caffe安装：基于anaconda3---python3.6,

by · 2019年11月06日 · 2633 Words · ~6min reading time | Improve on


原文链接： caffe安装：基于anaconda3---python3.6,

Ubuntu 16.04 在Conda沙盒环境下安装Caffe(Python2.7.15 + Protobuf2.6.1 + GPU) - 世界很大 - CSDN博客

export GOROOT=/usr/local/go
export GOPROXY=https://goproxy.io
export PATH=.:$PATH:$GOROOT/bin:$GOPATH/bin
#cuda9.0
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda

### add caffe yolov3
export PYTHONPATH="/home/ubuntu/caffe/models/twmht_caffe/distribute/python:$PYTHONPATH"
export LD_LIBRARY_PATH="/home/ubuntu/caffe/models/twmht_caffe/distribute/lib:$LD_LIBRARY_PATH"

####### add protobuf lib path ########
#(动态库搜索路径) 程序加载运行期间查找动态链接库时指定除了系统默认路径之外的其他路径
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/protobuf/lib/
#(静态库搜索路径) 程序编译期间查找动态链接库时指定查找共享库的路径
export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/protobuf/lib/
#执行程序搜索路径
export PATH=$PATH:/usr/local/protobuf/bin/
#c程序头文件搜索路径
export C_INCLUDE_PATH=$C_INCLUDE_PATH:/usr/local/protobuf/include/
#c++程序头文件搜索路径
export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/usr/local/protobuf/include/
#pkg-config 路径
export PKG_CONFIG_PATH=/usr/local/protobuf/lib/pkgconfig

# 2019年 11月 22日 星期五 09:33:06 CST
# HuaWei LiteOS Linux, Cross-Toolchain PATH
export PATH="/opt/hisi-linux/x86-arm/arm-himix200-linux-v2/bin:$PATH"
export PATH="/opt/hisi-linux/x86-arm/arm-himix200-linux/bin:$PATH"
#

export PYTHONPATH="/home/ubuntu/caffe/models/chuanqi305/build/python:$PYTHONPATH"
export LD_LIBRARY_PATH="/home/ubuntu/caffe/models/chuanqi305/build/lib:$LD_LIBRARY_PATH"

~/caffe/models/chuanqi305

export PYTHONPATH="/home/ubuntu/caffe/models/weiliu89/distribute/python:$PYTHONPATH"
export LD_LIBRARY_PATH="/home/ubuntu/caffe/models/weiliu89/distribute/lib:$LD_LIBRARY_PATH"

caffe安装
安装Anaconda3
下载：Anaconda3-5.0.1-Linux-x86_64.sh
默认路径安装（最终安装位置为/home/usename此处自己的用户名/anaconda3）
安装：./Anaconda3-5.0.1-Linux-x86_64.sh

下载caffe
首先安装依赖
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compilersudo
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libatlas-base-dev
sudo apt-get install libhdf5-serial-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
下载caffe源码：
git clone https://github.com/BVLC/caffe.git

（参考：https://www.jianshu.com/p/5afdb561ce94）

配置caffe的Makefile.config
cd到caffe目录，复制一份Makefile.config：cp Makefile.config.example Makefile
由于是基于anaconda和cpu，修改内容如下（默认路径安装的话可直接复制）：

## 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
# This code is taken from https://github.com/sh1r0/caffe-android-lib
# USE_HDF5 := 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/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)/miniconda3/envs/caffe-py2.7.15-pr2.6.1
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		$(ANACONDA_HOME)/include/python2.7 \
		$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include

ANACONDA_HOME := $(HOME)/anaconda3
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
          $(ANACONDA_HOME)/include/python3.6m \
          $(ANACONDA_HOME)/lib/python3.6/site-packages/numpy/core/include
PYTHON_LIBRARIES := boost_python3 python3.6m

### miniconda base envs
# ANACONDA_HOME := $(HOME)/anaconda3
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
#          $(ANACONDA_HOME)/include/python3.7m \
#          $(ANACONDA_HOME)/lib/python3.7/site-packages/numpy/core/include
# PYTHON_LIBRARIES := boost_python3 python3.7m

# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.6m
# PYTHON_INCLUDE := /usr/include/python3.6m \
#                 /usr/lib/python3.6/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
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_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)
# 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 ?= @

安装libboost（基于python3.6）的库
wget -O boost_1_55_0.tar.gz http://sourceforge.net/projects/boost/files/boost/1.55.0/boost_1_55_0.tar.gz/download

tar xzvf boost_1_55_0.tar.gz

cd boost_1_55_0/

./bootstrap --with-libraries=python --with-toolset=gcc

./b2 --with-python include="/home/usename(自己的用户名)/anaconda3/include/python3.6m/"

sudo ./b2 install
此时，/usr/local/lib中已增加了关于boost的动态库和静态库，建立软链接：

cd /usr/local/lib

sudo ln -s libboost_python36.so libboost_python3.so

sudo ln -s libboost_python36.a libboost_python3.a

编译caffe

sudo make all -j8
sudo make test -j8
sudo make pycaffe -j8

（补充：在sudo make test -j8后运行 sudo make runtest -j8时出错，关于CPU_Device(float)的错误，查了很多，也试了多个版本的boost还是没能解决，如果你知道解决方案麻烦提供一下呀～。
不过不影响caffe的正常使用）
进入python环境：python

import caffe
出错：提示找不到google模块
利用网上的办法pip install protobuf-py3后，又报错，找不到symbol_database，在网上下载symbol_database.py后仍然报错。
解决方案：卸载protobuf：pip uninstall protobuf-py3
conda环境下安装protobuf：

conda install protobuf
再次

import caffe
成功！！！

一。makefile.config修改文件

## 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.
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 := /usr/local/MATLAB/R2016b


# 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.

PYTHON_LIBRARIES := boost_python3 python3.6m

 ANACONDA_HOME := $(HOME)/anaconda3
 PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python3.6m \
$(ANACONDA_HOME)/include/pythonsudo apt-get install liblapack-dev3.6 \
$(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
#PYTHON_LIBRARIES := boost_python3 python3.6m
#PYTHON_INCLUDE := /home/zhao/anaconda3/include/python3.6m \
#                 /home/zhao/anaconda3/lib/python3.6/site-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
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_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)
# 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 ?= @

二。Cannot build caffe with anaconda. Error: while loading shared libraries: libhdf5_hl.so.10

sudo cp -s $HOME/anaconda2/lib/libhdf5_hl.so.100.0.1 /usr/lib/libhdf5_hl.so.100

sudo cp -s $HOME/anaconda2/lib/libhdf5_hl.so.100.0.1 /usr/lib/x86_64-linux-gnu/libhdf5_hl.so.100

sudo cp -s $HOME/anaconda2/lib/libhdf5.so.101.0.0 /usr/lib/libhdf5.so.101

sudo cp -s $HOME/anaconda2/lib/libhdf5.so.101.0.0  /usr/lib/x86_64-linux-gnu/libhdf5.so.101

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版权声明：本文为CSDN博主「威武胖子哥」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_38362252/article/details/79769501

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