http://blog.csdn.net/autocyz/article/details/52299889 
http://blog.csdn.net/kexinmcu/article/details/53177238 
http://blog.csdn.net/kexinmcu/article/details/53177238 
http://blog.csdn.net/u010459819/article/details/53057171?locationNum=1&fps=1　＃两天就搞定了确实有点不敢相信

1、安装NVIDIA驱动 
首先去官网上查看适合你GPU的驱动（http://www.nvidia.com/Download/index.aspx?lang=en-us） 例如，本人的GPU适合的驱动如图： 

点击Search（反应比较慢），但很重要！！！不要安装错了显卡驱动。有可能会导致编译caffe时，出现莫名其妙的错误。

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-384 # 这是我的显卡驱动型号

重启系统，使新驱动生效。测试是否安装成功

nvidia-smi # 或者  nvidia-settings

2、安装CUDA

1 下载并安装CUDA 
Cuda是Nvidia的编程语言平台，想使用GPU就必须要使用Cuda。这是下载的网址https://developer.nvidia.com/cuda-release-candidate-download 。因为现在官网上只有Cuda-9.0和Cudnn-7以上的版本。 这里我分享给大家的我的云盘（包括Cuda8.0和Cudnn5.1安装包），免得大家费时间去查找。https://pan.baidu.com/s/1kW8uwkv

载完cuda8.0后，执行如下语句：

sudo sh cuda_8.0.27_linux.run

安装选项如图下所示： 

2 下载后完成修改环境变量，然后修改环境变量。

sudo gedit ~/.bashrc   #修改环境变量  最好在在前面加上 cd ~
export PATH=/usr/local/cuda-8.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH  # 这个是CuDNN的路径
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
source ~/.bashrc  使环境变量生效

sudo gedit /etc/profile   #修改动态环境变量  最好在在前面加上 cd ~
export PATH=/usr/local/cuda/bin:$PATH 
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH 

########################## 二选一   #######################
sudo gedit /etc/ld.so.conf.d/cuda.conf  #创建链接 前面最好加cd ~
/usr/local/cuda/lib64   #在打开文档后添加  
sudo ldconfig    # 使库生效  完成之后重启  
##########################################################
sudo gedit ~/.bash_profile #打开.bash_profile # 最好进入根目录下进行修改
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda
source ~/.bash_profile #使更改的环境变量生效

3.编译测试cuda例子与测试，在命令行输入：

 cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery
 sudo make 
 ./deviceQuery

这里报错是应为Ubuntu16.04自带的gcc5.x版本CUDA不兼容，所以需要降低gcc+版本

sudo apt-get install g++-4.9  
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 20  
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 10  
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.9 20  
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 10  
sudo update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30  
sudo update-alternatives --set cc /usr/bin/gcc  
sudo update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30  
sudo update-alternatives --set c++ /usr/bin/g++  

打印类似如下信息，说明装成功

./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 950M"
  CUDA Driver Version / Runtime Version          9.0 / 8.0
  CUDA Capability Major/Minor version number:    5.0
  Total amount of global memory:                 2003 MBytes (2100232192 bytes)
  ( 5) Multiprocessors, (128) CUDA Cores/MP:     640 CUDA Cores
  GPU Max Clock rate:                            1124 MHz (1.12 GHz)
  Memory Clock rate:                             1001 Mhz
  Memory Bus Width:                              128-bit
  L2 Cache Size:                                 2097152 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce GTX 950M
Result = PASS

3、安装CUDNN

　　首先去官网下载你需要的cudnn，下载的时候需要注册账号。选择对应你cuda版本的cudnn下载。这里我下载的是cudnn5.1，是个压缩文件（.tgz）——— 编译https://developer.nvidia.com/rdp/cudnn-download

sudo tar -zxvf ./cudnn-8.0-linux-x64-v5.1.tgz #解压文件
cd cuda/include                               #这是进入解压之后的include文件
sudo cp cudnn.h /usr/local/cuda/include       #复制头文件
sudo cp lib* /usr/local/cuda/lib64/           #复制动态链接库
cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.5         #删除原有动态文件
sudo ln -s libcudnn.so.5.0.5 libcudnn.so.5    #生成软衔接
sudo ln -s libcudnn.so.5 libcudnn.so          #生成软链接

