caffe makefile.config anaconda2 python3 所有问题一种解决方式

我只改了两个数字，然后，所有错误，不翼而飞，两天折腾，全是穷折腾。

事情是这样的，除了官方说法，其他不带官方doc的教程都是耍流氓。

有人说，官方说anaconda+python非常简单好配置，为什么，我这么多错误，最后不得不用pip，因为官方配置文档，就是makefile.config里面是anaconda2+python2.7，如果你安装的是以上版本，那你的确很简单，但是旧版本是注定要被淘汰的，你看现在谁用windows xp？

没有教程，或者没有最新的针对anaconda3+python3.6的，中么办？

我告诉你，配置的时候只需按照你的anaconda安装包里面的路径，原本的改了那个针对anaconda2的路径即可，所有前置，所有版本，全都给你弄好了，你只要改了比如我的python在anaconda中的

 

那我需要把config中的python2.7改成3.6m总之，你既然用了anaconda，你就要精确的告诉你的caffe去哪里找我的库，而不是瞎改，改完了出错，到处去搜索（是我）我看了那么多doc，唯一一块自由发挥，就把自己给坑了（的确看脸，可能最近照镜子有点多）

最后附上我的config，我这片终极教程，是建立在你看了官网教程的基础上的，配置最后的config时的。另外提醒一句，GPU cudnn要求你的显卡加速在3以上，我的机子不到，而且bantu16.04要求装cuda8.0,我装了9.1。我显然是好奇又傻大胆，在犯错的边缘试探，就爱尝试最新版，等我装回cuda8,再整个GPU版本的。

请注意cudnn与cuda是不一定一起的，具体看管网，配置的时候说了三种情况。

另外如果装cudnn那么请注意时差，对面工作时间非常准时，我们只能在早上还有晚上访问观望。其他时间都是维护。我就奇怪了，运维都请不起吗？？？

## 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/python3.5 \
        /usr/lib/python3.6/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/python3.6/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
#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

# 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 ?= @

最后，如果有问题欢迎留言，我现在还比较熟悉，你在晚点我就忘了。

另外，万一火了，问得太多了，我就该高冷了（想太多）