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

cuda7.5下载：地址 https://developer.nvidia.com/cuda-downloads

文件： cuda_7.5.18_linux.run
登录界面前按Ctrl+Alt+F1进入命令提示符【禁用nouveau驱动】
执行命令： sudo vi /etc/modprobe.d/blacklist-nouveau.conf

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

cuda安装过程中遇到的问题

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
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caffe

下载caffe：执行命令： sudo git clone https://github.com/BVLC/caffe.git
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
更新配置
sudo ldconfig
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

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"

安装opencv3遇到的问题

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

安装BLAS——选择MKL

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
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 := mkl

cuDNN安装

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

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

重新编译测试caffe

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

其他

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

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

参考资料

时间： 2024-10-23 03:26:23

Caffe+Ubuntu14.04+CUDA7.5安装笔记

ubuntu 14.04安装

cuda7.5安装

cuda安装过程中遇到的问题

安装caffe

其他依赖项

opencv3.0安装

安装opencv3遇到的问题

安装BLAS——选择MKL

cuDNN安装

重新编译测试caffe

其他

参考资料

Caffe+Ubuntu14.04+CUDA7.5安装笔记的相关文章

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docker在ubuntu14.04下的安装笔记

Ubuntu14.04+CUDA7.5+Anaconda2-4.0.0+Matlab2014b+Caffe from Scratch

ubuntu14.04下手动安装eclipse

ubuntu14.04 下手动安装java jdk

ubuntu14.04 64位安装WPS（成功解决没有ia32-libs的问题）

Ubuntu14.04用apt安装CDH5.1.2[Apache Hadoop 2.3.0]

ubuntu14.04 flash driver 安装

Ubuntu14.04(64位)安装ATI_Radeon_R7_M265显卡驱动