caffe的安装笔记 Ubuntu16.04

跑实验用过一次caffe，光安装就用了一周，经历了各种错误，真的好难安装，记录下最后成功安装的方法，希望给大家安装时提供参考。

1、下载安装所需的包，例如anaconda,numpy ,protobuf,opencv，cython, scikit-image等

opencv使用

conda install opencv

直接使用 conda install caffe-gpu安装好所需的环境再使用源码编译，进行下面的步骤

再 使用conda uninstall caffe-gpu 去掉这个包，装上需要的环境，但是这个包不能直接使用（我也不知道原因）。

2、下载caffe

git clone 

3、更改makefile.config

使用GPU即使用cudnn，则去掉下一行的注释

USE_CUDNN := 1

USE_OPENCV := 0OPENCV_VERSION := 3

CUDA_DIR := /usr/local/cuda_env/cuda-8.0

ANACONDA_HOME := $(HOME)/anaconda3/envs/caffePYTHON_INCLUDE := $(ANACONDA_HOME)/include \ $(ANACONDA_HOME)/include/python3.6m\ $(ANACONDA_HOME)/lib/python3.6/site-packages/numpy/core/includePYTHON_LIB := $(ANACONDA_HOME)/lib

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serialLIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial/

完整的makefile.config

## Refer to  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 layersUSE_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 3OPENCV_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_env/cuda-8.0# 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/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/envs/caffePYTHON_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/libPYTHON_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/serialLIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /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)#  (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  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 ?= @

4、更改makefile,更改这一行

LIBRARIES += opencv_core opencv_highgui opencv_imgproc opencv_imgcodecs

5、编译caffe

cd caffemake all -j8make pycaffe

6、更改.bashrc路径

export PYTHONPATH="/home/usrname/caffe/python:$PYTHONPATH"

7、完整安装caffe路径(这个私人设置，不做参考）

# add for use caffe copy from jhyangexport LD_LIBRARY_PATH="/usr/local/cuda_env/cuda-8.0/lib64:/usr/local/cuda_env/cudnn-v5.1-for-cuda8.0/lib64:/usr/local/cuda_env/cudnn-v6.0-for-cuda8.0/lib64:/usr/local/cuda_env/cuda-9.0/lib64:/usr/local/cuda_env/cudnn-v7.0-for-cuda9.0/lib64"export PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/usr/bin:/bin:/usr/sbin:/sbin"export LIBRARY_PATH="/usr/local/cuda_env/cuda-8.0/lib64:/usr/local/cuda_env/cudnn-v5.1-for-cuda8.0/lib64:/usr/local/cuda_env/cudnn-v6.0-for-cuda8.0/lib64:/usr/local/cuda_env/cuda-9.0/lib64:/usr/local/cuda_env/cudnn-v7.0-for-cuda9.0/lib64"export PYTHONPATH="/home/usr/caffe/python:$PYTHONPATH"export CPATH=$CPATH:/usr/local/cuda_env/cudnn-v6.0-for-cuda8.0/include:/usr/include/hdf5/serial/hdf5.hexport CPATH=/usr/local/cuda_env/cudnn-v5.1-for-cuda8.0/include:/usr/local/cuda_env/cudnn-v6.0-for-cuda8.0/include:/usr/local/cuda_env/cudnn-v7.0-for-cuda9.0/include

8、测试使用

import caffe