ubuntu16.04+gtx1080+cuda8.0+opencv3.1.0+cudnn-v5+caffe安装教程

1.本教程对应的环境

system：ubuntu-16.04-desktop-amd64.iso

cuda：cuda_8.0.44_linux-16.04.run

cudnn：cudnn-8.0-linux-x64-v5.1.tgz

caffe：https://github.com/BVLC/caffe

2.安装ubuntu-16.04

略。安装基本更新。

sudo apt-get update
sudo apt-get upgrade

3.安装cuda-8.0

3.1 安装显卡驱动

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-367

重启系统，使新驱动生效。使用nvidia-smi测试是否安装成功。

3.2 安装cuda-Toolkit

3.2.1 执行安装文件

./cuda_8.0.44_linux-16.04.run --override

安装过程如下：

Do you accept the previously read EULA? (accept/decline/quit): accept
You are attempting to install on an unsupported configuration. Do you wish to continue? ((y)es/(n)o) [ default is no ]: y
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 367.48? ((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 [ default is /home/kinghorn ]: /usr/local/cuda-8.0
Installing the CUDA Toolkit in /usr/local/cuda-8.0 ...
Finished copying samples.
===========
= Summary =
===========
Driver:   Not Selected
Toolkit:  Installed in /usr/local/cuda-8.0
Samples:  Installed in /usr/local/cuda-8.0

②设置环境变量

vi /home/xxx/.bashrc

内容如下：

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

使环境变量生效

source /home/xxx/.bashrc

③将cuda库添加到系统动态库管理器

sudo vi /etc/ld.so.conf.d/cuda.conf

添加：

/usr/local/cuda/lib64

执行ldconfig使新加的库生效

sudo ldconfig

④编译cuda例子与测试

进入到/usr/local/cuda/NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery目录执行：

sudo make
./deviceQuery

打印出类似如下信息，说明装成功

./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 2 CUDA Capable device(s)
Device 0: "GeForce GTX 1080"
CUDA Driver Version / Runtime Version          8.0 / 8.0
CUDA Capability Major/Minor version number:    6.1
Total amount of global memory:                 8110 MBytes (8504279040 bytes)
(20) Multiprocessors, (128) CUDA Cores/MP:     2560 CUDA Cores
GPU Max Clock rate:                            1772 MHz (1.77 GHz)
Memory Clock rate:                             5005 Mhz
Memory Bus Width:                              256-bit
L2 Cache Size:                                 2097152 bytes
Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
Total amount of constant memory:               65536 bytes
Total amount of shared memory per block:       49152 bytes
Total number of registers available per block: 65536
Warp size:                                     32
Maximum number of threads per multiprocessor:  2048
Maximum number of threads per block:           1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch:                          2147483647 bytes
Texture alignment:                             512 bytes
Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
Run time limit on kernels:                     Yes
Integrated GPU sharing Host Memory:            No
Support host page-locked memory mapping:       Yes
Alignment requirement for Surfaces:            Yes
Device has ECC support:                        Disabled
Device supports Unified Addressing (UVA):      Yes
Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

（3）安装cudnn-v5.1库

①解压

tar xzvf cudnn-8.0-linux-x64-v5.1.tgz

得到cuda文件夹里面含有lib64和include两个文件夹

②拷贝到cuda安装目录

sudo cp cuda/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/

拷贝后将链接删除重新建立链接，否则，拷贝是多个多个不同名字的相同文件，链接关系使用ls -l查看cudnn解压后的lib64文件夹。也可以分别拷贝每一个文件，链接文件拷贝使用cp -d命令。

4.安装opencv3.1.0

（1）解压，创建build目录

unzip opencv-3.1.0.zip
cd opencv-3.1.0
mkdir build

（2）修改opencv源码，使其兼容cuda8.0

vi opencv-3.1.0/modules/cudalegacy/src/graphcuts.cpp

修改如下：



将：

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

改为：

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

（3）配置opencv，生成Makefile

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

如果因为ippicv_linux_20151201.tgz包下载失败而导致Makefile生成失败，可通过手动下载ippicv_linux_20151201.tgz安装包，将其拷贝至

opencv-3.1.0/3rdparty/ippicv/downloads/linux-8b449a536a2157bcad08a2b9f266828b目录内，重新执行配置命令即可。

