Ubuntu16.04 安装 CUDA、CUDNN、OpenCV 并用 Anaconda 配置 Tensorflow 和 Caffe 详细过程（此种方案不好，好的方案是另一篇，基于pyhton2的）

Ubuntu16.04 安装 CUDA、CUDNN、OpenCV 和 Caffe 详细过程（基于Python2，没有anaconda2和3，因为基于anaconda2和3在安装caffe时会报错，很难解决）

详细安装教程：https://blog.csdn.net/zhuiqiuzhuoyue583/article/details/88756053

 

下面方法不好，会出现很多问题，不容易解决。

1.安装驱动

找到“System Settings”，在找到“Details”。

查看驱动，我这里安装后显示的。

 

安装NVIDIA驱动：

在“System Settings”找到“Software & Updates ”

再找到“Additional Drivers”，系统会自动搜索可用的 NVIDIA驱动，选择 “NVIDIA”驱动即可。

再去“System Settings”，在找到“Details”，查看驱动：

 

2.安装相关依赖项

安装后续步骤或环境必需的依赖包，依次输入以下命令:

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler 

sudo apt-get install --no-install-recommends libboost-all-dev 

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

sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev 

sudo apt-get install git cmake build-essential

有一定几率安装失败而导致后续步骤出现问题，所以要确保以上依赖包都已安装成功，验证方法就是重新运行安装命令，如验证 git cmake build-essential是否安装成功共则再次运行以下命令：

sudo apt-get install git cmake build-essential 

界面提示如下则说明已成功安装依赖包，否则继续安装直到安装成功。

yhao@yhao-X550VB:~$ sudo apt-get install git cmake build-essential 
正在读取软件包列表... 完成 
正在分析软件包的依赖关系树 正在读取状态信息... 完成 
build-essential 已经是最新版 (12.1ubuntu2)。 
cmake 已经是最新版 (3.5.1-1ubuntu3)。 
git 已经是最新版 (1:2.7.4-0ubuntu1.1)。 
下列软件包是自动安装的并且现在不需要了： 
lib32gcc1 libc6-i386 使用'sudo apt autoremove'来卸载它(它们)。 
升级了 0 个软件包，新安装了 0 个软件包，要卸载 0 个软件包，有 94 个软件包未被升级。

3.安装CUDA

CUDA是NVIDIA的编程语言平台，想使用GPU就必须要使用cuda。
（1）下载CUDA8.0

首先在官网上(https://developer.nvidia.com/cuda-downloads)下载CUDA，选择自己合适的版本。
该链接界面只显示最新版本。若需要下载以前的版本，可在页面下方点击Legacy Releases，选择自己需要的其他版本，这里安装的是cuda8.0。
这里写图片描述

在Windows中使用迅雷下载，然后再Ubuntu中使用，这样比较快。

图2.CUDA下载
（2）安装CUDA

下载完成后执行以下命令：
 

sudo chmod 777 cuda_8.0.44_linux.run 

sudo ./cuda_8.0.44_linux.run

（注意：执行后会先出现一个声明，需要阅读到100%，同意声明后才会开始安装。）
安装时首先会有一系列提示让你确认，但是注意，有个让你选择是否安装nvidia361驱动时，一定要选择否：

Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?

因为前面我们已经安装了更加新的nvidia384，所以这里不要选择安装。其余的都直接默认或者选择是即可。

可能出现的错误：
安装cuda时可能有下面的信息：

Installing the CUDA Toolkit in /usr/local/cuda-8.0 … 
Missing recommended library: libGLU.so 
Missing recommended library: libX11.so 
Missing recommended library: libXi.so 
Missing recommended library: libXmu.so

原因是缺少相关的依赖库,安装相应库就解决了：

sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev

再次安装,就不再提示了。

(3)环境变量配置

打开~/.bashrc文件： sudo gedit ~/.bashrc 将以下内容写入到~/.bashrc尾部：

export CUDA_HOME=/usr/local/cuda-8.0
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/CUPTI/lib64:$LD_LIBRARY_PATHs
export PATH=/usr/local/cuda-8.0/bin:$PATH
export LD_LIBRARY_PATH="/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/CUPTI/lib64:/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/lib64"

