21天实战Caffe学习笔记Ubuntu16.04+Caffe环境搭建

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  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 youre 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 its 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

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