Ubuntu 16.04 64位 caffe安装 gpu CUDA8.0+cuDNN6.0+OpenCV3.1

添加依赖项：

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

一、安装显卡驱动

1.查看显卡驱动：

打开系统设置-->选择详细信息-->例如博主的一台电脑配置是这样的：

请确认你自己的电脑是NVIDIA显卡的，例如博主是GT 705，否则gpu的网络你是别想装了～～

2.查看驱动程序：

打开系统设置-->软件和更新-->附加驱动，例如博主这里就是这样的：

可以看到这里的显卡驱动版本是384，大家自己的电脑驱动可能有所不同，这里先请不要应用更改，会在之后的步骤中安装相应驱动

3.安装NVIDIA显卡驱动

sudo add-apt-repository ppa:xorg-edgers/ppa
sudo apt-get update
sudo apt-get install nvidia-384 #自己的驱动版本

4.安装成功会显示这个

输入：sudo nvidia-smi

说明安装成功！！

二、CUDA8.0安装

1.CUDA8.0下载

这里给出一个直接的网址：CUDA下载地址

我们点击CUDA Tookit 8.0 GA2/GA1：

这里博主选择的是runfile（local），点击下载，1.4GB

安装输入：sudo sh cuda_8.0.61_375.26_linux.run

之后会开始协议阅读，一直按空格直到显示结束100%，会开始安装选项设置：

            第一个是accept

            第二个是让你安装accelerate驱动，也就是下面这个，这个是no

            Install NVIDIA Accelerated Graphics Driver for .....?

            之后的文件夹位置默认，还有一个symbolic link这个选择yes

 反正除了一个驱动是no其他都是允许和是和默认～～

2.环境变量配置：

输入：

sudo gedit ~/.bashrc

在文件最后加上这两行：

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}}

可以执行这句话：（虽然可能没用）

source ~/.bashrc

3.测试CUDA的samples

cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery #自己的目录
make
sudo ./deviceQuery

这里有一个很重要的GPU加速性能，在第二行CUDA Capability Major/Minor Version Number ,博主的不行是2.1

注意看到最后一行显示Result = PASS，说明你的CUDA安装成功！！

三、cuDNN

1.下载地址，很直接：cuDNN下载地址

2.博主这里选择的是cuDNN v6.0 for CUDA 8.0 ,因为cuDNN 7.0装tensorflow会报错，版本不兼容，总之一步到位就好，省的卸载重装，因为显卡不行～～

选择 cuDNN v6.0 Library for Linux ，之后下载解压，会有5个文件：1个在include里，剩下4个在lib64里～

3.合并文件夹，说白了就是把cuDNN的东西放进CUDA8.0里

sudo cp cuda/include/cudnn.h /usr/local/cuda-8.0/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64/
sudo chmod a+r /usr/local/cuda-8.0/include/cudnn.h
sudo chmod a+r /usr/local/cuda-8.0/lib64/libcudnn*

四、安装opencv3.1

1.官网下载慢，这里直接给出百度云地址：opencv3.1 linux

2.解压，修改graphcuts.cpp(opencv3.1还不支持cuda8.0)

cd opencv-3.1.0/modules/cudalegacy/src
sudo gedit graphcuts.cpp

3.找到这个位置修改，其实就是加了一个 CUDART_VERSION>=8000

4.make（cmake没有就自己装一个吧～）

mkdir build
cd build
sudo cmake –D CMAKE_BUILD_TYPE=Release –D CMAKE_INSTALL_PREFIX=/usr/local ..
sudo make –j8#博主是8核的
sudo make install

五、安装caffe

1.下载：

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

2.下载完成之后解压，打开caffe文件夹，默认在 ～目录下

复制一份Makefile.config.example 命名为Makefile.config

sudo cp Makefile.config.example Makefile.config

之后打开Makefile.config

sudo gedit Makefile.config

博主的是这样的：

## 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-8.0
# 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)/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 /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 ?= @

注意几个修改的地方：

（1）是否使用cuDNN，博主因为GPU加速性能小于3.0，因此这里要注销，否则runtest的时候会报错！

# cuDNN acceleration switch (uncomment to build with cuDNN).

# USE_CUDNN := 1

（2）opencv版本是2还是3，博主这里是取消注释的，因为是opencv3.1

# Uncomment if you're using OpenCV 3
 OPENCV_VERSION := 3

   (3)CUDA的目录，注意不是链接目录！！

# CUDA directory contains bin/ and lib/ directories that we need.

CUDA_DIR := /usr/local/cuda-8.0

（4）最重要的地方

# 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

3.修改Makefile：

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

4.make:

sudo make all -j8    #8核

如果出现了：error while loading shared libraries: xxx.so.x:cannot open shared object file: No such file or directory

说明你的/usr/local/lib这个文件夹里没有这个文件，那么就请复制到这个文件夹里吧！

sudo cp xxx.so.x /usr/local/lib/

再不然就是依赖没装，那就apt-get install吧！

例如博主的libcudnn.so.6没有，那就去/usr/local/cuda-8.0/lib64找到它，之后复制到/usr/local/lib就可以，make clean再重新make

-----usr/local/cuda-8.0/lib64

----/usr/local/lib

5.make runtest

sudo make runtest

如果出现了：caffe make runtest error（core dumped）Check failed: status == CUDNN_STATUS_SUCCESS (6 vs. 0)

那就说明GPU加速性能不够，打开刚才的deviceQuery，就可以看到。如果想解决这个问题，只能把Makefile.config里的USE_CUDNN: =1注释掉，不用cuDNN，再make就没问题了，只是会慢～～

最后一行是PASS说明没有问题！！

本篇文章到此结束！！