刷新：

source ~/.bashrc

(4)配置cuDNN

注意：首次配置cuDNN时我是下载的cuDNN6.0版本，但是后来在编译fast rcnn及SSD时发现有很多问题是由于cuDNN版本不匹配引起的，因此后来又手动删除掉了cuDNN6.0的include和lib文件，重新下载cuDNN5.1版本并重新编译caffe。

cuDNN是GPU加速计算深层神经网络的库。 首先去官网https://developer.nvidia.com/rdp/cudnn-download 下载cuDNN，需要注册一个账号才能下载。

注册后的下载网址：https://developer.nvidia.com/rdp/cudnn-archive

在Windows中使用迅雷下载，然后再Ubuntu中使用，这样比较快。

图3.cuDNN下载

下载cuDNN5.1 之后进行解压：

sudo tar -zxvf ./cudnn-8.0-linux-x64-v5.1.tgz

进入cuDNN5.1 解压之后的include目录，在命令行进行如下操作：

cd cuda/include
sudo cp cudnn.h /usr/local/cuda/include  #复制头文件

再进入lib64目录下的动态文件进行复制和链接：

（这里的libcudnn.so.5.1.10是固有文件，而libcudnn.so.5是libcudnn.so.5.1.10链接得到的动态文件，libcudnn.so是libcudnn.so.5链接得到的动态文件。）

cd ..
cd lib64 
sudo cp lib* /usr/local/cuda/lib64/   #复制动态链接库

cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.5   #删除原有动态文件

sudo ln -s libcudnn.so.5.1.10 libcudnn.so.5 
#生成软衔接（注意这里要和自己下载的cudnn版本对应，可以在/usr/local/cuda/lib64下查看自己libcudnn的版本） 

sudo ln -s libcudnn.so.5 libcudnn.so #生成软链接

这里需要注意上面这个命令，网上有人的第三行命令为：

sudo ln -s libcudnn.so.5.1.5 libcudnn.so.5 #生成软衔接

起初我执行的也是上条链接 libcudnn.so.5.1.5 的命令，但是后面编译caffe时出错，报错内容为 /usr/bin/ld: 找不到 -lcudnn，所以这里需要先查看一下自己应该链接的是 libcudnn.so.5.1.10 还是 libcudnn.so.5.1.5 ，查看方法为下：
 

locate libcudnn.so

我执行完后显示如下：

yhao@yhao-X550VB:~$ locate libcudnn.so 
/home/yhao/.local/share/Trash/files/libcudnn.so 
/home/yhao/.local/share/Trash/files/libcudnn.so.5 
/home/yhao/.local/share/Trash/files/libcudnn.so.5.1.10 
/home/yhao/.local/share/Trash/files/cuda/lib64/libcudnn.so 
/home/yhao/.local/share/Trash/files/cuda/lib64/libcudnn.so.5 
/home/yhao/.local/share/Trash/files/cuda/lib64/libcudnn.so.5.1.10 
/home/yhao/.local/share/Trash/info/libcudnn.so.5.1.10.trashinfo 
/home/yhao/.local/share/Trash/info/libcudnn.so.5.trashinfo 
/home/yhao/.local/share/Trash/info/libcudnn.so.trashinfo 
/home/yhao/cuda/lib64/libcudnn.so 
/home/yhao/cuda/lib64/libcudnn.so.5 
/home/yhao/cuda/lib64/libcudnn.so.5.1.10 
/usr/local/lib/libcudnn.so 
/usr/local/lib/libcudnn.so.5

可以看到我的文件是 libcudnn.so.5.1.10 ，并没有 libcudnn.so.5.1.5，所以第三行命令我链接的是 libcudnn.so.5.1.10 ，这里第三行链接命令视你的查看结果而定。

注意：下面这个步骤不能缺少！！否则可能链接失败！

执行

sudo ldconfig -v  

#必须在/usr/local/cuda/lib64/目录下执行，否则可能会报
libcudnn.so.5: cannot open shared object file: 
No such file or directory

