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先到官网下载ubuntu14.04，网址：http://www.ubuntu.com/download/desktop
ubuntu-14.04.4-desktop-amd64.iso
参考：Ubuntu14.04 安装及使用：[1]制作安装U盘 制作安装U盘
然后参考：Ubantu14.04安装教程 安装系统
分区：
输入以下内容
blacklist nouveau
options nouveau modset=0
最后保存退出（:wq)
执行命令： sudo update-initramfs -u
再执行命令： lspci | grep nouveau 查看是否有内容
如果没有内容 ，说明禁用成功，如果有内容，就重启一下再查看
sudo reboot
重启后，进入登录界面的时候，不要登录进入桌面，直接按Ctrl+Alt+F1进入命令提示符。
重启后，登录界面时直接按Ctrl+Alt+F1进入命令提示符
安装依赖项：
sudo service lightdm stop
sudo apt-get install g++
sudo apt-get install git
sudo apt-get install freeglut3-dev
假设cuda_7.5.18_linux.run位于 ~ 目录，切换到~目录： cd ~
执行命令： sudo sh cude_7.5.18_linux.run
安装的时候，要让你先看一堆文字（EULA)，我们直接不停的按空格键到100%，然后输入一堆accept,yes,yes或回车进行安装。安装完成后，重启，然后用ls查看一下：
ls /dev/nvidia*
会看到/dev目录下生成多个nvidia开头文件(夹)
或者输入命令： sudo nvcc –version 会显示类似以下信息
dl@dl-Z170X-Gaming-3:~$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2015 NVIDIA Corporation
Built on Tue_Aug_11_14:27:32_CDT_2015
Cuda compilation tools, release 7.5, V7.5.17配置环境变量
执行命令： sudo vi /etc/profile
文件底部添加以下内容：
export PATH=/usr/local/cuda-7.5/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH编译samples
安装成功后在～目录下可以看到一个NVIDIA_CUDA-7.5_Samples文件夹，切换到目录
输入sudo make， 大概等个十多分钟后就会把全部的samples编译完毕。生成的可执行文件位于
NVIDIA_CUDA-7.5_Samples/bin/x86_64/linux/release 目录下
比如运行 ./nbody可以看到以下demo
是因为ubuntu 14.04的源过旧或不可访问导致，可以通过更新源解决。
首先，备份原始源文件source.list
sudo cp /etc/apt/sources.list /etc/apt/sources.list_backup
然后
sudo gedit /etc/apt/source.list
在文件尾部添加以下内容
deb http://archive.ubuntu.com/ubuntu/ trusty main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-security main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-updates main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-proposed main restricted universe multiverse
deb http://archive.ubuntu.com/ubuntu/ trusty-backports main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-security main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-updates main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-proposed main restricted universe multiverse
deb-src http://archive.ubuntu.com/ubuntu/ trusty-backports main restricted universe multiverse最后 sudo apt-get update
安装依赖项：
sudo apt-get install libatlas-base-dev
sudo apt-get install libprotobuf-dev
sudo apt-get install libleveldb-dev
sudo apt-get install libsnappy-dev
sudo apt-get install libopencv-dev
sudo apt-get install libboost-all-dev
sudo apt-get install libhdf5-serial-dev
sudo apt-get install libgflags-dev
sudo apt-get install libgoogle-glog-dev
sudo apt-get install liblmdb-dev
sudo apt-get install protobuf-compiler
编译caffe
cd ~/caffe
sudo cp Makefile.config.example Makefile.config
make all
配置运行环境
sudo vi /etc/ld.so.conf.d/caffe.conf
添加内容：
/usr/local/cuda/lib64
运行结果如下：
我们查看caffe目录下 Makefile.config 内容如下：
## 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
# 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
# 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 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 -gencode arch=compute_20,code=sm_21 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_50,code=compute_50
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := mkl
# 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
# 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
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
# 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
# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that ‘make runtest‘ will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @可以看到诸如
