1:anaconda 包管理工具下載地址，找到想要下載的對應版本 copy 下載路徑
2.linux 下下載安裝，點擊下一步下一步，會提示你是不是把路徑放在環(huán)境變量里，回復yes放進去,回車
?wget?https://repo.anaconda.com/archive/Anaconda2-5.1.0-Linux-x86_64.sh
?bash ??Anaconda2-5.1.0-Linux-x86_64.sh
安裝過程中會需要不斷回車來閱讀并同意license。安裝路徑默認為用戶目錄(可以自己指定)，最后需要確認將路徑加入用戶的.bashrc中。
最后，立即使路徑生效，需要在用戶目錄下執(zhí)行：
source .basic
3.測試是否安裝,成功進入python界面看出來python版本則成功。
4.anaconda 包的使用
conda ?info ?--package 查看包的版本
conda ?list ?查看已有的包
conda ?install??--package 安裝包
conda ?install ?package=1.2.0 安裝對應的版本包
conda ? uninstall ?--package 卸載包
python 導入 caffe
1.修改caffe 路徑下的Makefile.config 包，注意標黑字體
## 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 := 1
# 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 := /usr/include
BLAS_LIB := /usr/lib64
# 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
#PYTHON_INCLUDE := /usr/include/python2.7 \
? ? ? ? ? ? ? ? /usr/lib64/python2.7/site-packages/numpy/core/include
#PYTHON_INCLUDE := /usr/local/python-3.6.1 \
? ? ? ? ? ? ? ? /usr/local/python-3.6.1/site-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
# 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 ?= @
2.配置好環(huán)境開始導入發(fā)現(xiàn)導入缺少包
make clean
make all -j8?
make pycaffe
pycaffe 報錯
以python 2.7為例，anaconda2 中缺少atlas,openblas ，opencv ?
解決辦法?
conda install?atlas
conda install?opencv
conda instala?openblas=2.6.1
注意：
python 2.7 ?不支持 libprotobuf ,libopenblas ?如果報錯請刪除。