（4）编译

make -j8

编译过程中如果出现如下错误：

/usr/include/string.h: In function ‘void* __mempcpy_inline(void*, const void*, size_t)’: /usr/include/string.h:652:42: error: ‘memcpy’ was not declared in this scope return (char *) memcpy (__dest, __src, __n) + __n;

这是因为ubuntu的g++版本过高造成的，只需要在opencv-3.1.0目录下的CMakeList.txt 文件的开头加入：

set(CMAKE_CXX_FLAGS “${CMAKE_CXX_FLAGS} -D_FORCE_INLINES”)

添加之后再次进行编译即可。

（5）安装

sudo make install

（6）查看版本号

pkg-config --modversion opencv

5.安装caffe

（1）安装必要的依赖库

sudo apt-get install build-essential
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev
sudo apt-get 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 python-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

（2）解压修改配置文件

unzip caffe-master.zip
cp Makefile.config.example Makefile.config
vi Makefile.config

主要配置修改如下：

USE_CUDNN := 1
OPENCV_VERSION := 3
CUDA_DIR :=/usr/local/cuda-8.0
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
/usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial
WITH_PYTHON_LAYER := 1
USE_PKG_CONFIG := 1

（3）编译caffe

make -j8

可能遇到的错误1：src/caffe/net.cpp:8:18: fatal error: hdf5.h: No such file or directory

解决方法：

cd /usr/lib/x86_64-linux-gnu
sudo ln -s libhdf5_serial.so.10.1.0 libhdf5_serial.so
sudo ln -s libhdf5_serial_hl.so.10.0.2 libhdf5_serial_hl.so

可能遇到的错误2：error – unsupported GNU version! gcc versions later than 5.3 are not supported!

解决方法：修改/usr/local/cuda/include/host_config.h文件

#if __GNUC__ > 5 || (__GNUC__ == 5 && __GNUC_MINOR__ > 3)
#error -- unsupported GNU version! gcc versions later than 5.3 are not supported!

改为：

#if __GNUC__ > 5 || (__GNUC__ == 5 && __GNUC_MINOR__ > 4)
#error -- unsupported GNU version! gcc versions later than 5.4 are not supported!

可能遇到的错误3：

/usr/include/string.h: In function ‘void* **__mempcpy_inline(void*, const void*, size_t)’: /usr/include/string.h:652:42: error: ‘memcpy’ was not declared in this scope return (char *) memcpy (__dest, __src, __n) + __n;**

解决方法：修改caffe-master的Makefile

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

改为：

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

可能遇到的错误4:

caffe/proto/caffe.pb.h: No such file or directory

使用如下方法生成caffe.pb.h

protoc src/caffe/proto/caffe.proto --cpp_out=.
mkdir include/caffe/proto
mv src/caffe/proto/caffe.pb.h include/caffe/proto

（4）编译caffe的python接口

make pycaffe

（5）运行caffe runtest

make runtest

这里时间有点长。

6.运行手写体例程

进入到caffe根目录下，运行脚本

（1）获取数据

sh data/mnist/get_mnist.sh

（2）将标签数据转换成caffe使用的LMDB数据格式

sh examples/mnist/create_mnist.sh

（3）执行训练脚本

sh examples/mnist/train_lenet.sh

训练时间不同的显卡训练时间不同，gtx1080迭代10000次大约需要20s，最终结果如下所示：

I0716 14:46:01.360709 27985 solver.cpp:404]     Test net output #0: accuracy = 0.9908
I0716 14:46:01.360750 27985 solver.cpp:404]     Test net output #1: loss = 0.0303895 (* 1 = 0.0303895 loss)
I0716 14:46:01.360755 27985 solver.cpp:322] Optimization Done.
I0716 14:46:01.360757 27985 caffe.cpp:222] Optimization Done.

模型精度在0.99以上。至此，在ubuntu16.04系统下使用gtx1080显卡+cudnn-v5的开发环境就搭建完成了。