ubuntu系统下配置caffe的GPU环境，训练mobileNet-SSD并在EAIDK-310嵌入式上推理
- 前言
- 一、NVIDIA显卡驱动及KUDA的安装
-
- 1.安装gpu驱动
- 2.安装cuda和cudnn
- 二、ubuntu安装GPU版caffe
-
- (一) 安装依赖库:
- (二) 下载源码并切换到 ssd 分支
- (三) 修改配置文件
-
- 1.修改Makefile.config文件
- 2.修改Makefile文件
- (四) 编译及测试caffe
- 三、anaconda3环境下安装gpu版caffe
-
- (一) ubuntu系统安装anaconda3
-
- 1.下载及安装
- 2.创建虚拟环境
- (二) 虚拟环境下安装caffe
- 四、caffe环境下训练ＭobileNet—SSD
前言
如果没有安装好ubuntu系统或者系统崩溃需要重装的可以参考上一篇博客：ubuntu+win10双系统安装。
一、NVIDIA显卡驱动及KUDA的安装
1.安装gpu驱动
这里推荐一个简单的安装方法，打开菜单，找到软件和更新，
找到附加驱动，这里会自动推荐适合GPU的驱动，
选择使用NVIDIA驱动，点击右下角的应用更改，系统会自动安装显卡驱动并重启。
若此方法失败，则可参考网上安装gpu驱动的教程，教程有很多，这里不再赘述。
验证是否成功安装：nvidia-smi
nvidia-smi
显示如下图则说明安装成功，注意版本，我这里是Driver Version: 384.130
2.安装cuda和cudnn
版本问题
cada: cuda_8.0.44_linux.run
cudnn:cudnn-8.0-linux-x64-v5.1.tgz
- 如没有使用此版本以及上面的显卡驱动版本，不保证能配置成功。本人经过长达两周的时间摸索此对应的版本。推荐CUDA官网，cudnn的安装下载，若有需要，可参考百度云。
- 至于安装教程，网上很多，这里推荐一篇教程。
- 查看是否安装成功：nvcc -V
二、ubuntu安装GPU版caffe
(一) 安装依赖库:
1.安装指令如下：
sudo apt-get install libprotobuf-dev //（16.04默认版本2.6.1，不能升级）
sudo apt-get install libleveldb-dev
sudo apt-get install libsnappy-dev
sudo apt-get install libopencv-dev
sudo apt-get install libhdf5-serial-dev
sudo apt-get install protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libatlas-base-dev
sudo apt-get install python-dev
sudo apt-get install libgflags-dev
sudo apt-get install libgoogle-glog-dev
sudo apt-get install liblmdb-dev
2.特别注意
本人安装时python版本是Ubuntu16.04自带的Python2.7，如果需要其他python版本，建议参考后面第三节anaconda虚拟环境下的安装。
安装完成后确认python版本,直接输入python：
python
这里我只有用opencv2.4.9.1才编译成功，及Ubuntu16.04默认安装的版本。
安装完成后确认opencv版本,接着输入：
import cv2
cv2.__version__
(二) 下载源码并切换到 ssd 分支
1.下载码源
在主目录下(我的是/home/xxx,xxx为用户名)打开命令窗口，依次输入：
下载github上的caffe项目：
git clone https://github.com/weiliu89/caffe.git
若后面编译不成功，也可以下载这里编译好的caffe。
切换到caffe文件夹：
cd caffe
2.切换到 ssd 分支
git checkout ssd
3.安装python依赖库
切换到 caffe 下的 python 目录下，下载 python 依赖库(先安装 pip)
cd python
sudo apt-get install python-pip
for req in $(catpython requirements.txt); do pip install $req; done
(三) 修改配置文件
（针对python2.7,opencv2.4.9.1d的环境修改）
1.修改Makefile.config文件
先将Makefile.config.example复制并重命名为Makefile.config
复制指令如下：
cp Makefile.config.example Makefile.config
然后修改Makefile.config文件，修改后如下：
## 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
# 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 lines after *_35 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_61,code=sm_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/local/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)/anaconda2
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python2.7 \
$(ANACONDA_HOME)/lib/python2.7/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 \
/usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib \
/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
# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
2.修改Makefile文件
（这里代码有点多，避免一行行的去查找修改，这里直接给出修改好的Makefile文件。）
(四) 编译及测试caffe
- 在caffe目录下编译：
make pycaffe -j8
make all -j8
make test -j8
make runtest -j8
时间较长，请耐心等待，-j8是指多核同时编译，可节省时间，根据自己电脑设置-j**。出现pass则编译成功。