或者

sudo ldconfig /usr/local/cuda/lib64

至此，cudnn就配置完成了。

安装完成后可用 nvcc -V 命令验证是否安装成功，若出现以下信息则表示安装成功：

yhao@yhao-X550VB:~$ nvcc -V 
nvcc: NVIDIA (R) Cuda compiler driver 
Copyright (c) 2005-2016 NVIDIA 
Corporation Built on Tue_Jan_10_13:22:03_CST_2017 
Cuda compilation tools, release 8.0, V8.0.61

 

(5)测试CUDA的samples

cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery 
sudo make 
sudo ./deviceQuery

 如果显示一些关于GPU的信息，则说明安装成功。

例二：

cd /usr/local/cuda/samples/5_Simulations/fluidsGL
sudo make clean && sudo make
./fluidsGL

自带例子测试通过，Cuna8.0安装配置完成！ 

参考：

       https://blog.csdn.net/jiangyanting2011/article/details/78873113#commentBox

       https://blog.csdn.net/CAU_Ayao/article/details/80578600#commentBox

       https://blog.csdn.net/yhaolpz/article/details/71375762

 

4.安装OpenCV，这里是在安装anaconda3后，安装的opencv，主要在cmake时，将opencv配置到anaconda3中。

事实证明使用conda便捷安装的opencv是阉割版，不能实现视频和摄像头的读取功能，所以需要自己手动编译。

（1）下载opencv，我在window中用迅雷下载，下载后放到Ubuntu中，这样比较快。

           https://opencv.org/releases.html

           解压后，放在软件安装目录下，我是放在了“/home/zqzy”目录下了

         

（2）更新，否则可能会面会报错

sudo apt-get update
sudo apt-get upgrade

（3）安装依赖

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
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev
sudo apt-get install libgtk-3-dev
sudo apt-get install libatlas-base-dev gfortran pylint
sudo apt-get install python2.7-dev python3.5-dev
sudo apt-getinstallbuild-essential  
sudo apt-getinstall cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev  
sudo apt-getinstall python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev # 处理图像所需的包  
sudo apt-getinstall libavcodec-dev libavformat-dev libswscale-dev libv4l-dev liblapacke-dev
sudo apt-getinstall libxvidcore-dev libx264-dev # 处理视频所需的包  
sudo apt-getinstall libatlas-base-dev gfortran # 优化opencv功能
sudo apt-getinstall ffmpeg 

（4）执行如下命令：

cd  /home/zqzy/opencv-3.3.0
mkdir build
cd build

（5）cmake，这里根据自己的安装目录进行修改

针对anaconda3，进行安装

/*执行时的命令*/

cmake -D WITH_IPP=OFF -D PYTHON_DEFAULT_EXECUTABLE=/home/zqzy/anaconda3/bin/python3 -D BUILD_opencv_python3=ON -D BUILD_opencv_python2=OFF -D PYTHON3_EXCUTABLE=/home/zqzy/anaconda3/bin/python3 -D PYTHON3_INCLUDE_DIR=/home/zqzy/anaconda3/include/python3.6m -D PYTHON3_LIBRARY=/home/zqzy/anaconda3/lib/libpython3.6m.so.1.0 -D PYTHON_NUMPY_PATH=/home/zqzy/anaconda3/lib/python3.6/site-packages ..

/* 上面的命令，如下，这样好阅读，但是还是用上面的命令执行

cmake 
-D WITH_IPP=OFF 
-D PYTHON_DEFAULT_EXECUTABLE=/home/zqzy/anaconda3/bin/python3 
-D BUILD_opencv_python3=ON 
-D BUILD_opencv_python2=OFF 
-D PYTHON3_EXCUTABLE=/home/zqzy/anaconda3/bin/python3 
-D PYTHON3_INCLUDE_DIR=/home/zqzy/anaconda3/include/python3.6m 
-D PYTHON3_LIBRARY=/home/zqzy/anaconda3/lib/libpython3.6m.so.1.0 
-D PYTHON_NUMPY_PATH=/home/zqzy/anaconda3/lib/python3.6/site-packages 
..

