Ubuntu16.04+CUDA8.0+Caffe配置指南

本文旨在Ubuntu16.04下的caffe环境搭建。

显卡：TESLA K80

一、系统安装

这里与大多系统安装步骤无异，因而略过。大抵的一些注意事项是

1.时区选择的时候需要断网；

2.大陆地区的需要换源

3.apt-get update

选择的镜像：Ubuntu16.04-desktop

二、驱动安装

之前安装Ubuntu14.04下的驱动时，都是直接用CUDA安装包里自带的显卡驱动。但在当前目标配置下，安装驱动的时候会报couldn't locate the kernel files之类的错误。反复无法解决。遂选择手动安装显卡驱动。

在NVIDIA官网下载对应的驱动版本。这里选择用runfile安装。

1.禁用nouveau

vi /etc/modprobe.d/blacklist-nouveau.conf

加入文本

blacklist nouveau

options nouveau modeset=0

更新内核

update-initramfs -u

重启之后，查看nouveau是否禁用成功

lsmod | grep nouveau

2.禁用x server

如是lightdm，则如下命令，其他以此类推

service lightdm stop

如果该命令之后x server没有成功关闭，可尝试

pkill x

3.安装驱动

注意不要安装opengl libs（否则桌面环境不正常）

chmod a+x NVIDIA.run

./NVIDIA.run —no-opengl-files

调用以下命令，若显示显卡信息，则安装驱动成功。

nvidia-smi

三、CUDA8.0

CUDA历史版本，这里选择CUDA Toolkit 8.0 GA2。

同样别安装opengl libs，其余选项皆为默认或yes

chmod a+x cuda.run

./cuda.run —no-opengl-libs

设置环境变量和动态链接库

vi /etc/profile

在文件末尾加入：

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

再创建链接文件

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

加入文本

/usr/local/cuda/lib64

再执行以下命令使链接生效

ldconfig

编译测试程序，若出现显卡信息，则安装成功。

cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery

make

./deviceQuery

四、CUDNN6.0

CUDNN下载地址，这里选择6.0版本。

tar zxvf cudnn-8.0-linux-x64-v6.0.tgz

cd cuda

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

cp lib64/libcudnn.* /usr/local/lib

ln -sf /usr/local/lib/libcudnn.so.6.0.21 /usr/local/lib/libcudnn.so.6

ln -sf /usr/local/lib/libcudnn.so.6 /usr/local/lib/libcudnn.so

ldconfig -v

若出现gcc版本过高，而编译caffe-master报错的话，

vi /usr/local/cuda/include/host_config.h

搜索： #error -- unsupported GNU version! gcc versions later than 5.3 are not supported!

修改为： //#error -- unsupported GNU version! gcc versions later than 5.3 are not supported!

五、Matlab2016b

关于 GCC 和 G++ 版本问题

Matlab 2014a gcc/g++ 4.7.x, Matlab 2016a gcc/g++ 4.9.x

Ubuntu 15.04 gcc/g++ 4.9.x, Ubuntu 16.04 gcc/g++ 5.4.x

原则上Matlab需要和Ubuntu版本一致，由于CUDA 8只支持16.04,而且需要GCC 5.4.x 进行编译，而CUDA 7.5不支持 Ubuntu 16.04 因此Matlab会有一些奇葩，有时按照降级（或强制安装）的方法可以正常使用，有时却会报错，怀疑和显卡驱动有关。

引自宇宙骑士欧老师

因而选择了2016b版本。

将镜像文件挂载到image monitor上。将所有文件拷贝到Home/Matlab。

chmod a+x Matlab -R

./install

六、BLAS

安装MKL，以学生身份下载Student版，填好各种信息，可以直接下载，同时会给你一个邮件告知序列号。下载完之后，要把文件解压到home文件夹(注意任何一级文件夹不能包含空格，否则安装会失败)。这里下载的是2017版本。

tar zxvf parallel_studio_xe_2017.tar.gz

chmod a+x parallel_studio_xe_2017-R

sh install_GUI.sh

环境配置，新建conf文件

vi /etc/ld.so.conf.d/intel_mkl.conf

并键入

/opt/intel/lib/intel64

/opt/intel/mkl/lib/intel64

七、OpenCV3.1

安装依赖项

apt-get install build-essentialsudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-devsudo apt-get install –assume-yes libopencv-dev libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev libtbb-dev libqt4-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils unzip

apt-get install ffmpeg libopencv-dev libgtk-3-dev python-numpy python3-numpy libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libv4l-dev libtbb-dev qtbase5-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev

下载OpneCV源文件

wget -O opencv-3.1.0.zip http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/3.1.0/opencv-3.1.0.zip/download

或者从Github clone

mkdir opencv

cd opencv

git clone https://github.com/opencv/opencv.git

git clone https://github.com/opencv/opencv_contrib.git

编译和安装

mkdir build

cd build

cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D INSTALL_C_EXAMPLES=ON -D INSTALL_PYTHON_EXAMPLES=ON -D BUILD_EXAMPLES=ON -D WITH_QT=ON -D WITH_OPENGL=ON ..

make -j4

sudo make install

sudo sh -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf'

sudo ldconfig

这里可能遇到的问题包括：

- ippicv无法下载

- cuda8.0不支持

解决方案：

- 自行下载ippicv_linux_20151201，将文件拷贝到opencv-3.1.0/3rdparty/ippicv/downloads/linux-808b791a6eac9ed78d32a7666804320e/路径下

- modules/gpu/src/graphcuts.cpp

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

改成

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

八、其他依赖项

1. Google Logging Library（glog）下载地址

tar zxvf glog-0.3.3.tar.gz

./configure

make

sudo make install

如果没有权限就

chmod a+x glog-0.3.3 -R

2.其他依赖项

sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler protobuf-c-compiler protobuf-compiler 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 ipython

九、Caffe

下载源文件，进行配置

cp Makefile.config.example Makefile.config

vi Makefile.config

1.启用CUDNN

USE_CUDNN := 1

2.配置引用文件（Ubuntu16.04下，文件位置发生变化）

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/lib/x86_64-linux-gnu/hdf5/serial/include

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial

3.启用Intel Parallel Studio XE 2017

BLAS := mkl

4.配置路径，实现caffe对Python和Matlab接口的支持

PYTHON_LIB := /usr/local/lib

MATLAB_DIR := /usr/local/MATLAB/R2016b

5.启用Opencv

OPENCV_VERSION =3

这里列出一份配置参考

## 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_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 := mkl

# 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/R2016b

# 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

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/lib/x86_64-linux-gnu/hdf5/serial/include

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /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 ?= @

编译caffe-master

make all -j16

make test -j16

make runtest -j16

make pycaffe -j16

make matcaffe -j16

十、测试

这里用mnist数据集进行测试。如若下载数据集不方便，附上云盘下载。

不做具体说明。

sh data/mnist/get_mnist.sh

sh examples/mnist/create_mnist.sh

sh examples/mnist/train_lenet.sh

至此，配置完毕。

最后编辑于：2017.12.11 14:27:30

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