不多说了，经历了很多莫名其妙的错误最后终于安装好了，直接放安装脚本：
#!/bin/bash #安装时要注意有些库可能安装失败以及安装caffe有和protobuf相关错误时可能需要重新对protobuf进行make install cd /home/zw/softwares #需要事先下载对应版本的cuda sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb sudo apt-get update sudo apt-get install cuda cd /home/zw/git_home/ #我存放git项目的目录 git clone https://github.com/google/protobuf.git sudo apt-get install autoconf automake libtool curl make g++ unzip cd protobuf ./autogen.sh ./configure --prefix=/usr make -j8 make check -j8 sudo make install -j8 sudo ldconfig # refresh shared library cache. cd /home/zw/git_home/ git clone https://github.com/BVLC/caffe.git cd caffe 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 libgflags-dev libgoogle-glog-dev liblmdb-dev cp Makefile.config.example Makefile.config #config中如果启用anaconda目录改成anaconda2(安装时默认名称),否则sudo make pycaffe无法编译成功。不过建议不需要启用anaconda目录，因为没这个必要，后续只要在PYTHONPATH路径中加入caffe和安装protobuf即可。另外，如果事先安装了opencv3.0需要在Makefile.cinfig中修改对应选项 read -rsp $'更改你的Makefile.config, 完成后Press any key to continue... ' -n1 key make all -j8 make test -j8 make runtest make pycaffe -j8 cd /home/zw/git_home/protobuf/python ~/anaconda2/bin/python setup.py install #安装对应版本的protobuf，这里要特别注意，如果使用conda安装最新版本的protobuf，可能出现不兼容问题的，因为上面的caffe是用这个版本的protobuf编译的，切记！这里是我自己尝试出来的，花了不少时间 #echo "export PYTHONPATH=~/git_home/protobuf/python:$PYTHONPATH" >> ~/.bashrc #如果你用的时zsh，那么应该导入到～/.zshrc echo "export PYTHONPATH=~/git_home/caffe/python:$PYTHONPATH" >> ~/.bashrc echo "export PATH=~/git_home/caffe/build/tools:$PATH" >> ~/.bashrc
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 # 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 #事先安装了使用了opencv3，这里要启用 # 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 #使用了cuda，这里要启用 # 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 #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 # 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 ?= @