4、安装MATLAB2014A

1.从http://pan.baidu.com/s/1qYJ9tNm 下载Matlab2014的linux版本及**文件

2.下载完成后将iso文件挂载到Linux

sudo mkdir /media/matlab
mount -o loop path/filenam.iso /media/matlab
 # mount -o loop /home/tony /Downloads/matlab/MATHWORKS_R2014A.iso /media/matlab 按照需要修改
cd /media/matlab
sudo ./install

3 .安装过程中使用readme.txt中的***

4 .安装完成后使用*****下的 license进行**

5 .首先写入编写命令，然后将*****文件夹下的 libmwservices.so 拷贝到 /usr/local/MATLAB/R2014A/bin/glnxa64

 sudo chmod -R a+w /usr/local/MATLAB

6 .建立快捷使用Sudo matlab

sudo apt-get install matlab-support

安装后根据提示输入matlab的安装路径–(/usr/local/MATLAB/R2014A/)，确认即可，用户权限不填，表示所有人都可以用，gcc选否，然后可以在命令窗口输入 sudo matlab 启动了。

5、安装OpenCV3.3.0 
１.从官网上下载opencv3.３.0 （http://opencv.org/releases.html）的源代码并将其解压到你要安装的位置，进入下载文件的目录，如果是用Ubuntu火狐下载，那么默认是home/你的电脑名字/下载

cd home/你的电脑名字/下载 #需要根据具体的路径进行修改
unzip opencv-3.1.0.zip
cd opencv-3.1.0
mkdir build

2.安装opencv需要的一些依赖项

sudo apt-get -y remove ffmpeg x264 libx264-dev
sudo apt-get -y install libopencv-dev
sudo apt-get -y install build-essential checkinstall cmake pkg-config yasm
sudo apt-get -y install libtiff4-dev libjpeg-dev libjasper-dev
sudo apt-get -y install libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev
sudo apt-get -y install python-dev python-numpy
sudo apt-get -y install libtbb-dev
sudo apt-get -y install libqt4-dev libgtk2.0-dev
sudo apt-get -y install libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev
sudo apt-get -y install x264 v4l-utils ffmpeg
sudo apt-get -y install libgtk2.0-dev

3.编译OpenCV，先进入OpenCV的文件夹

cd ~/tony          #这是我OpenCV的文件夹
mkdir build        #新建一个build文件夹，编译的工程都在这个文件夹里
cd build/    
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON -DCUDA_NVCC_FLAGS="-D_FORCE_INLINES" ..
                   #需要点时间比较大约10-20分钟，不用管它。
make -j4           #这是编译花费的时间跟你电脑配置相关，我的笔记本是CPU是4核的所以选-j4
sudo make install  #安装OpenCV很重要的一步,上一步是编译，这步是安装

4.检验安装，输入以下指令查看OpenCV版本号

pkg-config --modversion opencv

6、安装Caffe

1.首先安装各种依赖包

sudo apt-get update 
sudo apt-get install -y build-essential cmake git pkg-config 
sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev   protobuf-compiler 
sudo apt-get install -y libatlas-base-dev 
sudo apt-get install -y--no-install-recommends libboost-all-dev 
sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev 
sudo apt-get install -y python-pip 
sudo apt-get install -y python-dev 
sudo apt-get install -y python-numpy python-scipy  

2.安装caffe

cd ~
git clone https://github.com/BVLC/caffe.git  #从github上git caffe
cd caffe                                     #打开到刚刚git下来的caffe 
sudo cp Makefile.config.example Makefile.config  #将Makefile.config.example的内容复制到Makefile.config 
#因为make指令只能make Makefile.config文件，而Makefile.config.example是caffe给出的makefile例子 
sudo gedit Makefile.config                   #打开Makefile.config文件

这是我的Makefile.config文件，改过的地方我都标注了change ,可以对照修改。

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
 USE_CUDNN := 1 **#change**　＃ 安装CUDA里面有一指标 CUDA Capability...如果小于３则不建议修改此项

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0

# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
#   You should not set this flag if you will be reading LMDBs with any
#   possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1

