Caffe+Ubuntu14.04+CUDA7.5安装笔记

ubuntu 14.04安装

• 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：

  1. boot 设置 200M 主分区
  2. / 设置 50000M
  3. swap 设置 4000M
  4. home 设置 剩余M

cuda7.5安装

1. cuda7.5下载：地址 https://developer.nvidia.com/cuda-downloads
   文件： cuda_7.5.18_linux.run
2. 登录界面前按Ctrl+Alt+F1进入命令提示符 【禁用nouveau驱动】
3. 执行命令： sudo vi /etc/modprobe.d/blacklist-nouveau.conf

4. 输入以下内容

   blacklist nouveau
   options nouveau modset=0
   

   最后保存退出（:wq)

5. 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。

6. 重启后，登录界面时直接按Ctrl+Alt+F1进入命令提示符

7. 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

8. 假设cuda_7.5.18_linux.run位于 ~ 目录，切换到~目录： cd ~

9. 执行命令： sudo sh cude_7.5.18_linux.run

10. 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

11. 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

12. 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
    

cuda安装过程中遇到的问题

1. 在执行命令： sudo apt-get install g++ 时出现以下错误
   g++ : Depends: g++-4.8 (>= 4.8.2-5~) but it is not going to be installed

2. 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。

3. 首先，备份原始源文件source.list
   sudo cp /etc/apt/sources.list /etc/apt/sources.list_backup

4. 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

5. 最后 sudo apt-get update

安装caffe

1. 下载caffe：执行命令： sudo git clone https://github.com/BVLC/caffe.git

2. 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

3. 编译caffe
   cd ~/caffe
   sudo cp Makefile.config.example Makefile.config
   make all

4. 配置运行环境
   sudo vi /etc/ld.so.conf.d/caffe.conf
   添加内容：
   /usr/local/cuda/lib64

5. 更新配置
   sudo ldconfig
6. caffe测试，执行以下命令：
   cd ~/caffe
   sudo sh data/mnist/get_mnist.sh
   sudo sh examples/mnist/create_mnist.sh
   最后测试：
   sudo sh examples/mnist/train_lenet.sh

运行结果如下：

其他依赖项

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

opencv3.0安装

1. 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

2. 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

3. 在Install-OpenCV-master文件夹中包含安装各个版本opencv脚本

4. 切换到目录执行：
   sudo sh Ubuntu/dependencies.sh
   安装依赖项

5. 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

6. 修改Makefile.config

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

7. (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"

安装opencv3遇到的问题

1. 在执行
   sudo sh Ubuntu/3.0/opencv3_0_0.sh
   出现有个地方一直卡住了，显示在下载一个文件： ippicv_linux_20141027.tgz
   因为墙的原因，这个文件无法下载下来

2. [其他文档] ippicv_linux_20141027.tgz 处下载文件 ippicv_linux_20141027.tgz

3. 下载后拷贝到opencv/3rdparty/ippicv/downloads/linux-8b449a536a2157bcad08a2b9f266828b/ 目录下即

4. http://stackoverflow.com/questions/25726768/opencv-3-0-trouble-with-installation

安装BLAS——选择MKL

1. 首先下载 MKL(Intel(R) Parallel Studio XE Cluster Edition for Linux 2016)
   网址： https://software.intel.com/en-us/intel-education-offerings
   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， 需要自己申请序列号

2. 下载完成后： parallel_studio_xe_2016.tgz

3. 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

4. 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

5. 修改Makefile.config

       # BLAS choice:
       # atlas for ATLAS (default)
       # mkl for MKL
       # open for OpenBlas
       BLAS := mkl

cuDNN安装

1. cudnn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

2. 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

3. 修改Makefile.config

   
   # cuDNN acceleration switch (uncomment to build with cuDNN).
   
   USE_CUDNN := 1

重新编译测试caffe

1. 编译

   sudo make clean
   sudo make all

2. sample测试： （ 比不使用cudnn快很多）
   sh data/mnist/get_mnist.sh
   sh examples/mnist/create_mnist.sh

3. 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
   

其他

1. Vi编辑命令 常用vi编辑器命令行

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

1. 搜狗输入法安装
   Ubuntu14.04安装搜狗输入法

2. im-config 然后 ibus选取fcitx

3. fcitx-config-gtk3

参考资料

1. Caffe学习系列(1)：安装配置ubuntu14.04+cuda7.5+caffe+cudnn
2. Caffe + Ubuntu 15.04 + CUDA 7.5 新手安装配置指南
3. ubuntu 14.04 install g++问题＂g++:Depends:g++-4.8(>= 4.8.2-5
4. ippicv_linux_20141027.tgz
5. http://stackoverflow.com/questions/25726768/opencv-3-0-trouble-with-installation