ubuntu14.04 Anaconda 的安裝使用

利用anaconda來配置python環境

先到https://www.continuum.io/downloads 下載anaconda, 現在的版本有python2.7版本和python3.6版本，下載好對應版本、對應系統的anaconda，它實際上是一個sh腳本文件，大約500M左右。我下載的是linux版的python 2.7版本。

下載成功後，在終端執行（2.7版本）：

# bash Anaconda2-2.4.1-Linux-x86_64.sh

在安裝的過程中，會問你安裝路徑，直接回車默認就可以了。有個地方問你是否將anaconda安裝路徑加入到環境變量（.bashrc)中，這個一定要輸入yes

安裝成功後，會有當前用戶根目錄下生成一個anaconda2的文件夾，裏面就是安裝好的內容。

輸入conda list 就可以查詢，你現在安裝了哪些庫，常用的numpy, scipy名列其中。如果你還有什麼包沒有安裝上，可以運行

conda install ***  來進行安裝，

如果某個包版本不是最新的，運行 conda update *** 就可以了。

首次conda list 出現找不到這個命令的錯誤：在終端輸入export PATH=~/anaconda2/bin：$PATH

編譯python接口

首先，將caffe根目錄下的python文件夾加入到環境變量

打開配置文件bashrc，在最後面加入

export PYTHONPATH=/home/xxx/caffe/python:$PYTHONPATH

sudo ldconfig

因爲之前配置好的caffe，，不能再繼續執行sudo make與sudo make python等指令，，首先運行

sudo make clean
sudo make pycaffe clean
sudo make test clean
sudo make runtest clean

然後修改Makefile.config文件，下邊是我的

## 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 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_50,code=compute_50

# 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 /usr/include/hdf5/serial/
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

# 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 ?= @

然後執行

sudo make 
sudo make pycaffe 
sudo make test 
sudo make runtest 

sudo make runtest 沒編譯過去，也不是必須的，先不管了。。

最終查看python接口是否編譯成功：

進入python環境，進行import操作

python
import caffe

如果沒有提示錯誤，則編譯成功

安裝jupyter

安裝了python還不行，還得安裝一下ipython，後者更加方便快捷，更有自動補全功能。而ipython notebook是ipython的最好展現方式。最新的版本改名爲jupyter notebook,我們先來安裝一下。（如果安裝了anaconda, jupyter notebook就已經自動裝好，不需要再安裝）

sudo pip install jupyter 提示sudo：pip：command not find

去掉sudo 執行，，成功安裝！！

原因是：我們知道在執行Linux命令時，如果在其前面加上sudo，就表示以root權限執行。但是這其實是有一個前提的，就是隻有那些Linux內置系統命令纔可以用如此的形式來執行，而對於Shell內置命令或其他用戶自定義命令、別名等，是不能用sudo來使用root權限的。

$ jupyter notebook

就會在瀏覽器中打開notebook,  點擊右上角的New-python2, 就可以新建一個網頁一樣的文件，擴展名爲ipynb。在這個網頁上，我們就可以像在命令行下面一樣運行python代碼了。輸入代碼後，按shift+enter運行，更多的快捷鍵，可點擊上方的help-Keyboard shortcuts查看，或者先按esc退出編輯狀態，再按h鍵查看。

在jupyter裏輸入

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import caffe
caffe_root='/home/shan/caffe/'
import os,sys
os.chdir(caffe_root)
sys.path.insert(0,caffe_root+'python')
im = caffe.io.load_image('examples/images/cat.jpg')
print im.shape
plt.imshow(im)
plt.axis('off')

報錯：No module named google.protobuf.internal

解決：sudo chmod 777 -R anaconda2

        conda install protobuf