Ubuntu 64bit下手动安装深度学习包Caffe记录(CPU)

前言

之前在电脑上安装Caffe都是使用的简单安装（sudo…sudo..do），这几天闲下来折腾起了手动安装。这样做主要是为了方便进行移植，这里将所有的依赖库安装到了

/home/pc_name(你自己的电脑账户名，下同)/local_install/

目录下。博主在Ubuntu 64bit上安装成功，现在将整个的安装过程记录下来，希望对各位看官有所帮助。若各位看官需要本次安装所需的全部安装包可以QQ联系我（2414385027）。

整篇文章基本讲干货，其他的什么介绍之类的就省略了-_-||…

1. Protobuf安装

tar zxvf protobuf-cpp-3.4.1.tar.gz
cd protobuf-cpp-3.4.1/
./configure --prefix=/home/pc_name/local_install

make
make install

安装完成之后为了校验安装的正确性，查看对应目录下有没有protoc文件

ls ~/local_install/bin/
protoc

为了能在命令行运行，将该目录加入到PATH中去

export PATH=~/local_install/bin/:$PATH

将其写入到

/home/pc_name/.bashrc

2. BOOST安装

tar zxvf boost_1_56_0.tar.gz
cd boost_1_56_0/

./bootstrap.sh --with-libraries=system,thread,python
./b2
./b2 install

cp -r boost/ /home/pc_name/local_install/include/
cp stage/lib/* /home/pc_name/local_install/lib/

3. CFLAGS安装

tar zxvf gflags-2.2.1.tar.gz
cd gflags-2.2.1/
mkdir build && cd build
cmake ..
ccmake ..

若未能识别ccmake需要执行下面命令进行安装

sudo apt-get install cmake-curses-gui

按照上面的进行配置完成之后按C键，之后G键生成Makefile文件

make
make install

CFLAGS在Caffe中主要起到命令行参数解析的作用，这与protobuf功能类似，只是参数输入源不同。CFLAGS的使用方法可参考Caffe源码中的tools/caffe.cpp

4. GLOG安装

tar zxvf glog-0.3.5.tar.gz
cd glog-0.3.5/
./configure --prefix=/home/pc_name/local_install/
make
make install

5. OpenBLAS安装

unzip OpenBLASv0.2.20.zip
make –j
make PREFIX=/home/pc_name/local_install/ install

6. HDF5安装

tar xvf hdf5-1.10.1.tar
cd hdf5-1.10.1/
./configure --prefix=/home/pc_name/local_install/
make -j && make install

7. OpenCV安装

unzip opencv-3.0.0.zip
cd opencv-3.0.0/
mkdir build && cd build
cmake ..
ccmake ..

make
make install

8. LMDB安装

tar zxvf lmdb-LMDB_0.9.21.tar.gz
cd lmdb-LMDB_0.9.21/
make

将生成的lmdb.h拷贝到

/home/pc_name/local_install/include

目录下，liblmdb.so拷贝到

/home/pc_name/local_install/lib

目录下。

9. LEVELDB安装

tar zxvf leveldb-1.20.tar.gz
cd leveldb-1.20/
make

cp -r include/leveldb ~/local_install/include/
cp libleveldb.so* ~/local_install/lib/

10. Snappy安装

tar zxvf snappy-1.1.1.tar.gz
cd snappy-1.1.1/
./configure --prefix=/home/pc_name/local_install/
make && make install

在所有的附加库安装完成之后得到的2级目录结构是这样的

.
├── bin
├── include
│   ├── boost
│   ├── gflags
│   ├── glog
│   ├── google
│   ├── leveldb
│   ├── opencv
│   └── opencv2
├── lib
│   ├── cmake
│   └── pkgconfig
└── share
├── doc
├── hdf5_examples
└── OpenCV
16 directories

11. Caffe安装

至于Caffe的安装需要的是配置好Makefile.config文件就好了，下面提供本次讲解使用的文件内容（只是用CPU）

## 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 := open
# 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 := /home/sucker/local_install/include $(PYTHON_INCLUDE)\
/usr/local/include
LIBRARY_DIRS := /home/sucker/local_install/lib $(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

# 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 ?= @

编辑好config文件之后就是进行编译了，就是make命令。之后为了检验安装是否成功需要进行如下验证

make test
make runtest

本人在其中遇到这个错误

.build_release/tools/caffe: error while loading shared libraries: libboost_system.so.1.56.0: cannot open shared object file: No such file or directory make: *** [runtest] Error 127

解决办法：

在/home/pc_name/下编辑.bashrc文件，添加

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/pc_name/local_install/lib