• Caffe ubuntu编译


    一、安装环境

    系统:Ubuntu 16.04 x64_64

    CUDA: 7.5
    cudnn: 7.5

    General dependencies:

    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

    OpenBLAS:

    sudo apt-get install libatlas-base-dev libopenblas-dev

    Python:

    https://conda.io/miniconda.html 下载 Miniconda python 2.7 并安装。
    待安装成功后,安装dumpy: 启动终端,输入
    conda install dumpy

    Remaining dependencies:

    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

    二、编译

      进入到caffe目录,拷贝Makefile.config.example文件到Makefile.config,内容如下:

    ## 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
    
    # 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)/Miniconda2
    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 
    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
    
    # 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 ?= @
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  • 原文地址:https://www.cnblogs.com/ziyu-trip/p/6885985.html
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