未完待续......
在虚拟机上安装好Ubuntu16.04后点击屏幕上端的工具栏，点击设备-->安装增强功能...-->运行，会出现终端自动执行命令，按提示点击回车
共享粘贴板：
点击 设备-->共享粘贴板-->双向
共享文件：
点击 设备-->共享文件夹-->共享文件夹... 出现如下窗口
重启系统，此时会出现名字为“sf_{共享文件夹}”的文件夹，这就是和主机共享的文件夹，但Ubuntu对此没有访问权限，想获取权限的话需打开终端，输入：
sudo usermod -a -G vboxsf {yourusernanme}重启即可获得访问共享文件的权限
下载后缀为.sh的anaconda3
cd到要安装anaconda3的路径下，终端输入：
sudo bash {filename}.sh一路安装下去。
在anaconda3环境下使用canda若出现permission denided的错误时，解决方法如下：
sudo chown -R {username}:{username} /home/{username}/anaconda3安装caffe:
首先安装依赖库：
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 libatlas-base-dev
sudo apt-get install --no-install-recommends libboost-all-dev建立pycaffe接口：
sudo apt-get install python-dev及相关依赖库
sudo apt-get install libgflags-dev
sudo apt-get install libgoogle-glog-dev
sudo apt-get install liblmdb-dev下载caffe源码：
首先安装git
sudo apt-get install git下载caffe源码：
git clone https://github.com/BVLC/caffe.git# cd caffe安装opencv
cd caffe
sudo git clone https://github.com/jayrambhia/Install-OpenCV
cd Install-OpenCV/Ubuntu
sudo bash dependencies.sh
cd 2.4
sudo sh opencv2_4_10.sh进入caffe目录：
cd ~/caffe
生成Makefile.config文件：
cp Makefile.config.example Makefile.config
sudo gedit Makefile.config
修改内容如下：
## 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.
# 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/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)/anaconda3
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python3.6m \
$(ANACONDA_HOME)/lib/python3.6m/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 ?= @编译pycaffe
cd caffe/python
for req in $(cat requirements.txt); do pip install -y $req; done
cd ..
make pycaffe编译caffe:
sudo make all -jx (x为处理器个数)
sudo make test -j2
sudo make runtest -j2执行make runtest时如果报错：
.build_release/tools/caffe:error whileloading shared libraries: libhdf5_hl.so.100: cannot open sharedobject file: Nosuch file or directory注意这里报错的库文件（libhdf5_hl.so.100）在不同时期的caffe上可能有所差别，要根据自己报错的库文件做修改。Anaconda自带的库能找到相同的库文件libhdf5_hl.so.100的，这是一个软链指向了libhdf5_hl.so.10.0.1这个文件。因此参考这个issues后，可以在 /usr/lib 及/usr/lib/x86_64-linux-gnu 分别放了一个软链指向了Anaconda的库中libhdf5_hl.so.10.0.1。
解决方法如下：
sudo cp -s $HOME/anaconda3/lib/libhdf5_hl.so.100.0.1 /usr/lib/libhdf5_hl.so.100
sudo cp -s $HOME/anaconda3/lib/libhdf5_hl.so.100.0.1 /usr/lib/x86_64-linux-gnu/libhdf5_hl.so.100若再出现：
.build_release/tools/caffe:error whileloading shared libraries: libhdf5.so.101: cannot open shared objectfile: Nosuch file or directory解决方法如下：
sudo cp -s $HOME/anaconda3/lib/libhdf5.so.101.0.0 /usr/lib/libhdf5.so.101
sudo cp -s $HOME/anaconda3/lib/libhdf5.so.101.0.0 /usr/lib/x86_64-linux-gnu/libhdf5.so.101若出现报错：
BenchmarkTest/0.TestTimerMilliSeconds, where TypeParam = caffe::CPUDevice或者
BenchmarkTest/1.TestTimerMilliSeconds, where TypeParam = caffe::CPUDevice解决方法，在当前路径下：
export MKL_CBWR=AUTO