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Ubuntu16.04系统中CUDA9+MKL+MATLAB2015b+Anaconda3-5.1.0+OpenCV3.4.0+TensorFlow_GP ...

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

一、安装CUDA

1、查看显卡驱动是否安装好:

$nvidia-settings

2、安装OpenGL:

sudo apt-get install build-essential libgl1-mesa-dev

sudo apt-get install freeglut3-dev

sudo apt-get install libglew-dev libsdl2-dev libsdl2-image-dev libglm-devlibfreetype6-dev

3、进入下载好的.run文件目录,依次执行:

sudo sh cuda_9.0.176_384.81_linux.run

sudo sh cuda_9.0.176.1_linux.run

sudo sh cuda_9.0.176.2_linux.run

(按空格键直至协议许可部分,按提示进行安装,不用再次安装显卡驱动)

安装结束后的显示:

===========

=Summary =

===========

Driver: Not Selected

Toolkit: Installed in /usr/local/cuda-9.0

Samples: Installed in /home/zili, but missing recommended libraries

Pleasemake sure that

- PATH includes /usr/local/cuda-9.0/bin

- LD_LIBRARY_PATH includes /usr/local/cuda-9.0/lib64, or, add/usr/local/cuda-9.0/lib64 to /etc/ld.so.conf and run ldconfig as root


Touninstall the CUDA Toolkit, run the uninstall script in/usr/local/cuda-9.0/bin

Pleasesee CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-9.0/doc/pdffor detailed information on setting up CUDA.


***WARNING:Incomplete installation! This installation did not install the CUDADriver. A driver of version at least 384.00 is required for CUDA 9.0functionality to work.

Toinstall the driver using this installer, run the following command,replacing <CudaInstaller> with the name of this run file:

sudo<CudaInstaller>.run -silent -driver

4、将CUDA添加到Path:

sudo gedit ~/.bashrc

在该文件最后加入以下两行并保存:

export PATH=/usr/local/cuda-9.0/bin:$PATH

export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

使该配置生效:source ~/.bashrc


注意:安装cuda时,不要修改/etc/profile,所有的环境变量均写在~/.bashrc里,修改前者容易出现系统错误。

5cudnn动态库更换:

解压后进入include文件夹,执行:

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

cd ..

再将lib64目录下的动态文件进行复制

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


sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

6、验证CUDA是否安装成功:

进入Samples文件夹,执行

sudo make -j8

cd 1_Utilities/deviceQuery

./deviceQuery

出现以下输出表示安装成功:

./deviceQueryStarting...

CUDADevice Query (Runtime API) version (CUDART static linking)

Detected1 CUDA Capable device(s)

Device0: "GeForce GTX 1080"

CUDADriver Version / Runtime Version 9.0 / 9.0

CUDACapability Major/Minor version number: 6.1

Totalamount of global memory: 8114 MBytes (8508145664bytes)

(20)Multiprocessors, (128) CUDA Cores/MP: 2560 CUDA Cores

GPUMax Clock rate: 1771 MHz (1.77 GHz)

MemoryClock rate: 5005 Mhz

MemoryBus Width: 256-bit

L2Cache Size: 2097152 bytes

MaximumTexture Dimension Size (x,y,z) 1D=(131072), 2D=(131072,65536), 3D=(16384, 16384, 16384)

MaximumLayered 1D Texture Size, (num) layers 1D=(32768), 2048 layers

MaximumLayered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers

Totalamount of constant memory: 65536 bytes

Totalamount of shared memory per block: 49152 bytes

Totalnumber of registers available per block: 65536

Warpsize: 32

Maximumnumber of threads per multiprocessor: 2048

Maximumnumber of threads per block: 1024

Maxdimension size of a thread block (x,y,z): (1024, 1024, 64)

Maxdimension size of a grid size (x,y,z): (2147483647, 65535, 65535)

Maximummemory pitch: 2147483647 bytes

Texturealignment: 512 bytes

Concurrentcopy and kernel execution: Yes with 2 copy engine(s)

Runtime limit on kernels: Yes

IntegratedGPU sharing Host Memory: No

Supporthost page-locked memory mapping: Yes

Alignmentrequirement for Surfaces: Yes

Devicehas ECC support: Disabled

Devicesupports Unified Addressing (UVA): Yes

SupportsCooperative Kernel Launch: Yes

SupportsMultiDevice Co-op Kernel Launch: Yes

DevicePCI Domain ID / Bus ID / location ID: 0 / 1 / 0

ComputeMode:

<Default (multiple host threads can use ::cudaSetDevice() with devicesimultaneously) >

deviceQuery,CUDA Driver = CUDART, CUDA Driver Version = 9.0, CUDA Runtime Version= 9.0, NumDevs = 1

Result= PASS


二、BLAS安装与配置

BLAS(基础线性代数集合)是一个应用程序接口的标准。caffe官网上推荐了三种实现:ATLAS,MKL, orOpenBLAS。其中atlas可以直接通过命令行安装,在此不再介绍。我采用的是intelmkl库,首先,通过链接https://software.intel.com/en-us/qualify-for-free-software/studentintel官网申请学生版的**ParallelStudio XE Cluster Edition **,下载完成之后cd到下载目录进行安装:

