【深度学习环境搭建】ubuntu16.04+python2.7+opencv3.1+caffe0.15

最新推荐文章于 2023-04-30 22:39:26 发布

原创最新推荐文章于 2023-04-30 22:39:26 发布 · 576 阅读

3 ·

CC 4.0 BY-SA版权

文章标签：

#深度学习 #搭建环境

深度学习同时被 2 个专栏收录

3 篇文章

订阅专栏

搭建环境

1 篇文章

订阅专栏

本文详细介绍了在阿里云平台上搭建深度学习环境的过程，包括选择合适的服务器配置、安装CUDA和cuDNN、配置Ubuntu环境、安装OpenCV和Caffe，以及解决过程中遇到的各种问题。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

整理一下深度学习搭建环境遇到的坑，这次实验使用Fast-rcnn（caffe环境） Faster-rcnn（caffe环境）yolo(在C++环境，不需要搭建)算法，实验是识别汽车前后门板上的50多个小焊点。

感谢曾师兄、裴师兄、老莫为了实验的环境搭建所作出的贡献

关于实验平台

实验使用阿里云平台（没钱买服务器，按小时扣费那种），注意重要文件存在系统盘，数据盘每次登陆清空;
选择服务器类别，配置按自己的算法要求来，GPU选择了12G，显卡是Nvida M40，镜像选择ubuntu16.04，可视化桌面需要自己安装。不建议选择ubuntu16.04+nvida的镜像，因为它不会帮你禁用原来的显卡，会出现循环登录或屏幕显示有较大问题；
有钱还是自己配置好的显卡和GPU吧，实验室的话可以申请买服务器，阿里云真是个坑，平台使用一个月左右花了2000，对个人用户十分不友好，有时还会出现资源被占用的情况。如果使用服务器建议可现在淘宝购买优惠券，能省个几百块钱。深度学习应用图像处理对显卡要求比较高，大家根据自己的实际情况，尽量选择较好的，后面的训练才不会报溢出，而且训练时间比较短，我的Fast-rcnn用VGG16网络训练一次7个小时，时间成本非常大；
说一下GPU，网上有不少CPU训练的教程，实际CPU不能用来训练，甚至测试都不可以，只能用来跑demo，所以还是老老实实用GPU吧。

ubuntu16.04+CUDA8.0+cudnn5.0/6.0

Ubuntu建议用16.04，用最新的18.06经常会出现找不到包的情况，而且会出现各种奇奇怪怪的问题。还要注意CUDA CUDNN安装版本需对应。CUDA9.0对应CUDNN7.0，CUDA8.0对应CUDNN5.0、CUDNN6.0，两个都装了，需要的时候更换软连接就好。
CUDA8.0的安装参考 https://blog.youkuaiyun.com/jonms/article/details/79318566 ，这一部分是老莫安装的所以在此不详写，以下是安装cudnn步骤。

在官网 https://developer.nvidia.com/cudnn 下载cudnn安装包

在include的文件夹复制头文件

sudo cp cudnn.h /usr/local/cuda/include/

在lib64文件夹复制动态链接库
```
sudo cp lib* /usr/local/cuda/lib64/  
```

删除原有动态文件

cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.5

生成新的软衔接

sudo ln -s libcudnn.so.6.0.21 libcudnn.so.6 
sudo ln -s libcudnn.so.6 libcudnn.so

安装成功

python2.7+opencv3.1

caffe基于opencv环境的，我不确定仅仅安装python-opencv是否有用，建议大家还是老实安装opencv比较好。这里说一下不建议用anaconda安装opencv。anaconda会隐藏一些系统路径，导致一些包你安装在系统里，一些装在anaconda上，出现找不到这种模块的问题，最后安装不成功。用C编译是最稳妥并且之后使用不太容易出错。
安装教程参考 https://www.pyimagesearch.com/2016/10/24/ubuntu-16-04-how-to-install-opencv/，以下归纳所有步骤，并对一些关键点做出备注。