*/

 

针对anaconda2，进行安装

cmake -D WITH_IPP=OFF -D PYTHON_DEFAULT_EXECUTABLE=/home/zqzy/anaconda2/bin/python2 -D BUILD_opencv_python3=OFF -D BUILD_opencv_python2=ON -D PYTHON2_EXCUTABLE=/home/zqzy/anaconda2/bin/python -D PYTHON2_INCLUDE_DIR=/home/zqzy/anaconda2/include/python2.7 -D PYTHON2_LIBRARY=/home/zqzy/anaconda2/lib/libpython2.7.so.1.0 -D PYTHON_NUMPY_PATH=/home/zqzy/anaconda2/lib/python2.7/site-packages ..

/*上面的命令，如下，这样好阅读，但是还是用上面的命令执行

cmake -D WITH_IPP=OFF 
-D PYTHON_DEFAULT_EXECUTABLE=/home/zqzy/anaconda2/bin/python2 
-D BUILD_opencv_python3=OFF 
-D BUILD_opencv_python2=ON 
-D PYTHON2_EXCUTABLE=/home/zqzy/anaconda2/bin/python 
-D PYTHON2_INCLUDE_DIR=/home/zqzy/anaconda2/include/python2.7 
-D PYTHON2_LIBRARY=/home/zqzy/anaconda2/lib/libpython2.7.so.1.0 
-D PYTHON_NUMPY_PATH=/home/zqzy/anaconda2/lib/python2.7/site-packages 

..

*/

 

（6）接下来一步该make了，我用的是，这步时间比较久，耐心等等，如果这步没有报错，那么就离成功不远了.

make -j8

（7）接下来再用几句简单命令即可完成安装：

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

  在打开的写字板中输入：

/usr/local/lib

  在终端输入：

sudo ldconfig

  最后设置路径：

sudo gedit /etc/bash.bashrc

 文件末尾添加并保存：

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

export PKG_CONFIG_PATH

测试

终端输入python，import cv2，报错 NO module in cv2，应该是找不到cv2.so文件。

针对anaconda3，进行安装：

解决方法：

去opencv-3.3.0/build/lib/python3这个路径下找到cv2.cpython-36m-x86_64-linux-gnu.so文件，

然后复制到/Software/anaconda3/lib/python3.6/site-packages文件夹下。

cp /home/zqzy/opencv-3.3.0/build/lib/python3/cv2.cpython-36m-x86_64-linux-gnu.so /home/zqzy/anaconda3/lib/python3.6/site-packages

在终端输入python，import cv2，没有报错。

 

针对anaconda2，进行安装：

解决方法：

去opencv-3.3.0/build/lib这个路径下找到cv2.so文件，

然后复制到/Software/anaconda2/lib/python2.7/site-packages文件夹下。

cp /home/zqzy/opencv-3.3.0-py2/build/lib/cv2.so /home/zqzy/anaconda2/lib/python2.7/site-packages

在终端输入python，import cv2，没有报错。

如果，复制完之后又报错：
ImportError: /home/wz/Software/anaconda3/bin/../lib/libstdc++.so.6: version `GLIBCXX_3.4.21’ not found (required by /home/wz/Software/anaconda3/lib/python3.6/site-packages/cv2.cpython-36m-x86_64-linux-gnu.so)
原因：gcc库版本太老
解决方案： conda install libgcc

应该装好了吧^-^~~
 

附上测试摄像头的代码

# 打开摄像头并灰度化显示
import cv2

capture = cv2.VideoCapture(0)

while(True):
    # 获取一帧
    ret, frame = capture.read()
    # 将这帧转换为灰度图
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    cv2.imshow('frame', gray)
    if cv2.waitKey(1) == ord('q'):
        break
capture.release()
cv2.destroyAllWindows()

参考教程：https://blog.csdn.net/aaon22357/article/details/81913465

                   https://opencv.org/releases.html

5.安装caffe，这里是在安装anaconda3后，安装的caffe，主要在安装时，将caffe配置到anaconda3中。

（1）安装依赖

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler 
sudo apt-get install --no-install-recommends libboost-all-dev 
sudo apt-get install libatlas-base-dev 
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install build-essential 
sudo apt-get install python-dev 

