\({CUDA_nppim_LIBRARY};\){CUDA_nppist_LIBRARY};\({CUDA_nppisu_LIBRARY};\){CUDA_nppitc_LIBRARY};${CUDA_npps_LIBRARY}”)
3）.找到行
unset(CUDA_nppi_LIBRARY CACHE)
改为
unset(CUDA_nppial_LIBRARY CACHE)
unset(CUDA_nppicc_LIBRARY CACHE)
unset(CUDA_nppicom_LIBRARY CACHE)
unset(CUDA_nppidei_LIBRARY CACHE)
unset(CUDA_nppif_LIBRARY CACHE)
unset(CUDA_nppig_LIBRARY CACHE)
unset(CUDA_nppim_LIBRARY CACHE)
unset(CUDA_nppist_LIBRARY CACHE)
unset(CUDA_nppisu_LIBRARY CACHE)
unset(CUDA_nppitc_LIBRARY CACHE)
4）.找到文件OpenCVDetectCUDA.cmake
修改以下几行
…
set(__cuda_arch_ptx “”)
if(CUDA_GENERATION STREQUAL “Fermi”)
set(__cuda_arch_bin “2.0”)
elseif(CUDA_GENERATION STREQUAL “Kepler”)
set(__cuda_arch_bin “3.0 3.5 3.7”)
…
改为
…
set(__cuda_arch_ptx “”)
if(CUDA_GENERATION STREQUAL “Kepler”)
set(__cuda_arch_bin “3.0 3.5 3.7”)
elseif(CUDA_GENERATION STREQUAL “Maxwell”)
set(__cuda_arch_bin “5.0 5.2”)
…
问题：ippicv下载不下来，无法继续编译
解决方法：把ippicv_2017u2_lnx_intel64_20170418.tgz在github上的opencv 3rdparty中下载下来；查看/3rdparty/ippicv/ippicv.cmake文件，将下载下来的.tagz文件重新命名成“对应的hash码-ippicv_linux_20170418.tgz”; 将重命名的文件保存至opencv3.3.0/.cache/ippicv下。
问题：
5）.cuda9中有一个单独的halffloat(cuda_fp16.h)头文件,也应该被包括在opencv的目录里，将头文件cuda_fp16.h添加至 opencv\modules\cudev\include\opencv2\cudev\common.hpp，即在common.hpp中添加

include

随后进入opencv，进行编译：
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D CUDA_GENERATION=Kepler ..
sudo make
sudo make install
测试；

6. matlab安装：

安装文件：R2016a.iso文件
mkdir ~/matlab_iso
sudo mount -o loop R2016a_glnxa64.iso ~/matlab_iso
cd ~/matlab_iso
sudo ./install
不使用Internet激活，秘钥：09806-07443-53955-64350-21751-41297
安装路径：/usr/local/MATLAB/R2016a
安装完成后将libmwservices.so复制到/usr/local/MATLAB/R2014a/bin/glnxa64中：
sudo cp libmwservices.so /usr/local/MATLAB/R2016a/bin/glnxa64/libmwservices.so

7.多gpu编程，安装nccl

git clone https://github.com/NVIDIA/nccl.git
cd nccl
sudo make install -j4

8. 安装caffe

安装文件：github下载caffe-master
命令：
cd caffe-master
sudo cp Makefile.config.example Makefile.config
sudo gedit Makefile.config
（１）修改Makefile.config文件
若使用cudnn，则将# USE_CUDNN := 1 修改成： USE_CUDNN := 1

若使用的opencv版本是3的，则将# OPENCV_VERSION := 3 修改为： OPENCV_VERSION := 3

若要使用python来编写layer，则需要将# WITH_PYTHON_LAYER := 1 修改为 WITH_PYTHON_LAYER := 1

取消对行 USE_NCCL := 1 的注释。这可以启用在多个 GPU 上运行 Caffe 所需的 NCCL。

将# Whatever else you find you need goes here.下面的 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
修改为： INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial //这是因为ubuntu16.04的文件包含位置发生了变化，尤其是需要用到的hdf5的位置，所以需要更改这一路径

删除MakeFile.config 中关于compute_20 compute_21的内容来兼容CUDA>=9.0

若使用MATLAB接口的话，则要MATLAB_DIR换成你自己的MATLAB安装路径
MATLAB_DIR := /usr/local
MATLAB_DIR := /usr/local/MATLAB/R2016a

（２）打开makefile文件
将
NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC \((COMMON_FLAGS) 替换 NVCCFLAGS += -D_FORCE_INLINES -ccbin=\)(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
最后：
sudo make all -j8
sudo make test
sudo make runtest
sudo make pycaffe
sudo make matcaffe
出现问题：NVCC src/caffe/test/test_im2col_kernel.cu
nvcc fatal：Unsupported gpu architecture ‘compute_20’
解决方案：删除MakeFile.config 中关于compute_20 compute_21的内容来兼容CUDA>=9.0
注意：安装caffe前，确认/usr/local/cuda-9.0 下有bin目录

9．安装ssh远程服务

sudo apt-get install openssh-server
打开”终端窗口”，输入”sudo ps -e |grep ssh”–>回车–>有sshd,说明ssh服务已经启动，如果没有启动，输入”sudo service ssh start”–>回车–>ssh服务就会启动
打开”终端窗口”，输入”sudo gedit /etc/ssh/sshd_config”–>回车–>把配置文件中的”PermitRootLogin without-password”加一个”#”号,把它注释掉–>再增加一句”PermitRootLogin yes”–>保存，修改成功。

注：蓝色字体为命令行命令