Ubuntu 64bit下手动安装深度学习包Caffe记录(CPU)

逃离我推掉我的手 2022-06-06 05:55 218阅读 0赞

# 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

![CFLAGS配置][CFLAGS]  
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 ..

![OpenCV配置注意项][OpenCV]

make
    make install

![安装完成][SouthEast]

## 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

![这里写图片描述][SouthEast 1]

[CFLAGS]: /images/20220606/96501991feb444c2957d4ab0a38159ee.png
[OpenCV]: /images/20220606/e2057998db6c43d589c3ea4d34d3ae52.png
[SouthEast]: /images/20220606/2ad98b10e7564b10be85dc234f56e725.png
[SouthEast 1]: /images/20220606/2cb0cfa48e4a4b8fbc0d721e899b7369.png