Ubuntu 14.04上安装caffe

本来实在windows 10上尝试安装caffe，装了一天没装上，放弃; 改在windows上装ubuntu的双系统，装了一个下午，不小心windows的系统盘被锁死了，也不会unlock？只好含泪卸掉了windows10，只装ubuntu 15.10。过程真是让人揩一把辛酸泪。。。不过又在ubuntu中用错了一个命令，系统崩溃，重新安装win8.1 + ubuntu 14.04, 成功，继续安装caffe。。。

安装：

ubuntu 14  .04，

1. 安装开发所需依赖包：

sudo apt-get install build-essential  # basic requirement  
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler #required by caffe

2. 安装CUDA 7.5：

根据Nvidia官方文档http://developer.download.nvidia.com/compute/cuda/7.5/Prod/docs/sidebar/CUDA_Installation_Guide_Linux.pdf一步一步安装

　　1) 确定GPU支持CUDA

lspci | grep -i nvidia

　　结果显示：

　　 到

http://developer.nvidia.com/cuda-gpus去验证，发现支持CUDA

　　2) 确定linux版本支持CUDA：

uname -m && cat /etc/*release

　　结果显示：

　　

　　3) 确定系统已经安装了gcc：

gcc --version

　　结果显示：

　　

　　4) 确定系统已经安装了正确的Kernel Headers和开发包：

　　查看系统正在运行的kernel版本：

uname -r

　　结果：4.2.0-35-generic，这个是必须安装的kernel headers和开发包的版本

　　安装对应的kernels header和开发包：

sudo apt-get install linux-headers-$(uname -r)

　　5) 安装CUDA：下载对应版本的cuda，从https://developer.nvidia.com/cuda-downloads，下载deb(network)版

　　首先进行md5校验：

md5sum cuda-repo-ubuntu1404_7.5-18_amd64.deb

　　对比相同，然后使用deb文件进行安装：

sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb 
sudo apt-get update
sudo apt-get install cuda

 　　重启，完成cuda安装

3. 安装cuDNN

下载从https://developer.nvidia.com/rdp/cudnn-download cuDNN，版本为cudnn-7.0-linux-x64-v3.0-prod.tgz

tar -zxvf cudnn-7.0-linux-x64-v3.0-prod.tgz 
cd cuda
sudo cp lib64/* /usr/local/cuda/lib64/
sudo cp include/cudnn.h /usr/local/cuda/include/ 

更新软链接？不懂，照做

cd /usr/local/cuda/lib64
sudo rm -rf libcudnn.so libcudnn.so.7.0
sudo ln -s libcudnn.so.7.0.64 libcudnn.so.7.0
sudo ln -s libcudnn.so.7.0 libcudnn.so

4. 设置环境变量：

在/etc/profile中添加CUDA 环境变量

sudo gedit /etc/profile，在打开的文件中加入如下两句话

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

保存后，使环境变量立即生效：source /etc/profile

 

5. 安装cuda samples

进入/usr/local/cuda/samples，执行下面的命令来build samples

sudo make all -j4

全部编译完成后，进入 samples/bin/x86_64/linux/release, 运行deviceQuery： ./deviceQuery

如果出现显卡信息，则驱动及显卡安装成功，结果如下：

安装成功！

6. 安装Intel MKL，openBlas 或Atlas：

我选择的是Atlas，为caffe默认使用的，不要额外配置，安装命令

sudo apt-get install libatlas-base-dev

7. 安装opencv：

首先安装必须的包

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

从opencv官网下载最新版本opencv，opencv-3.1.0.zip

解压unzip opencv-3.1.0.zip

编译

cd  opencv-3.1.0
mkdir release
cd release
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
make
sudo make install

 测试是否安装成功：

 mkdir ~/opencv-workshop
cd ~/opencv-workshop/
gedit DisplayImage.cpp
gedit CMakeLists.txt
cmake .
make

其中 Display.cpp的代码为

#include <stdio.h>
#include <opencv2/opencv.hpp>
using namespace cv;

int main(int argc, char** argv)
{
    Mat image;
    image = imread(argv[1], 1);
    if (!image.data)
    {
        printf("No image data
");
        return -1;
    }
    namedWindow("display", WINDOW_AUTOSIZE);
    imshow("display", image);
    waitKey(0);
    return 0;
}

其中 CMakeList.txt的内容是

cmake_minimum_required(VERSION 2.8)
project(DisplayImage)
find_package(OpenCV REQUIRED)
add_executable(DisplayImage DisplayImage.cpp)
target_link_libraries(DisplayImage ${OpenCV_LIBS})

脚本运行后，运行可执行文件，看是否能正确显示图片，能，则成功

./DisplayImage ~/图片/2016-04-13 21:35:01屏幕截图.png 

8. 安装caffe所需要的Python环境Anaconda：

按caffe推荐，python环境使用Anaconda，去官网下载安装包http://conda.pydata.org/miniconda.html,

 下载Anaconda2-4.0.0-Linux-x86_64.sh，切换到文件所在路径，执行

bash Anaconda2-4.0.0-Linux-x86_64.sh

按照提示进行安装。

安装好后，在/etc/ld.so.conf中添加一下路径：/home/wm/anaconda2/lib

在~/.bashrc最后添加下面一句：

export LD_LIBRARY_PATH="/home/wm/anaconda2/lib:$LD_LIBRARY_PATH"

9. 安装python依赖库

下载caffe源码，解压后进入caffe-master下的python目录，

（首先要安装python-pip

sudo apt-get install python-pip

）执行如下命令

 for req in $(cat requirements.txt); do pip install $req; done

10. 编译Caffe

进入caffe-master目录，复制一份Makefile.config.examples

cp Makefile.config.example Makefile.config 

修改Makefile.config中的路径，只需要将默认的Python路径注释掉，去掉对Anaconda路径的注释，并且开启CuDNN和Opencv3的支持即可，修改好的如下：

## 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 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_50,code=compute_50

# 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
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

# 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

# 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

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 ?= @

保存退出，编译

make all
make test
make runtest

没有报错，成功。

11. 使用MNIST数据集进行测试

1) 数据预处理：

sh data/mnist/get_mnist.sh 

2) 重建lmdb/leveldb文件，命令

sh examples/mnist/create_mnist.sh 

3) 训练mnist，命令

sh examples/mnist/train_lenet.sh

这是可以看到caffe的训练过程，至此caffe配置已成功：

接下来会学习caffe的使用。。。 加油