# cuDNN acceleration switch (uncomment to build with cuDNN).
# USE_CUDNN := 1
# Uncomment if you‘re using OpenCV 3
# OPENCV_VERSION := 3
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas都是使用默认的设置，我们可以安装其他依赖项提高caffe运行效率
github上有人写好完整的运行脚本自动下载opencv，编译，安装，配置等
Caffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南 作者 Xin-Yu Ou（欧新宇） 可以到他的网盘中下载
PS：为了方便大家使用，我提供一个百度云盘，用于分享部分安装过程中需要用到的软件包和链接地址（所有软件包仅供学术交流使用，请大家尽量去官网下载。）。百度云盘链接: http://pan.baidu.com/s/1qX1uFHa 密码:wysa
在Install-OpenCV-master文件夹中包含安装各个版本opencv脚本
切换到目录执行：
sudo sh Ubuntu/dependencies.sh
安装依赖项
执行opencv3.0安装脚本
sudo sh Ubuntu/3.0/opencv3_0_0.sh
等待安装完成即可
修改Makefile.config
# Uncomment if you‘re using OpenCV 3
OPENCV_VERSION := 3(可选）opencv3.1已经发布，如果要安装最新的opencv3.1，我们可以先执行
sudo sh get_latest_version_download_file.sh
获取最新的地址，然后更新opencv3_0_0.sh中的下载地址，同时需要修正文件名等
arch=$(uname -m)
if [ "$arch" == "i686" -o "$arch" == "i386" -o "$arch" == "i486" -o "$arch" == "i586" ]; then
flag=1
else
flag=0
fi
echo "Installing OpenCV 3.0.0"
mkdir OpenCV
cd OpenCV
echo "Removing any pre-installed ffmpeg and x264"
sudo apt-get -y remove ffmpeg x264 libx264-dev
echo "Installing Dependenices"
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
echo "Downloading OpenCV 3.0.0"
wget -O opencv-3.0.0.zip http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/3.0.0/opencv-3.0.0.zip/download
echo "Installing OpenCV 3.0.0"
unzip opencv-3.0.0.zip
cd opencv-3.0.0
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D INSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON -D WITH_QT=ON -D WITH_OPENGL=ON ..
make -j8
sudo make install
sudo sh -c ‘echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf‘
sudo ldconfig
echo "OpenCV 3.0.0 ready to be used"[其他文档] ippicv_linux_20141027.tgz 处下载文件 ippicv_linux_20141027.tgz
下载后拷贝到opencv/3rdparty/ippicv/downloads/linux-8b449a536a2157bcad08a2b9f266828b/ 目录下即
下载完成后： parallel_studio_xe_2016.tgz
执行以下命令：
$ tar zxvf parallel_studio_xe_2016.tar.gz
$ chmod a+x parallel_studio_xe_2016 -R
$ sh install_GUI.sh
环境配置：
$ sudo gedit /etc/ld.so.conf.d/intel_mkl.conf
然后添加以下内容
/opt/intel/lib/intel64
/opt/intel/mkl/lib/intel64
配置生效： sudo ldconfig -v
安装MKL完成
修改Makefile.config
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := mklcudnn下载
下载地址：https://developer.nvidia.com/cudnn
或者到网盘： http://pan.baidu.com/s/1bnOKBO 下载
下载相应文件cudnn-7.0-linux-x64-v4.0-rc.tgz, 放到~根目录下
切换到～目录，执行命令
sudo tar xvf cudnn-7.0-linux-x64-v4.0-rc.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修改Makefile.config
# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1编译
sudo make clean
sudo make all
sample测试： （ 比不使用cudnn快很多）
sh data/mnist/get_mnist.sh
sh examples/mnist/create_mnist.sh
我们可以将迭代次数增加到50000次
sudo gedit examples/mnist/lenet_solver.prototxt
修改max_iter: 50000
最后：
sh examples/mnist/train_lenet.sh
A:当前行的尾部追加内容
i:游标前插入内容
I:游标后插入内容
o:在鼠标所在行的下面添加内容
O:在鼠标所在行的上面添加内容
ESC:退出编辑模式
Ctrl-T:移动到下一个tab
Backspace:向后移动一个字符
Ctrl-U:删除当前
cw:删除游标所在的字符，然后进入编辑模式
cc:删除游标所在的行，然后进入编辑模式
C:删除从游标所在的位置到行尾的字符，然后进入编辑模式
dd:删除当前行
ndd:删除第n行
D:删除当前行游标所在的位置后面的字符
dw:删除邮编所在的字符
d}:删除当前段剩余的字符
d^:删除游标前到行首的字符
c/pat:删除游标后面到第一次匹配字符间的内容
dn:删除游标后面到下一个匹配字符间的内容
dfa:删除当前行游标到匹配字符间的内容（匹配的字符也将被删）
dta:删除当前行游标到匹配字符间的内容（匹配的字符不被删）
dL:删除从游标到屏幕的最后一行之间的内容
dG:删除从游标到文件末尾之间的内容
J:连结上下两行的内容
p:在游标后面插入buffer中的内容
P:在游标前面插入buffer中的内容
rx:用x替换字符
Rtext:用text从游标开始处进行替换
u:撤销最后的改变
U:还原当前行的内容
x:向后删除游标所在位置的字符
X:向前删除游标前面的字符
nX:删除前面的n个字符，游标所在的字符将不会被删
.:还原最后的改变
~:反转字母的大小写
y:拷贝当前行到新的buffer
yy:拷贝当前行
"xyy:拷贝当前行的buffer名为x的buffer
ye:拷贝当单词的末尾搜狗输入法安装
Ubuntu14.04安装搜狗输入法
im-config 然后 ibus选取fcitx
fcitx-config-gtk3
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原文地址：http://blog.csdn.net/zengdong_1991/article/details/51174044