# Uncomment if you're using OpenCV 3
 OPENCV_VERSION := 3  **# change**

# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++

# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr

# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \ 
        -gencode arch=compute_35,code=sm_35 \
        -gencode arch=compute_50,code=sm_50 \
        -gencode arch=compute_52,code=sm_52 \
        -gencode arch=compute_60,code=sm_60 \
        -gencode arch=compute_61,code=sm_61 \
        -gencode arch=compute_61,code=compute_61
         **#这里删除掉了gencode arch=compute_20,code=sm_20 免得报错**

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas

# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib

# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app
MATLAB_DIR := /usr/local/MATLAB   **#change**
MATLAB_DIR := /usr/local/MATLAB/R2014a **#change**

# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
        /usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
        # $(ANACONDA_HOME)/include/python2.7 \
        # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include

# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
#                 /usr/lib/python3.5/dist-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib  
# PYTHON_LIB := $(ANACONDA_HOME)/lib

# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib

# Uncomment to support layers written in Python (will link against Python libs)
 WITH_PYTHON_LAYER := 1   **#change**

# Whatever else you find you need goes here.
#INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
#LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

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   **#change**


# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib

# NCCL acceleration switch (uncomment to build with NCCL)
# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
# USE_NCCL := 1

# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1

# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1

# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0

# enable pretty build (comment to see full commands)
Q ?= @

接下来是修改Makefile文件（Ubuntu 的gedit命令很注意大小写！！如果打错了，则会创建一个新的文件进行修改）

sudo gedit Makefile

NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS) #将这个修改成下面那个  --ctrl+f查找
NVCCFLAGS +=-D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

３编译caffe

 cd ~ /caffe  　　　　　#这是我的caffe目录
 sudo apt-get install python-opencv  #安装cython, python-opencv
 sudo pip install cython easydict　　
 sudo apt-get install python-numpy python-scipy python-matplotlib python-sklearnpython-skimage python-h5py python-protobuf python-leveldb python-networkx python-nosepython-pandas python-gflags Cython ipython　＃安装依赖库这里跟上面的一些是重复的，因为我是想编译FRCNN 所以我把我要用的包重现安装一遍。
 make pycaffe　　　　　　　　　　#编译caffe的python接口
 sudo gedit /etc/profile　　　＃添加~/caffe/python到$PYTHONPATH：
  # 添加： export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH
 source /etc/profile 　　　　　# 使编译环境生效 
 sudo apt-get install protobuf-c-compiler protobuf-compiler
 sudo make clean 　　　　　　　#第一编译不用添加，如果编译失败必须增需要添加。
 sudo make all -j4 　　　　　# 这里最好是用这个以免编译出现很奇怪的错误

 make runtest　　　　　#运行caffe runtest

成功如下所示： 

４．到了一步那就要恭喜你了，caffe环境马上就安装好了，测试安装，成功如下所示：

sh data/mnist/get_mnist.sh
sh examples/mnist/create_mnist.sh
sh examples/mnist/train_lenet.sh

本人在安装caffe过程出现的问题，正在整理争取出一个问题简集。

安装Pycharm 
将下载的安装包提取到指定文件夹，然后进去文件夹，右键从此打开终端 sudo sh ./pycharm.sh 即可像Window 下安装一样进行. 
** 网址 ： http://idea.imsxm.com/

ＰＳ：附上搜狗输入法安装步骤

安装命令:

sudo apt-get install fcitx libssh2-1
wget "http://pinyin.sogou.com/linux/download.php?f=linux&bit=64" -O "sougou_64.deb"
sudo dpkg -i sougou_64.deb

出现的问题：

dpkg: 处理归档 sougou_64.deb (--install)时出错：
 无法访问归档文件: 没有那个文件或目录
在处理时有错误发生：


正在读取数据库 ... 系统当前共安装有 218082 个文件和目录。)
正准备解包 sougou_64.deb  ...
正在解包 sogoupinyin (2.1.0.0086) ...
dpkg: 依赖关系问题使得 sogoupinyin 的配置工作不能继续：
 sogoupinyin 依赖于 libopencc2 | libopencc1；然而：
  未安装软件包 libopencc2。
  未安装软件包 libopencc1。
 sogoupinyin 依赖于 fcitx-libs (>= 4.2.7)；然而：
  未安装软件包 fcitx-libs。
 sogoupinyin 依赖于 fcitx-libs-qt (>= 4.2.7)；然而：
  未安装软件包 fcitx-libs-qt。

解决办法

sudo apt-get install -f