Ubuntu16.04+cuda9.0+cudnn7.4.1+opencv3.4.0+anaconda3(python3.6)+caffe

最新推荐文章于 2019-10-13 02:23:51 发布

转载最新推荐文章于 2019-10-13 02:23:51 发布 · 356 阅读

本文介绍如何在Ubuntu16.04上配置Caffe深度学习框架，包括安装基本环境、依赖库，配置及编译Caffe等步骤。特别针对CUDA9.0、cuDNN7.4.1和OpenCV3.4.0版本进行了详细说明。

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Ubuntu16.04+cuda9.0+cudnn7.4.1+opencv3.4.0+anaconda3(python3.6)+caffe

0.基本环境配置

操作系统：Ubuntu16.04

英伟达驱动：nvidia384.130（安装）

CUDA版本：cuda9.0(安装)

cnDNN版本：cudnn7.4.1(安装)

OpenCV版本：opencv3.4.0(安装)

Anaconda版本：anaconda3（python3.6）（安装）

安装cuda要注意nvidia版本！

1.基本依赖库安装

终端输入：

sudo apt install caffe-cuda

安装依赖：

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
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

2.配置caffe

2.1clone caffe源码

本文caffe安装目录为：

usr/local/

cd 进入安装目录并git：

git clone https://github.com/BVLC/caffe.git

2.2配置Makefile.config文件

进入caffe文件夹，复制更改Makefile.config文件：

cd /usr/local/caffe
sudo cp Makefile.config.example Makefile.config
sudo gedit Makefile.config

更改配置如下：

## 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_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
BLAS := open
# 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
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_python-py35 python3.6m
#这一段可删除#（注意：check the boost_python version library in /usr/lib/x86_64-linux-gnu/. For example, if there exists libboost_python-py34.so in this directory, uncomment and modify the line of PYTHON_LIBRARIES as PYTHON_LIBRARIES := boost_python-py34 python3.6m，本文用boost_python-py35）
# 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 /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 $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
LINKFLAGS := -Wl,-rpath,$(HOME)/anaconda3/lib

2.3. 配置Makefile文件

进入caffe文件夹中，并执行。终端输入：

cd /usr/local/caffe
sudo gedit Makefile

修改如下：（可以ctrl+f进行搜索）

PYTHON_LIBRARIES ?= boost_python python2.7
修改为：
PYTHON_LIBRARIES ?= boost_python-py35 python3.6m

NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)
修改为：
NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)

将：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
改为：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial

2.4编译

cd到caffe目录，并编译

cd /usr/local/caffe
sudo make clean
sudo make all -j12

在终端执行：

sudo make test -j12

成功之后输入：

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{path to your anaconda}/anaconda3/lib:{path to your anaconda}/anaconda3/pkgs/python-3.6.1-2/lib

运行测试：

sudo make runtest -j12

成功：

成功之后会显示PASSED，继续进入caffe下python目录，修改requirements.txt里为python-dateutil>=2.0：

cd /usr/local/caffe/python
sudo gedit requirements.txt

修改成功后在当前python目录执行执行：

for req in $(cat requirements.txt); do pip install $req; done

最后对pycaffe配置：

cd /usr/local/caffe
sudo make pycaffe

成功：

3.测试caffe

3.1更改环境变量

sudo gedit /etc/profile

加入：export PYTHONPATH={path to your caffe}/caffe/python/:$PYTHONPATH

本文中为：export PYTHONPATH=/usr/local/caffe/python/:$PYTHONPATH

source一下使之生效：

source /etc/profile

3.2测试

python
import caffe

成功：

4.Error

1.cannot find -lboost_python-py36

按照2.2和2.3的配置文件进行修改，ubuntu自带的boost_1.58.0没有对python3.6m进行编译，所以在/usr/lib/x86_64-linux-gnu/. 不存在 libboost_python-py36.so ，只能将Makefile.config中改为：PYTHON_LIBRARIES := boost_python-py35 python3.6m

2.Warning!***HDF5 library version mismatched error *** python pandas...

系统的HDF5与Anaconda中冲突，解决方法：

conda install -c anaconda hdf5=1.8.15

3.略...

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