（2）下载caffe

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

（3）配置Makefile.config文件之前，先复制几个文件，有的没有“libpython3.6m”这个文件

     针对anaconda3，有的anaconda3没有libpython3.6m*，就不需要执行这句话。

 sudo cp ~/anconda3/bin/libpython3.6m*  /usr/lib/x86_64-linux-gnu

     针对 anaconda2，有的anaconda2也没有libpython2.7m*，就不需要执行这句话。

（4）将Makefile.config.example的内容复制到Makefile.config

sudo cp Makefile.config.example Makefile.config   

（5）修改Makefile.config文件

sudo gedit Makefile.config #打开Makefile.config文件

（6）修改Makefile.config文件

针对anaconda3，修改Makefile.config文件

a.使用 cudnn 
USE_CUDNN := 1 

b.使用的 opencv 版本是 3 
OPENCV_VERSION := 3 

c.使用 python 来编写 
WITH_PYTHON_LAYER := 1 

d.重要的一项 : 
将# Whatever else you find you need goes here.下面的 
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib 
修改为： 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
这是因为ubuntu16.04的文件包含位置发生了变化，尤其是需要用到的hdf5的位置，所以需要更改这一路径 

e.删除 CUDA_ARCH := 的前两行,避免 CUDA 报错 


f. 修改 PYTHON_INCLUDE 路径，将其注释即可
# 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 

g. 修改 Anaconda 路径 
# Verify anaconda location, sometimes it's in root. 
ANACONDA_HOME := /home/zqzy/anaconda3           #改成自己anaconda的路径

#注意这里的 \，书写方式是“空一个 空格，然后回车，下一行使用Tab建进行空格”
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \    
                $(ANACONDA_HOME)/include/python3.6m \
                $(ANACONDA_HOME)/lib/python3.6/site-packages/numpy/core/include
# 注意这里的空格是使用“Tab”键来写的

h. 使用 Python3 
# Uncomment to use Python 3 (default is Python 2) 
PYTHON_LIBRARIES := boost_python3 python3.6m 

#注意这里的 \，书写方式是“空一个 空格，然后回车，下一行使用Tab建进行空格”
PYTHON_INCLUDE := /usr/include/python3.6m \
                /usr/lib/python3.6/dist-packages/numpy/core/include \
                /home/senius/anaconda3/include/python3.6m 
# 注意这里的空格是使用“Tab”键来写的

i. 修改 PYTHON_LIB 路径 
# We need to be able to find libpythonX.X.so or .dylib. 
#PYTHON_LIB := /usr/lib 

#注意这里的 \，书写方式是“空一个 空格，然后回车，下一行使用Tab建进行空格”
PYTHON_LIB := $(ANACONDA_HOME)/lib \
              $(ANACONDA_HOME)/pkgs/python-3.6.4-hc3d631a_1/lib
# 注意这里的空格是使用“Tab”键来写的

j. 紧接着上面这个PYTHON_LIB，在其下面添加
LINKFLAGS := -Wl,-rpath,/home/zqzy/anaconda3/lib #要添加这句话，否则报错

以上内容一定要仔细查看自己相应的目录一一对应，若没有相关目录一定是少安装了某些依赖，这一步配置好了后面就不会有什么错误

以上内容一定要仔细查看自己相应的目录一一对应，有的可能不太一样，需要自己去相应的目录查看，若没有相关目录一定是少安装了某些依赖，这一步配置好了后面就不会有什么错误

这里附上我修改后的针对anaconda3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 := 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 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
CUDA_ARCH := -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/zqzy/anaconda3
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		  $(ANACONDA_HOME)/include/python3.6m \
		  $(ANACONDA_HOME)/lib/python3.6/site-packages/numpy/core/include

