Ubuntu 16.04源码编译深度学习框架caffe 的两天血泪史总结，终于成功啦

Ubuntu 16.04安装caffe cup版本

说在前面：caffe 的编译特别容易出错，心态易爆炸，请保持耐心。编译之前请先查清自己的Ubuntu版本，版本在17以上请参考官方提供更简单快速的安装方式http://caffe.berkeleyvision.org/

1. lsb_release -a
2. cat /proc/version 
3. uname -a 

1. 安装下面的所以依赖

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install  -y libopencv-dev
sudo apt-get install -y build-essential cmake git pkg-config
sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install -y liblapack-dev
sudo apt-get install -y libatlas-base-dev 
sudo apt-get install -y --no-install-recommends libboost-all-dev
sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev


sudo apt-get install -y python-pip
sudo apt-get install -y python-dev
sudo apt-get install -y python-numpy python-scipy
sudo apt-get install -y python3-dev
sudo apt-get install -y python3-numpy python3-scipy

2. 使用git下载caffe 源码

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

正式安装caffe

1. 如果需要caffe python 的接口，可以先进入caffe 下的python文件夹开始下载依赖

cd caffe/python/
sudo apt-get install python-pip
for req in $(cat requirements.txt); do pip install $req; done

2. 拷贝一个安装文件，然后修改其中的部分配置

cp Makefile.config.example Makefile.config
vim Makefile.config

3. 将以下修改

1.将
#CPU_ONLY := 1
改为
CPU_ONLY := 1
2.应用 opencv 版本，将
#OPENCV_VERSION := 3 
修改为： 
OPENCV_VERSION := 3
3.使用 python 接口,将
#WITH_PYTHON_LAYER := 1 
修改为 
WITH_PYTHON_LAYER := 1
4.修改重要的一项
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

• 完整的文件为：

## 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
# This code is taken from https://github.com/sh1r0/caffe-android-lib
# USE_HDF5 := 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.
# 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 ?= @

4. 再修改caffe 下的Makefile 文件，文件行数很多，须耐心查找

1.将：
NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)
替换为：
NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
2.将：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
改为：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial opencv_core opencv_imgproc opencv_imgcodecs opencv_highgui

5. 开始编译，最刺激的一部分开始了，中间过程有任何报错都表示安装不成功

make all
make test
make runtest
# 编译caffe 的python接口，不需要可以省略
make pycaffe

• （中间过程有报错安装失败，都请再重复编译之前使用make clean）

测试是否安装成功，并添加环境变量

（熟悉环境变量添加的可直接跳到下面的步骤编号1处）

• 说明：Ubuntu系统包含两类环境变量：系统环境变量和用户环境变量。系统环境变量对所有系统用户都有效，用户环境变量仅仅对当前的用户有效 *
• 用户环境变量可以存储以下文件中

~/.profile  #推荐使用
~/.bashrc, ~/.bash_profile, ~/.bash_login  # 图形化界面启动时会失效

• 系统环境变量存储文件

/etc/profile

/etc/profile.d（它是文件夹，在该目录下的*.sh，即以sh为后缀的文件都会被加载。）
推荐使用以上两种，下面这种方式图形化界面启动会失效
/etc/bash.bashrc

• 环境变量中基础知识

export命令：使得变量真正输出成为环境变量。等号左边为变量名，右边为变量实际值。export后，可以通过$variable_name的形式访问变量，如$JAVA_HOME，即可替换为JAVA_HOME=/usr/lib/jvm/jdk1.7.0。

PATH变量中通常有多个指定路径，中间用冒号隔开。拼接上$PATH变量，则不影响原先的路径设置。

• 如何使其生效，示例：

root@iZ8vbi4t:~/ml/caffe/python# vim /etc/profile

# /etc/profile: system-wide .profile file for the Bourne shell (sh(1))
# and Bourne compatible shells (bash(1), ksh(1), ash(1), ...).

if [ "$PS1" ]; then
  if [ "$BASH" ] && [ "$BASH" != "/bin/sh" ]; then
    # The file bash.bashrc already sets the default PS1.
    # PS1='\h:\w\$ '
    if [ -f /etc/bash.bashrc ]; then
      . /etc/bash.bashrc
    fi
  else
    if [ "`id -u`" -eq 0 ]; then
      PS1='# '
    else
      PS1='$ '
    fi
  fi
fi

if [ -d /etc/profile.d ]; then
  for i in /etc/profile.d/*.sh; do
    if [ -r $i ]; then
      . $i
    fi
  done
  unset i
fi
export FACESDK=/root/face_service_sdk_v0.3.4-jingcai/lib/

source /etc/profile  #修改成自己添加变量的存储文件名，最好是绝对路径
# 查看环境是否生效，控制台输入命令：
$FACEALG

1. 测试caffe 的python接口，切换到caffe/python下（没加环境变量的缘故，所以只能在该目录下才可以）

python
import caffe
报错则安装失败

2. 添加环境变量

cd ~
vim ~/.bashrc
#最后一行添加：
export PYTHONPATH=/home/gs/caffe/python:$PYTHONPATH
#这里的路径是你的caffe下的python路径

3. 让环境变量立即生效

source .bashrc

4. 任意目录下重复步骤一，无报错则环境变量添加成功
5. 自行测试caffe 的测试示例，连接地址如下

https://www.cnblogs.com/denny402/p/5075490.html