CUDA(Compute Unified Device Architecture)，是顯示卡廠商NVIDIA推出的運算平臺CUDA™是一種由NVIDIA推出的通用平行計算架構，該架構使GPU能夠解決複雜的計算問題。開發人員現在可以使用C語言來為CUDA™架構編寫程式，C語言是應用最廣泛的一種高階程式語言。所編寫出的程式於是就可以在支援CUDA™的處理器上以超高效能執行。CUDA3.0已經開始支援C++和FORTRAN。

$ sudo  add-apt-repository ppa:graphics-drivers/ppa

$ sudo apt-get update
$ sudo apt-get install nvidia-367 nvidia-settings nvidia-prime

$ nvidia-settings

sudo md5sum cuda_7.5.18_linux.run     檢驗結果與原始檔的md5sum比較。

$ chmod 777 cuda_7.5.18_linux.run
$ sudo ./cuda_7.5.18_linux.run --override 

===========
= Summary =
===========

Driver:   Not Selected
Toolkit:  Installed in /usr/local/cuda-7.5
Samples:  Installed in /usr/local/cuda-7.5, but missing recommended librarie

$ tar zxvf cudnn-7.5-linux-x64-v5.0-ga.tgz 

$ sudo cp cudnn.h /usr/local/cuda/include/     #複製標頭檔案

$ sudo cp lib* /usr/local/cuda/lib64/    #複製動態連結庫

$ cd /usr/local/cuda/lib64/      
$ sudo rm -rf libcudnn.so libcudnn.so.5    #刪除原有動態檔案
$ sudo ln -s libcudnn.so.5.0.5 libcudnn.so.5
$ sudo ln -s libcudnn.so.5 libcudnn.so

$ sudo gedit /etc/profile

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

$ sudo vim /etc/ld.so.conf.d/cuda.conf

/usr/local/cuda/lib64

$ sudo ldconfig

$ cd /usr/local/cuda-7.5/include
$ sudo cp host_config.h host_config.h.bak   #備份編譯標頭檔案
$ sudo gedit host_config.h

#if __GNUC__ > 5 || (__GNUC__ == 5 && __GNUC_MINOR__ > 9)

#error -- unsupported GNU version! gcc versions later than 4.9 are not supported!

#endif /* __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ > 9) */

$ cd /usr/local/cuda/samples
$ sudo make all -j4
$ cd /usr/local/cuda/samples/bin/x86_64/linux/release
$ sudo ./deviceQuery

BLAS,即基礎線性代數子程式庫，裡面擁有大量已經編寫好的關於線性代數運算的程式,是一個應用程式介面的標準。caffe官網上推薦了三種實現：ATLAS, MKL, or OpenBLAS。其中atlas可以直接通過命令列安裝.在此，採用intel的MKL庫。intel官網下載MKL ,該連結進入後，申請學生版本的Parallel Studio XE Cluster Edition，這裡註冊要學校的郵箱拿去註冊。該檔案有3.9GB，下載完成後切換進檔案所在目錄解壓縮該檔案。

$ cd ~/Downloads/
$ tar zxvf parallel_studio_xe_2016_update3.tgz   #解壓下載檔案
$ chmod 777 parallel_studio_xe_2016_update3 -R   #獲取檔案許可權
$ cd parallel_studio_xe_2016_update3/
$ sudo ./install_GUI.sh

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

/opt/intel/lib/intel64
/opt/intel/mkl/lib/intel64

$ sudo ldconfig

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

$ unzip opencv-3.1.0.zip
$ cd opencv-3.1.0
$ mkdir build
$ cd build
$ cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..

$ cd ~/Downloads/
$ mv ippicv_linux_20151201.tgz opencv-3.1.0/3rdparty/ippicv/downloads/linux-808b791a6eac9ed78d32a7666804320e/ 
$ vim opencv-3.1.0/CMakeLists.txt 

set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_FORCE_INLINES") 

$ cd opencv-3.1.0/build
$ cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..
$ make -j4       #四核運算
$ sudo make install

$ bash Anaconda2-4.0.0-Linux-x86_64.sh

$ sudo gedit ~/.bashrc    
export PATH="/home/jeson/anaconda2/bin:$PATH"
export LD_LIBRARY_PATH="/home/jeson/anaconda2/lib:$LD_LIBRARY_PATH"

$ ipython

$ cd matlab2014/
$ unrar x MATHWORKS_R2014A.part1.rar

$ sudo ./install

$ cd ~/matlab2014/Crack/Linux/
$ sudo cp libmwservices.so /usr/local/MATLAB/R2014a/bin/glnxa64/

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install -y build-essential cmake git pkg-config
sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install -y libatlas-base-dev
sudo apt-get install -y --no-install-recommends libboost-all-dev
sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install -y python-pip
sudo apt-get install -y python-dev
sudo apt-get install -y python-numpy python-scipy
sudo apt-get install -y libopencv-dev

cd ~
git clone https://github.com/BVLC/caffe.git    //從github上git caffe

cd caffe    //開啟到剛剛git下來的caffe
cp Makefile.config.example Makefile.config //make指令只能make Makefile.config
sudo gedit Makefile.config      //開啟Makefile.config檔案

OPENCV_VERSION := 3    #取消註釋符號#,應用opencv3
BLAS := mkl   #BLAS庫應用英特爾的mkl
#註釋第66行自帶的python
ANACONDA_HOME := /home/jeson/anaconda2  #將具體anaconda2路徑寫入
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		  $(ANACONDA_HOME)/include/python2.7 \
		  $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \   #PYTHON_INCLUDE之前的註釋#去掉，以及之後兩行前的#也去掉
PYTHON_LIB := $(ANACONDA_HOME)/lib
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial

MATLAB_DIR := /usr/local/MATLAB/R2014a

\\首先執行下面兩句話：
find . -type f -exec sed -i -e 's^"hdf5.h"^"hdf5/serial/hdf5.h"^g' -e 's^"hdf5_hl.h"^"hdf5/serial/hdf5_hl.h"^g' '{}' \;
cd /usr/lib/x86_64-linux-gnu
\\然後根據情況執行下面兩句：
sudo ln -s libhdf5_serial.so.10.1.0 libhdf5.so
sudo ln -s libhdf5_serial_hl.so.10.0.2 libhdf5_hl.so
\\注意：這裡的10.1.0和10.0.2根據不同的系統可能對應的數字會不同，比如在ubuntu16.04中其數字就是10.1.0.
\\具體的數字可以在輸入命令時候使用tab鍵補全檢視

NVCCFLAGS += -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

$ sudo gedit ～/.bashrc

export LD_LIBRARY_PATH="/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH”

sudo ldconfig #編譯立即生效

make all -j4
make test -j4
make runtest

sudo gedit ~/.bashrc
export MKL_CBWR=AUTO     #開啟的檔案末尾加入該行，然後儲存退出。
sudo ldconfig            #編譯立即生效 

make clean
make all -j4
make test -j4
make runtest

$ sudo gedit ~/.bashrc

export PYTHONPATH="/home/jeson/caffe/python:$PYTHONPATH"

sudo ldconfig
make pycaffe
make distribute
ipython
import caffe

cd /usr/local/MATLAB/R2014a/sys/os/glnxa64/
sudo mv libstdc++.so.6 libstdc++.so.6_back
sudo ldconfig
cd ~/caffe/
make matcaffe
make mattest