步骤1：在Ubuntu 16.04上安装OpenCV依赖项

更新软件包

sudo apt-get update
sudo apt-get upgrade

安装开发人员工具

sudo apt-get install build-essential cmake pkg-config

安装图像调用I/O库

sudo apt-get install libjpeg8-dev libtiff5-dev libjasper-dev libpng12-dev

安装视频流访问包

sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev

安装GUI模块依赖库
```
sudo apt-get install libgtk-3-dev
```

安装优化opencv内部功能的库

sudo apt-get install libatlas-base-dev gfortran

安装opencv支持库
```
sudo apt-get install python2.7-dev
```

步骤2：下载opencv源代码

下载opencv源代码并解压

cd ~
wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.1.0.zip
unzip opencv.zip

下载opencv_contrib存储库

wget -O opencv_contrib.zip https://github.com/Itseez/opencv_contrib/archive/3.1.0.zip
unzip opencv_contrib.zip

步骤3：设置python环境-python2.7

安装pip——python包管理器

cd ~
wget https://bootstrap.pypa.io/get-pip.py
sudo python get-pip.py

安装Numpy
```
pip install numpy
```
注意在Fast r-cnn中使用numpy==1.10,这里安装高版本的需要在训练Fast r-cnn时更改其python文件的部分代码
使用cmake安装opencv
结合 https://blog.kickview.com/building-a-digits-dev-machine-on-ubuntu-16-04/ 根据自己运行的需求更改编译内容
```
cd ~/opencv-3.1.0/
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE\
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D WITH_CUDA=ON \
-D ENABLE_FAST_MATH=1 \
-D CUDA_FAST_MATH=1 \
-D WITH_CUBLAS=1\
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH= ~ /opencv_contrib-3.1.0/modules \
-D BUILD_opencv_dnn=OFF \
-D BUILD_EXAMPLES=ON \
-D PYTHON_EXECUTABLE= ~/.virtualenvs/cv/bin/python ..
```
注意opencv_dnn=OFF，安装caffe包括dnn，如重复在opencv会出现问题, WITH_CUDA=ON使用CUDA8.0，PYTHON_EXECUTABLE使用系统里的python。运行后检查图示。

检查是否支持cuda OpenCL 等，检查python2的路径是否正确（图示在虚拟环境里，根据自己的安装目录检查）等。
编译
```
make -j8
```
安装
```
sudo make install
sudo ldconfig
```

步骤4：完成安装，进行测试

成功出现cv2版本号3.1.0即为正确安装

cd ~
workon cv
python
import cv2
cv2.__version__

caffe安装

参考 https://blog.youkuaiyun.com/ZWX2445205419/article/details/72673121 及 https://blog.kickview.com/building-a-digits-dev-machine-on-ubuntu-16-04/ 关于caffe安装这部分

首先安装以下依赖库

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install -y build-essential cmake git pkg-config
sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install -y libatlas-base-dev 
sudo apt-get install -y --no-install-recommends libboost-all-dev
sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev
(Python general)
sudo apt-get install -y python-pip
(Python 2.7 development files)
sudo apt-get install -y python-dev
sudo apt-get install -y python-numpy python-scipy
(OpenCV 2.4)
sudo apt-get install -y libopencv-dev

下载caffe源文件

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

给执行文件改名

cp Makefile.config.example Makefile.config

配置Makefile.config
依据个人安装路径情况进行配置

## 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 # 使用CUDNN

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1 #使用GPU不更改

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0  #使用opencv不更改
# 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 #安装的OpenCV版本为3.1.0

# 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-8.0 # 更改为安装的cuda路径
# 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 := /Applications/MATLAB_R2012b.app
MATLAB_DIR := /usr/local/MATLAB/R2014a # matlab的安装路径，没有则注释掉

# 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 \ # 使用系统自带Python库和安装好的numpy库
   /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)/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 #使用python支持，去掉注释

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial /usr/lib/x86_64-linux-gnu/hdf5/serial #注意添加路径是否正确
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

# 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 ?= @

准备caffe支持python所需的库

cd ~/caffe/python
for req in $(cat requirements.txt); do pip install $req; done

开始编译
```
cd ~/caffe 
mkdir build
cd build
cmake ..
make all -j8 
make install
make test
make runtest -j8
```
与opencv相似，检查支持的库及路径是否正确。包括检测到的CUDA版本，cudnn版本，opencv版本，numpy版本，各种支持的版本，python配置路径
配置caffe的python接口
```
make pycaffe
```
安装过程与caffe类似，不赘述

安装完成，在python路径中添加caffe路径

export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH

测试
```
Import caffe
```