# Uncomment to use Python 3 (default is Python 2)
PYTHON_LIBRARIES := boost_python3 python3.6m
PYTHON_INCLUDE := /usr/include/python3.6m \
                 /usr/lib/python3.6/dist-packages/numpy/core/include \
                 /home/zqzy/anaconda3/include/python3.6m

# We need to be able to find libpythonX.X.so or .dylib.
#PYTHON_LIB := /usr/lib
PYTHON_LIB := $(ANACONDA_HOME)/lib \
              $(ANACONDA_HOME)/pkgs/python-3.6.4-hc3d631a_1/lib

LINKFLAGS := -Wl,-rpath,/home/zqzy/anaconda3/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

# 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 ?= @

针对anaconda2，修改Makefile.config文件

a.使用 cudnn 
USE_CUDNN := 1 

b.使用的 opencv 版本是 3 
OPENCV_VERSION := 3 

c.使用 python 来编写 
WITH_PYTHON_LAYER := 1 

d.重要的一项 : 
将# Whatever else you find you need goes here.下面的 
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib 
修改为： 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
这是因为ubuntu16.04的文件包含位置发生了变化，尤其是需要用到的hdf5的位置，所以需要更改这一路径 

e.删除 CUDA_ARCH := 的前两行,避免 CUDA 报错 


f. 修改 PYTHON_INCLUDE 路径，将其注释即可
# 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 

g. 修改 Anaconda 路径 
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
ANACONDA_HOME := /home/zqzy/anaconda2    #取消注释，并且修改为自己anaconda的安装路径
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \     #取消注释
         $(ANACONDA_HOME)/include/python2.7 \    #取消注释，注意这里空格是Tab键
         $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include   ##取消注释

# 注意这里的空格是使用“Tab”键来写的


h. 修改 PYTHON_LIB 路径 
# We need to be able to find libpythonX.X.so or .dylib. 
#PYTHON_LIB := /usr/lib 

#注意这里的 \，书写方式是“空一个 空格，然后回车，下一行使用Tab建进行空格”
PYTHON_LIB := $(ANACONDA_HOME)/lib \
              $(ANACONDA_HOME)/pkgs/python-2.7.14-h1571d57_29/lib
# 注意这里的空格是使用“Tab”键来写的

i. 紧接着上面这个PYTHON_LIB，在其下面添加
LINKFLAGS := -Wl,-rpath,/home/zqzy/anaconda2/lib #要添加这句话，否则报错

以上内容一定要仔细查看自己相应的目录一一对应，若没有相关目录一定是少安装了某些依赖，这一步配置好了后面就不会有什么错误

以上内容一定要仔细查看自己相应的目录一一对应，有的可能不太一样，需要自己去相应的目录查看，若没有相关目录一定是少安装了某些依赖，这一步配置好了后面就不会有什么错误

这里附上我修改后的针对anaconda2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 := 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 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
CUDA_ARCH := -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/zqzy/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 \
	$(ANACONDA_HOME)/pkgs/python-2.7.14-h1571d57_29/lib

LINKFLAGS := -Wl,-rpath,/home/zqzy/anaconda2/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

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

# 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 ?= @

（6）修改 Makefile 文件把下面第一行代码改为第二行代码：

将：LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5 
替换为：LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial opencv_core opencv_imgproc opencv_imgcodecs opencv_highgui

 （7）修改 Makefile 文件把下面第一行代码改为第二行代码：

将： NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS) 
替换为： NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

（8）修改 Makefile 文件把下面第一段代码改为第二段代码：

将：

# Debugging
ifeq ($(DEBUG), 1)
        COMMON_FLAGS += -DDEBUG -g -O0
        NVCCFLAGS += -G
else
        COMMON_FLAGS += -DNDEBUG -O2
endif

修改为：

# Debugging
ifeq ($(DEBUG), 1)
        COMMON_FLAGS += -DDEBUG -g -O0
        NVCCFLAGS += -G
else
        COMMON_FLAGS += -DNDEBUG -O2
        NVCCFLAGS += -G
endif

# 注意这里的空格是使用“Tab”键来写的。

也就是加一句 NVCCFLAGS += -G

（9）针对anaconda3

           使用“locate  libpython3.6m.so.1.0” 查找 “libpython3.6m.so.1.0 ” 的位置，查找出来是在anaconda3/lib中

