caffe makefile.config anaconda2 python3 所有問題一種解決方式

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我只改了兩個數字，然後，所有錯誤，不翼而飛，兩天折騰，全是窮折騰。

事情是這樣的，除了官方說法，其他不帶官方doc的教程都是耍流氓。

有人說，官方說anaconda+python非常簡單好配置，為什麼，我這麼多錯誤，最後不得不用pip，因為官方配置文檔，就是makefile.config裡面是anaconda2+python2.7，如果你安裝的是以上版本，那你的確很簡單，但是舊版本是註定要被淘汰的，你看現在誰用windows xp？

沒有教程，或者沒有最新的針對anaconda3+python3.6的，中麼辦？

我告訴你，配置的時候只需按照你的anaconda安裝包裡面的路徑，原本的改了那個針對anaconda2的路徑即可，所有前置，所有版本，全都給你弄好了，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總之，你既然用了anaconda，你就要精確的告訴你的caffe去哪裡找我的庫，而不是瞎改，改完了出錯，到處去搜尋（是我）我看了那麼多doc，唯一一塊自由發揮，就把自己給坑了（的確看臉，可能最近照鏡子有點多）

最後附上我的config，我這片終極教程，是建立在你看了官網教程的基礎上的，配置最後的config時的。另外提醒一句，GPU cudnn要求你的顯卡加速在3以上，我的機子不到，而且bantu16.04要求裝cuda8.0,我裝了9.1。我顯然是好奇又傻大膽，在犯錯的邊緣試探，就愛嘗試最新版，等我裝回cuda8,再整個GPU版本的。

請注意cudnn與cuda是不一定一起的，具體看管網，配置的時候說了三種情況。

另外如果裝cudnn那麼請注意時差，對面工作時間非常準時，我們只能在早上還有晚上訪問觀望。其他時間都是維護。我就奇怪了，營運都請不起嗎？？？

## 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 OpenBlasBLAS := 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 := buildDISTRIBUTE_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 ?= @

最後，如果有問題歡迎留言，我現在還比較熟悉，你在晚點我就忘了。

另外，萬一火了，問得太多了，我就該高冷了（想太多）

 

caffe makefile.config anaconda2 python3 所有問題一種解決方式