关于caffe的安装，这里首先要安装Ubuntu16.04,博客上有很多教程。推介三点：1.别在虚拟机上装，推荐双系统安装。2.采用刻盘安装，关于刻盘安装方法，网上都有教程。由于每个人的电脑版本型号不一样，在安装的过程中同一问题会出现不一样的操作，这方面大家有问题的可以在下面评论，lz尽量帮忙解答。3.在安装过程中，要分盘，我介意最少200G的空间，因为大部分是用来搞深度学习的，数据采集需要很大的空间。

好了，言归正传，开始安装。

Cuda的安装：这里参照keras中文文档里Cuda的安装

打开终端输入：

#系统升级（目的：建议换源，我这里用的阿里的源，相对速度快一点）

sudo apt update

sudo apt upgrade

#安装python基础开发包

sudo apt install -y python-dev python-pip python-nose gcc g++ git gfortran vim

#安装运算加速库，打开终端输入

sudo apt install -y libopenblas-dev liblapack-dev libatlas-base-dev

打开到下载了cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb的目录，注意这里下载的是deb格式，比run格式操作要简单方便很多，也不需要自己下载安装nvidia的驱动。在目录下打开终端输入

sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb

sudo apt update

sudo apt -y install cuda

将CUDA路径添加至环境变量 在终端输入：

sudo gedit /etc/profile

在profile文件中添加：

export CUDA_HOME=/usr/local/cuda-8.0

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

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

之后

source /etc/profile

测试 在终端输入：

nvcc -V

会得到相应的nvcc编译器相应的信息，那么CUDA配置成功了。(记得重启系统)

cudnn的安装：（该步装不装差别不是很大，嫌麻烦可以不用安装

采用5.1版本，即是cudnn-8.0-win-x64-v5.1-prod.zip。 下载解压出来是名为cuda的文件夹，里面有bin、include、lib，将三个文件夹复制到安装CUDA的地方覆盖对应文件夹，在终端中输入：

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

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

cd /usr/local/cuda/lib64

sudo ln -sf libcudnn.so.5.1.10 libcudnn.so.5

sudo ln -sf libcudnn.so.5 libcudnn.so

sudo ldconfig -v

Opencv2.4.13的安装：

安装相关依赖项：

>>>sudo apt-get install build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev libdc1394-22-dev libatlas-base-dev gfortran

可能会出现部分依赖无法安装依赖的情况，可能是ubuntu版本过高(本文为ubuntu16.04)

安装cmake-gui：

sudo apt-get install cmake-qt-gui

打开到下载了opencv-2.4.13.zip的目录，建议安装2.4.13在目录下打开终端输入：

unzip opencv-2.4.13.zip

cd opencv-2.4.13/

mkdir opencv-debug

cd opencv-debug/

cmake-gui

在如下的界面下选择源码目录位置,debug目录,再configure ,generate。没有报错即可。

                                              Figure1 Cmake界面

编译安装：

在debug目录下：

make -j8

要是中途出现了一些问题是与cuda有关的，打开opencv下面那个cmakelist文件把with_cuda设置为OFF，之后再cmake，再编译。之后：

sudo make install

环境变量配置：

安装成功后还需要设置opencv的环境变量,打开文件

sudo gedit /etc/profile  

在最后一行输入如下内容：

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

执行下列命令, 使环境变量立即生效

source /etc/profile

设置lib库路径，打开文件

sudo gedit /etc/ld.so.conf.d/opencv.conf

opencv的库一般安装在’/usr/local/lib’文件夹下，在文件内添加

/usr/local/lib

执行下列命令使之立刻生效

sudo ldconfig

经过上面的流程，这样就可以在eclipse里或者qtcreator里用opencv了。 不过要配置号相应的路径和lib文件。

验证是否安装成功

对opencv的sample进行编译并运行，依次执行如下代码

cd opencv-2.4.13/samples/c

./build_all.sh

./facedetect --cascade="/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml" --scale=1.5 lena.jpg

Caffe的安装：

首先安装各种依赖包，命令行执行入下命令：

sudo apt-get update

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

将终端cd到想要安装caffe的位置，从github上clone caffe，执行如下指令：

git clone https://github.com/BVLC/caffe.git

从github上git caffe

cd caffe

打开到刚刚git下来的caffe

sudo cp Makefile.config.example Makefile.config

将Makefile.config.example的内容复制到Makefile.config

sudo gedit Makefile.config

打开Makefile.config文件，进行编辑

以下列出以编辑好的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 := 2

# 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 through *_61 lines for compatibility.

# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.

# For CUDA >= 9.0, comment the *_20 and *_21 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_52,code=sm_52 \

-gencode arch=compute_60,code=sm_60 \

-gencode arch=compute_61,code=sm_61 \

-gencode arch=compute_61,code=compute_61

# 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)/anaconda3

# 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 /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

# 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

# NCCL acceleration switch (uncomment to build with NCCL)

# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)

# USE_NCCL := 1

# Uucomment 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 ?= @

修改的重点我都已经标记出来，其余的要改的路径啥的根据自己的安装修改即可，这个网上教程也挺全的。

配置完成Makefile.config文件以后，保存并执行以下命令：

make all -j8  

make runtest -j8  

make pycaffe -j8  

make matcaffe -j8  

如果make runtest –j8 指令能够顺利通过，表明caffe已基本配置完成，接下来只需检查python和matlab接口是否能用。

最终查看python接口是否编译成功：进入python环境，进行import操作：

# python

>>> import caffe

如果没有提示错误，则编译成功。