tar -zxvf cudnn-7.5-linux-x64-v5.1-rc.tgz

cd cuda

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

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

cd /usr/local/cuda/lib64/

sudo chmod +r libcudnn.so.5.1.3

sudo ln -sf libcudnn.so.5.1.3 libcudnn.so.5

sudo ln -sf libcudnn.so.5 libcudnn.so

sudo ldconfig

gcc --version

sudo add-apt-repository ppa:ubuntu-toolchain-r/test

sudo apt-get update

sudo apt-get install gcc-4.9

sudo apt-get install g++-4.9

sudo su

cd ../../usr/bin

ln -s /usr/bin/g++-4.9 /usr/bin/g++ -f

ln -s /usr/bin/gcc-4.9 /usr/bin/gcc -f

　$ unzip opencv-3.0.0-beta.zip

　　$ cd opencv-3.0.0-beta

　　$ mkdir release

　　$ cd release

　　$ cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_TIFF=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON

　　　　　　 -D WITH_OPENGL=ON ..        //注意CMakeList.txt在上一层文件夹

　　$ make -j $(nproc)            // make -j 多核处理器进行编译(默认的make只用一核，很慢)，$(nproc)返回自己机器的核数

　　$ make install                 //把编译结果安装到 /usr/local的 lib/ 和 include/下面

git clone --recursive https://github.ocm/BVLC/caffe

cd caffe/python

for req in $(cat requirements.txt); do pip install $req; done

cd ../

cp Makefile.config.example Makefile.config

## 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 := atlas

# 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)/anaconda2

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

# N.B. both build and distribute dirs are cleared on `make clean`

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 ?= @

make all -j8

make test

make runtest

make pycaffe

#make matcaffe #when you need it