1. 安装前准备工作：

   sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
   sudo apt-get install --no-install-recommends libboost-all-dev
   sudo apt-get install libatlas-base-dev
   sudo apt-get install the python-dev
   sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

    

2. 【可选】安装CUDA和Anaconda，详细见Ubuntu16.04+theano环境
3. 下载caffe：

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

    

4. 修改配置文件：

   cd caffe/
   cp Makefile.config.example Makefile.config

    

5. 修改配置文件中的各种路径

   vim Makefile.config.

    我配置好的配置文件为：

   ## 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
   
   # 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 := 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)/anaconda2
   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/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 ?= @

    

6. 【注意】不要直接复制粘贴（用户名不对），注意添加hdf5的有关路径，没有装CUDA的需要把CPU_ONLY := 1前的#号去掉，安装Anaconda的用户注意其文件名为~/anaconda2
7. 编译：

   make all
   make test
   make runtest

    

8. 没有错误即成功

【21天实战Caffe】学习笔记（一）Ubuntu16.04+Caffe环境搭建