Caffe在Ubuntu 14.04 的安装

虽然看起来caffe比TensorFlow难用吧？先装个caffe试一下。昨天听了学长讲了半天，嘿嘿。 安装才是最麻烦的，不过用的台式机没有GPU怕是方便了很多。 依赖包这种的，我习惯一股脑全上，怕什么hh。

1. 安装开发依赖包

1.一般依赖项 sudo apt-get install build-essential sudo apt-get install vim cmake git 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 2.BLAS依赖项 sudo apt-get install libatlas-base-dev 或者安装 sudo apt-get install libopenblas-dev 3.python依赖项 安装python及其头文件 sudo apt-get install python sudo apt-get install python-dev 安装python的其他依赖 sudo apt-get install python-numpy sudo apt-get install ipython sudo apt-get install ipython-notebook sudo apt-get install python-sklearn sudo apt-get install python-skimage sudo apt-get install python-protobuf 4.谷歌glog和gflags和lmdb依赖项 sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev 5.安装opencv 我应该把之前的安装写个博客在这里放个链接。别人这样装得嘿嘿。 > 这个尽量不要手动安装， Github上有人已经写好了完整的安装脚本：https://github.com/jayrambhia/Install-OpenCV 下载该脚本，进入Ubuntu/2.4 目录, 给所有shell脚本加上可执行权限 chmod +x *.sh 然后安装最新版本 （当前为2.4.9） sudo ./opencv2_4_9.sh 脚本会自动安装依赖项，下载安装包，编译并安装OpenCV。整个过程大概半小时左右。 注意，中途可能会报错 opencv-2.4.9/modules/gpu/src/nvidia/core/NCVPixelOperations.hpp(51): error: a storage class is not allowed in an explicit specialization 解决方法在此：http://code.opencv.org/issues/3814 下载 NCVPixelOperations.hpp 替换掉opencv2.4.9内的文件， 重新build。

2.下载caffe，修改Makefile.config文件

安装git，并且下载代码

sudo apt-get install git git clone https://github.com/BVLC/caffe.git

然后进入到源码目录

cd caffe cp Makefile.config.example Makefile.config vim Makefile.config

主要是修改Makefile.config文件 没有GPU，python默认路径，我就改了3个地方

CPU_ONLY := 1 USE_OPENCV := 1 WITH_PYTHON_LAYER := 1

Caffe中的Makefile.config的一些说明 稍加修改。其中很多注释掉example注释掉了，但是是默认使用的，所以去不去掉注释并没有关系。 如

# CUSTOM_CXX := g++ linux系统默认使用g++编译器，OSX则是clang++。

两者都不打开 Caffe默认用的是LMDB

# USE_LEVELDB := 0 # USE_LMDB := 0

3.编译caffe

make all -j8 make test -j8 make runtest

如果要编译python的绑定（你要安装好python及其依赖）

make pycaffe -j8

结束！

Makefile.config文件 整体解释如下， |依自己情况去掉注释

## 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 "CuDNN是NVIDIA专门针对Deep Learning框架设计的一套GPU计算加速库，用于实现高性能的并行计算,在有GPU并且安装CuDNN的情况下可以打开即将注释去掉。" # CPU-only switch (uncomment to build without GPU support). # CPU_ONLY := 1 "表示是否用GPU,如果只有CPU这里要打开" # uncomment to disable IO dependencies and corresponding data layers USE_OPENCV := 1 "因为要用到OpenCV库所以要打开，下面这两个选项表示是选择Caffe的数据管理第三方库，两者都不打开 Caffe默认用的是LMDB，这两者均是嵌入式数据库管理系统编程库。" # 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 "当需要读取LMDB文件时可以取消注释，默认不打开。" # Uncomment if you're using OpenCV 3 # OPENCV_VERSION := 3 是3再注释，不然不用管 # 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++ "linux系统默认使用g++编译器，OSX则是clang++。" # CUDA directory contains bin/ and lib/ directories that we need. CUDA_DIR := /usr/local/cuda "CUDA的安装目录" # 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 "这些参数需要根据GPU的计算能力 (http://blog.csdn.net/jiajunlee/article/details/52067962)来进行设置，6.0以下的版本不支持×_50的计算能力。" # BLAS choice: # atlas for ATLAS (default) # mkl for MKL # open for OpenBlas BLAS := open "如果用的是ATLAS计算库则赋值atlas，MKL计算库则用mkl赋值，OpenBlas则赋值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 := /usr/local/OpenBlas/include BLAS_LIB := /usr/local/OpenBlas/lib "blas库安装目录" # 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 "如果不是安装在标准路径则要指明" # 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 "matlab安装库的目录" # 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 "python安装目录" # 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 <font color="green">python库位置</font> # 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 # 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 "所用的GPU的ID编号" # enable pretty build (comment to see full commands) Q ?= @