註釋： 由於網上很多連結並不適用本人機器，在參考他人的教程後，根據自己機器實際情況，安排瞭如下教程。

適用硬體要求：顯示卡GTX1050,1060,1070,1080系列

軟體：cuda8.0，cudnn5.1，opencv3.0

安裝過程：

安裝好ubuntu14.04之後，需要先更改驅動：系統設定-》軟體更新-》附加驅動

1. 安裝依賴項

sudo apt-get install build-essential

sudo apt-get install cmake git

sudo apt-get install freeglut3-dev libx11-dev libxmu-dev libxi-dev libglu1-mesa-dev

sudo apt-get install 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

sudo apt-get install libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev

sudo apt-get install vim

sudo apt-get install libprotobuf-dev protobuf-compiler #（protobuf 2.5.0）

2. 安裝cuda8.0

（安裝 .run格式的）

sudo sh  *.run

選擇：

Do you accept the previously read EULA?
accept/decline/quit: accept

Install NVIDIA Accelerated Graphics Driver for linux-x86_64 361.62?
(y)es/(n)o/(q)uit: n

Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y

Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]:

Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y

Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y

Enter CUDA Samples Location

設定環境變數：

ctrl+H  開啟  (主目錄/home/*/) .bashrc檔案，新增

export PATH=/usr/local/cuda-8.0/bin:$PATH  

export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH

終端執行：source ~/.bashrc

檢查：nvcc --version

3. 安裝cudnn

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

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

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

sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

4. 安裝BLAS

sudo apt-get install libatlas-base-dev

5. opencv 3.0 安裝（包括3.1 3.2）

ippicv下載連結（百度雲）：連結：https://pan.baidu.com/s/1gZBQskfb4XYrfVA3MmrvAQ 密碼：d9oj

修改/opencv-3.0.0/modules/cudalegacy/src/graphcuts.cpp

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)

改成

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || (CUDART_VERSION >= 8000)

sudo apt-get install build-essential cmake libgtk2.0-dev pkg-config python-dev python-numpy libavcodec-dev libavformat-dev libswscale-dev  

cd opencv3.0下：

mkdir build

cd build

cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..  

然後終止，將ippicv壓縮包複製到opencv-3.0.0/3rdparty/ippicv/downloads/linux-8b449a536a2157bcad08a2b9f266828b/下面

重新：

cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..  

sudo make install 

查詢opencv版本：

Pkg-config --modversion opencv

附：

sudo apt-get install -y python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags cython

6. caffe的編譯

下載caffe，caffe路徑下，複製Makefile.config.example 重新命名Makefile.config到當前路徑。修改路徑：

## 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/R2014a

# 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

# 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 ?= @

編譯caffe:

cd caffe（caffe檔案）

(命令 為終端執行)

mkdir build

cd build

make all -j16

make test -j16

make runtest -j16

make pycaffe

注：import caffe時，需要

import sys

sys.path.append(r'caffe下python檔案的路徑')

import caffe

或者新增pycaffe的環境變數：

開啟 .bashrc

export PYTHONPATH=/home/*/caffe/python:$PYTHONPATH

終端執行 source ~/.bashrc

測試caffe安裝成功？：

轉到 Caffe 目錄下

sh data/mnist/get_mnist.sh

sh examples/mnist/create_mnist.sh

sh examples/mnist/train_lenet.sh