$tar zxvf parallel_studio_xe_2018_update2_cluster_edition.tgz #解压下载文件

$chmod 777 parallel_studio_xe_2018_update2_cluster_edition -R #获取文件权限

$cd parallel_studio_xe_2018_update2_cluster_edition/

$sudo ./install_GUI.sh


***:ST5V-XXXXXXXX,选择自定义安装MKL


安装完成之后,进行相关文件的链接:

$sudo gedit /etc/ld.so.conf.d/intel_mkl.conf

在打开的文件中添加库文件:

/opt/intel/lib/intel64

/opt/intel/mkl/lib/intel64


添加完成之后,编译链接使lib文件立即生效:

$sudo ldconfig


三、安装Java

http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html

将下载下来的jdk-8u101-linux-x64.tar.gz文件解压。使用如下命令解压:

sudo tar zxvf ./jdk-8u101-linux-x64.tar.gz

为了方便管理可将解压后的文件移至/usr/java目录下。

设置环境变量:

编辑.bashrc文件sudo gedit ~/.bashrc

在该文件中添加如下内容:

JAVA_HOME=/usr/java

JRE_HOME=$JAVA_HOME/jre

JAVA_BIN=$JAVA_HOME/bin

CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar:$JRE_HOME/lib

PATH=$PATH:$JAVA_HOME/bin:$JRE_HOME/bin

export JAVA_HOME JRE_HOME PATH CLASSPATH


为了让更改立即生效,请在终端执行如下命令:

source ~/.bashrc

通过以上步骤,JDK已安装完成。输入以下命令验证java -version


四、安装MATLAB2015b

注意不要安装Matlab2016b2017b等较新的版本,否则编译出的matcaffe库无法通过makemattest

1、挂载映像文件:

cd~

mkdir matlab_iso

sudo mount -t auto -o loop LinuxMatlab/R2015b_glnxa64.iso matlab_iso/

2、安装Matlab

sudo ./matlab_iso/install

输入安装**:09806-07443-53955-64350-21751-41297

结束安装后取消iso挂载:

umount matlab_iso/

sudo rm -r matlab_iso/ #删除空的文件夹

3、**:

cd /usr/local/MATLAB/R2015b/bin

sudo ./matlab

首先选择无internet手动**,选择license_standalone.lic

然后将*****文件夹中的3*.so文件复制到/usr/local/MATLAB/R2015b/bin/glnxa64目录下:

[email protected]:~/LinuxMatlab/Linux64_*****$

sudo cp R2015b/bin/glnxa64/libcufft.so.7.0.28 /usr/local/MATLAB/R2015b/bin/glnxa64

sudo cp R2015b/bin/glnxa64/libinstutil.so /usr/local/MATLAB/R2015b/bin/glnxa64

sudo cp R2015b/bin/glnxa64/libmwservices.so /usr/local/MATLAB/R2015b/bin/glnxa64

4、创建快捷方式:

打开Ubuntu软件中心,搜索matlab,点击install,在弹出的安装路径填入:/usr/local/MATLAB/R2015b

用户权限不用填,表示全部用户可用、gcc不填

完成安装,在启动栏可以看到有matlab图标。

注意首次打开MATLAB需在终端输入sudo /usr/local/MATLAB/R2015b/bin/matlab才行。


如果Ubuntu软件中心的MATLAB支持接口不是首次安装,出现路径错误,要修改MATLAB的路径可进入/etc/matlab/debconf文件修改。


如果点击启动栏上的MATLAB出现java.lang.runtime.Exception错误,只需要在终端中输入以下命令即可:

sudo chmod -R a+rw ~/.matlab


设置matlab默认工作路径:预设-常规-初始工作文件夹-/home/zili/Programming/MATLAB



caffe通过make mattest后,可选择设置matlab默认工作路径:

sudo gedit /usr/local/MATLAB/R2015b/toolbox/local/matlabrc.m

在该文件最后加入以下语句:

cd '/home/zili/Programming/MATLAB'


UbuntuMatlab路径不能永久保存的问题及其解决方案

原因:pathdef.m文件的权限问题

解决方法:

cd /usr/local/MATLAB/R2015b/toolbox/local

sudo chmod 777 pathdef.m


五、安装Anaconda3

到官网下载anaconda3

地址:https://www.anaconda.com/download/#linux

下载好后执行:

bash ~/Downloads/Anaconda3-5.1.0-Linux-x86_64.sh

注意选择将Anaconda加入Path中。

Anaconda换源:

制定清华的源:

conda config --add channelshttps://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/

有资源显示源地址:

conda config --set show_channel_urls yes


六、编译安装OpenCV3.4.0

注意OpenCV版本!以下方式编译OpenCV3.4.1可能会出错。

1、安装依赖:

sudo apt-get install --assume-yes libopencv-dev build-essential cmake gitlibgtk2.0-dev pkg-config python-dev python-numpy libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev libtbb-dev libqt4-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils unzip

sudo apt-get install ffmpeg libgtk-3-dev python3-numpy qtbase5-dev  unzip


2、下载和编译


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