最近由于需要，安装一下caffe,由于手边暂时没有gpu，所以在自己笔记本先安装一下cpu版本的caffe。由于caffe是属于比较老的框架，然后对python 2.7支持比较好，或者python3.5以下也是可以的，但是由于我们的python是3.6,以及tensorflow == 1.6，因此我们尝试在python 3.6的情况下，安装caffe CPU版本。
系统版本：
ubutu 16.04anoconda 4.3.30python 3.6opencv 3.2.0caffe 1.0.0为了和tensorflow、pytorch、theano等环境区分，我们首先创建一个caffe环境，使用anaconda创建。conda创建、查看、删除虚拟环境
1.首先查看当前已有环境：
conda-env list2.创建caffe虚拟环境
conda create -n CAFFE python=3.63.进入环境
source activate CAFFE小提示，退出环境命令为source deactivate
为了安装caffe，我们首先先将opencv3安装好
参考我的博客: ubutu16.04卸载opencv2安装opencv3
1.切换到root权限，su, 依次安装：
sudo apt-get install libprotobuf-dev sudo apt-get install libleveldb-dev sudo apt-get install libsnappy-dev sudo apt-get install libopencv-dev sudo apt-get install libhdf5-serial-dev sudo apt-get install protobuf-compiler sudo apt-get install --no-install-recommends libboost-all-devCPU Only的情况下，跳过了CUDA相关的安装； 接下来是BLAS：
sudo apt-get install libatlas-base-dev使用默认Python来建立pycaffe接口，需要安装：
sudo apt-get install python-dev一些兼容性依赖库：
sudo apt-get install libgflags-dev sudo apt-get install libgoogle-glog-dev sudo apt-get install liblmdb-dev参考：
ubuntu16.04 python3.6 caffe(CPU) 配置记录caffe安装：基于anaconda3—python3.6, linux, 仅CPUUbuntu16.04安装Caffe(CPU Only)利用C++ Boost库将C++项目封装为Python模块没有安装git的话需要先装一下git，同样也是需要root权限的。
sudo apt-get install git下载Caffe源码：
git clone https://github.com/BVLC/caffe.git如果需要Caffe的Python接口，切换到caffe下的python目录下，输入以下命令下载python依赖库（先安装pip）：
sudo apt-get install python-pip for req in $(cat requirements.txt); do pip install $req; done小技巧，从这里可以看出这里的requirements.txt需要安装的东西还是比较多的，由于下载速度可能会比较慢，所以有些包可能需要下载，然后离线安装，安装包的格式就是.whl和.zip格式等。
前面的各种包并不是太难，caffe难装的原因就是编译可能会出现各种各样的错误，这里我们也是一步一步来。
拷贝一份Makefile.config.example并重命名成Makefile.config，修改该配置文件：
cp Makefile.config.example Makefile.config这一步非常麻烦，但是也十分重要。。。
这是我的Makefile.config，里面做了一些修改。主要参考的是ubuntu16.04 python3.6 caffe(CPU) 配置记录，同时也在这个基础上，做了一些修改，这是依照后面的错误来的。
添加HOME := /home/xuchao，原来没有写。添加代码 LIBRARIES += glog gflags protobuf leveldb snappy lmdb boost_system hdf5_hl hdf5 m opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs我的Makefile.config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 # 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/local/lib/python2.7/dist-packages/numpy/core/include # Anaconda Python distribution is quite popular. Include path: # Verify anaconda location, sometimes it's in root. # HOME path !!!! HOME := /home/xuchao ANACONDA_HOME := $(HOME)/anaconda3 PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ $(ANACONDA_HOME)/include/python3.6m \ $(ANACONDA_HOME)/lib/python3.6/site-packages/numpy/core/include # Uncomment to use Python 3 (default is Python 2) PYTHON_LIBRARIES := boost_python3 python3.6m # PYTHON_INCLUDE := /usr/include/python3.6m \ # /usr/lib/python3.6/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 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 $LIBRARY_DIRS.) # USE_PKG_CONFIG := 1 LIBRARIES += glog gflags protobuf leveldb snappy lmdb boost_system hdf5_hl hdf5 m opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs # N.B. both build and distribute dirs are cleared on `make clean` BUILD_DIR := build DISTRIBUTE_DIR := distribute # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1 # The ID of the GPU that 'make runtest' will use to run unit tests. TEST_GPUID := 0 # enable pretty build (comment to see full commands) Q ?= @ LINKFLAGS := -Wl,-rpath,$(HOME)/anaconda3/lib#1.首先输入命令：
sudo ln -s /home/xuchao/anaconda3/bin/python3 /usr/lib/python3.6m(这里是建立一个软链接，把我本地目录下的python3.6加到usr/lib/中去。2.下载boost_1_67_0
wget -O boost_1_67_0.tar.gz http://sourceforge.net/projects/boost/files/boost/1.67.0/boost_1_67_0.tar.gz/download tar xzvf boost_1_67_0.tar.gz3.安装附加依赖库
sudo apt-get update sudo apt-get install build-essential g++ python-dev autotools-dev libicu-dev build-essential libbz2-dev libboost-all-dev4.编译boost_1_67_0
cd boost_1_67_0/ ./bootstrap.sh --with-libraries=python --with-toolset=gcc ./b2 --with-python include="/home/xuchao/anaconda3/include/python3.6m/" sudo ./b2 install/home/xuchao/可能要换成你自己的目录名字。
编译安装成功后，/usr/local/lib下会有libboost_python36.so和libboost_python36.a，有些应用link时需要的是libboost_python3.so或者libboost_python3.a，我们建个软链：
cd /usr/local/lib sudo ln -s libboost_python-py36.so libboost_python3.so sudo ln -s libboost_python-py36.a libboost_python3.a这时，我们要把/usr/local/lib 中，相关文件，建立相对于名称的软链接到/usr/lib/x86_64-linux-gnu中。
sudo cp /usr/local/lib/libboost_python36.a /usr/lib/x86_64-linux-gnu/libboost_python_python36.a sudo cp /usr/local/lib/libboost_python36.so.1.67.0 /usr/lib/x86_64-linux-gnu/libboost_python3.so依次输入以下命令：
sudo make all -j4 sudo make test -j4 sudo make runtest -j4 sudo make pycaffe -j4make默认单核运算，如果想加快速度，我这里是4核，可以在每条命令后面加上-j4，如果有报错，建议最好make clean重新开始。 如果所有测试都通过，则说明安装好了。
测试Caffe的Python接口，切换到caffe/python文件目录下，记录下来当前路径，输入以下命令：
export PYTHONPATH=/home/xuchao/caffe/python:$PYTHONPATH/home/xuchao换成你自己的。
进入python环境，输入：
import caffe果没有报错，证明安装成功。 上面的方法，一旦关闭终端或者打开新终端则失效，如果放到配置文件中，可以永久有效果，命令操作如下：
#A.把环境变量路径放到 ~/.bashrc文件中 sudo echo export PYTHONPATH="~/caffe/python" >> ~/.bashrc #B.使环境变量生效 source ~/.bashrc其他参考：
BVLC/caffeError: ‘make all’ ‘make test’ #2348Trouble building caffe on Ubuntu 18.04 #6720花了一天的时间，终于把caffe安装好了。后面安装gpu版本的caffe再说了。