执行如下命令：

sudo gedit  /etc/ld.so.conf

/home/zqzy/anaconda3/lib/

sudo ldconfig

针对anaconda2

  使用“locate  libpython2.7.so.1.0” 查找 “libpython2.7.so.1.0 ” 的位置，查找出来是在/usr/lib/x86_64-linux-gnu/中

执行如下命令：

执行命令：
sudo gedit  /etc/ld.so.conf

添加内容：

/usr/lib/x86_64-linux-gnu/

执行命令：
sudo ldconfig

（10）编辑 /usr/local/cuda/include/host_config.h ，将其中的第115行注释掉

//#error -- unsupported GNU version! gcc versions later than 5 are not supported! 

（11）这里只针对anaconda3，对于anaconda2不需要

            因为在配置文件的第 74 行中有 PYTHON_LIBRARIES := boost_python3 ，但是在系统中无法找到boost_python3.lib 这个库文件。

解决方案如下：

检查是否有如下文件：

ls /usr/lib/x86_64-linux-gnu/libboost_python-py35.so

这里如果有，说明我们的系统中已经有了这种库文件，只是文件名不同。

添加软链接

cd /usr/lib/x86_64-linux-gnu/
sudo ln -s libboost_python-py35.so libboost_python3.so
sudo ln -s libboost_python-py35.a libboost_python3.a 
sudo ln -s libboost_python-py35.so.1.58.0 libboost_python3.so.1.58.0 

然后 sudo gedit /etc/ld.so.conf 

添加 /usr/lib/x86_64-linux-gnu 

sudo ldconfig

针对anaconda2，不需要第11这一步，因为没有在配置文件的第 74 行中添加 PYTHON_LIBRARIES := boost_python3

（12）将cuda信息，复制到“/usr/local/lib”的相应路径下

sudo cp /usr/local/cuda-8.0/lib64/libcudart.so.8.0 /usr/local/lib/libcudart.so.8.0 && sudo ldconfig
sudo cp /usr/local/cuda-8.0/lib64/libcublas.so.8.0 /usr/local/lib/libcublas.so.8.0 && sudo ldconfig  
sudo cp /usr/local/cuda-8.0/lib64/libcurand.so.8.0 /usr/local/lib/libcurand.so.8.0 && sudo ldconfig 
sudo cp /usr/local/cuda-8.0/lib64/libcudnn.so.5 /usr/local/lib/libcudnn.so.5 && sudo ldconfig 
#这里根据自己编译运行时的提示错误，进行相应的拷贝，有的是libcudnn.so.5 ，有的是libcudnn.so.6

（13）编译

cd .. #到caffe-master目录
sudo make all -j8
sudo make pycaffe -j8

（14）添加环境变量

sudo gedit ~/.bashrc
最后一行添加export PYTHONPATH=/home/zqzy/caffe-master/python:$PYTHONPATH
source ~/.bashrc

（15）安装protobuf

先修改权限，否则安装不了：

sudo chown -R zqzy:zqzy /home/zqzy/anaconda3

#这里的zqzy是我的用户名

在Anaconda终端搜索protobuf库，如图：

然后安装protobuf（选择你对应的版本，我这里是python3.6，所以安装protobuf是3.2.0），如图：

 

（16）测试

sudo make runtest -j8

（16）在Python中导入，没有错误，即可

import caffe

参考：

https://blog.csdn.net/yhaolpz/article/details/71375762#t8

https://blog.csdn.net/u012675539/article/details/51351553

https://blog.csdn.net/seniusen/article/details/78474929#commentBox

https://blog.csdn.net/ymshan92/article/details/80847564

https://blog.csdn.net/luteresa/article/details/79901991#commentBox

https://blog.csdn.net/weixin_40824648/article/details/80265943#t6

https://blog.csdn.net/qq_38451119/article/details/81126692

https://blog.csdn.net/hyl999/article/details/79712407

https://blog.csdn.net/ling_xiobai/article/details/78659981