1. 安装依赖库：1

2sudo 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

2. 安装BLAS：1

2sudo apt-get install libatlas-base-dev

(or install OpenBLAS or MKL for better CPU performance)

3.安装Opencv：下载 linux 版本 opencv 安装包,unzip解压到指定目录

在安装目录下执行下面code1

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23sudo apt-get update

sudo apt-get upgrade // 更新

//安装依赖库

sudo apt-get install build-essential

sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

//安装opencv

sudo cmake .

sudo make

sudo make install

//配置环境变量

sudo /bin/bash -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf'

sudo ldconfig

sudo gedit/etc /bash.bashrc

PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig

export PKG_CONFIG_PATH

//编译samples程序：

cd samples

sudo cmake .

sudo make -j $(nproc)

cd cpp

//运行测试程序：

sudo ./cpp-example-facedetect ../data/lena.jpg

4. 安装 Matlab

5. 下载Caffe：1

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3cd path_caffe // 到指定目录

git clone git://github.com/BVLC/caffe.git//下载Caffe

cp Makefile.config.example Makefile.config

修改Makefile，在

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3LIBRARIES += glog gflags protobuf leveldb snappy \

lmdb boost_system hdf5_hl hdf5 m \

opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs

处加入后面的opencv_imgcodecs，因为opencv3.0.0把imread相关函数放到imgcodecs.lib中了(原来是imgproc.lib)

CPU model修改Makefile.config文件：去掉CPU_ONLY:= 1的注释

GPU model

mint下安装英伟达驱动和cuda显得格外简单。我在ubuntu下折腾了n久，都以悲剧收场。

安装驱动： 打开mint的驱动管理器 直接安装即可

安装完成后　打开驱动管理器如图所示，

而且系统右下角会出现英伟达的图标，点击打开图标后显示

如果途中选中的是　Intel　那一项可能是因为bios显卡设置的问题，重启电脑进入bios,将显卡设置为独立显卡模式即可。

下载最新的cuda安装包(.deb文件)

sudo dpkg -i xxx.deb

sudo apt-get update

sudo apt-get insatll cuda

也可以直接用软件管理器打开.deb文件直接安装

安装完成后进入 /usr/local/cuda/samples/

编译例子　　　 sudo make

全部编译完成后， 进入 samples/bin/x86_64/linux/release,

sudo下运行deviceQuery,如果出现下列显卡信息， 则驱动及显卡安装成功

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40sudo ./deviceQuery

./deviceQuery Starting.

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

Detected 1 CUDA Capable device(s)

Device 0: "NVS 4200M"

CUDA Driver Version / Runtime Version 7.0 / 7.0

CUDA Capability Major/Minor version number: 2.1

Total amount of global memory: 1024 MBytes (1073414144 bytes)

( 1) Multiprocessors, ( 48) CUDA Cores/MP: 48 CUDA Cores

GPU Max Clock rate: 1620 MHz (1.62 GHz)

Memory Clock rate: 800 Mhz

Memory Bus Width: 64-bit

L2 Cache Size: 65536 bytes

Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65535), 3D=(2048, 2048, 2048)

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: 32768

Warp size: 32

Maximum number of threads per multiprocessor: 1536

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): (65535, 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 = 7.0, CUDA Runtime Version = 7.0, NumDevs = 1, Device0 = NVS 4200M

Result = PASS

效果如下图所示:

然后查看CUDA Capability Major/Minor version number这一项，为2.1.

在Makefile.config文件中，修改

CuDNN model

安装CuDNN

下载CuDNN库文件:cudnn-7.0-linux-x64-v3.0-rc.tgz

tar -xzvf cudnn-7.0-linux-x64-v3.0-rc.tgz

cd cuda/lib64

sudo cp lib* /usr/local/cuda/lib64/

cd ..

cd include

sudo cp cudnn.h /usr/local/cuda/include/

在~/caffe/Makefile.config中，将# USE_CUDNN := 1的注释去掉，

即：USE_CUDNN := 1，并重新编译

6. 编译Caffe：

之前make过的话，要make clean命令清除之前的结果，然后重新执行下面的命令即可

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4make clean

make all -j4

make test -j4

make runtest -j4

出现 error，具体信息如下：

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make: 3* [runtest] Error 127

解决办法

sudo ldconfig /usr/local/cuda-7.0/lib64

error

SolverTest/3.TestInitTrainTestNets

F0910 11:19:15.516458 4395 cudnn_softmax_layer.cpp:19] Check failed: status == CUDNN_STATUS_SUCCESS (6 vs. 0) CUDNN_STATUS_ARCH_MISMATCH

Check failure stack trace:

因为笔记本上的NVS4200M的CUDA Capability是2.1,而官方的cudnn加速是不支持3.0以下的版本的，因此只能在Makefile.config中注释掉USE_CUDNN这行,最后终于编译成功。

执行data下的的相关文件下载数据

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3sudo sh data/mnist/get_mnist.sh

sudo sh data/cifar10/get_cifar10.sh

sudo sh data/ilsvrc12/get_ilsvrc_aux.sh

运行Mnist例子

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sudo ./examples/mnist/train_lenet.sh

7. gcc降级

ubuntu14.04(mint)自带的gcc版本是4.8，MATLAB2014a支持的最高版本为4.7x。因此，需要安装gcc4.7，

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9sudo apt-get install gcc-4.7 g++-4.7 g++-4.7-multilib gcc-4.7-multilib

sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.7 100

sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8 50

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.7 100

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 50

sudo update-alternatives --install /usr/bin/cpp cpp-bin /usr/bin/cpp-4.7 100

sudo update-alternatives --install /usr/bin/cpp cpp-bin /usr/bin/cpp-4.8 50

// 验证gcc默认版本：

gcc -v

8. 编译matlab借口

修改Makefile.config文件，配置matlab安装路径:

MATLAB_DIR :=/home/sun/app/matlab/R2015a

sudo make matcaffe