- 测试：
添加环境变量，注意caffe的路径：
cd caffe/python
export PYTHONPATH=~/caffe/python:$PYTHONPATH
进入 python 环境，输入：import caffe 如果没有报错，证明安装成功。若编译通过但是导入失败，则make pycaffe一下。
- 导入环境变量：
sudo echo export PYTHONPATH=“~/caffe/python" >>~/.bashrc
source ~/.bashrc #使环境变量生效
或者：
echo "export CAFFE_ROOT=~/caffe" >> ~/.bashrc
echo "export PYTHONPATH=${CAFFE_ROOT}/python:$PYTHONPATH" >> ~/.bashrc
source ~/.bashrc
如出错参考链接 。
重新打开一个终端窗口，输入python，再输入caffe，不出错则添加环境变量成功。
三、anaconda3环境下安装gpu版caffe
(一) ubuntu系统安装anaconda3
（因为这里主要介绍anaconda3下的caffe环境配置，至于anaconda3的安装，不做过多的赘述。）
1.下载及安装
下载：anaconda安装包下载，推荐官网，或者百度云，提取码：zu0i。
安装：网上很多教程，这里推荐一篇。
是否安装成功：安装完成后需要添加环境变量，查看环境变量终端输入：
sudo gedit ~/.bashrc
打开.bashrc后看最后是否有如下字样代码
## >>> conda initialize >>>
## !! Contents within this block are managed by 'conda init' !!
__conda_setup="$('/home/×××/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; then
eval "$__conda_setup"
else
if [ -f "/home/×××/anaconda3/etc/profile.d/conda.sh" ]; then
. "/home/×××/anaconda3/etc/profile.d/conda.sh"
else
export PATH="/home/×××/anaconda3/bin:$PATH"
fi
fi
unset __conda_setup
## <<< conda initialize <<<
若没有则复制进去，注意×××是自己的用户名。
安装完成后，终端命令行前出现默认环境(base)，输入conda不报错说明安装成功。
2.创建虚拟环境
- 创建名为
caffe的python2.7的虚拟环境并切换至该环境：
conda create -n caffe python=2.7
conda activate caffe
(二) 虚拟环境下安装caffe
-
安装依赖文件如第二节所示，但是我遇到的问题是
sudo apt-get安装的opencv库在conda虚拟环境下无法调用，重新编译安装的opencv库与我想安装的caffe环境又有冲突，导致caffe编译失败。
这里给出我的解决方法：
将usr/lib/python2.7/dist-packages目录下的cv.py,cv.pyc和cv2.x86_64-linux-gnu.so三个文件复制到anaconda3/envs/caffe/lib/python2.7/dist-packages文件夹下，并将cv2.x86_64-linux-gnu.so改名为cv2.so
至此，caffe下的cv2版本才符合要求，如图所示：
-
下载码源（与前面一致）
在主目录下(我的是/home/xxx/my_code,xxx为用户名)打开命令窗口，依次输入：
下载github上的caffe项目：git clone https://github.com/weiliu89/caffe.git切换到caffe文件夹：
cd caffe -
切换到 ssd 分支
git checkout ssd
- 安装python依赖库
切换到 caffe 下的 python 目录下，下载 python 依赖库(先安装 pip)
cd python
sudo apt-get install python-pip
for req in $(catpython requirements.txt); do pip install $req; done
- 修改配置文件
首先是Makefile.config文件，将python的路径改成anaconda下虚拟环境的python，其他的不变。
修改后如下：
## 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
# 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 lines after *_35 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_61,code=sm_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/local/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/lf/anaconda3/envs/caffe
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python2.7 \
$(ANACONDA_HOME)/lib/python2.7/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 := $(ANACONDA_HOME)/lib
LINKFLAGS := -Wl,-rpath,$(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/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
# 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 ?= @
其次是Makefile文件，代码比较多，避免一行行的去查找修改，这里直接给出修改好的Makefile文件。）
剩下的编译过程也是完全一样，请参考前面部分。
至此，anconda下的caffe环境就安装好了。
四、caffe环境下训练ＭobileNet—SSD
本人安装ubuntu下的caffe环境主要是利用自己的数据集训练ＭobileNetSSD model，这部分可以参考链接。
最后，将训练的模型.caffemodel模型文件转化成.tmfile文件，在eaidk-310平台上做推理，这部分内容可以参考下一篇博客。
