Caffe - Ubuntu 安装及问题解决

tar zvxf  cudnn-8.0-linux-x64-v5.1.tgz 
cd cuda/
sudo cp lib64/lib* /usr/local/cuda/lib64/
sudo cp include/cudnn.h /usr/local/cuda/include/

- 建立软链接，并更新：    

cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.5
sudo ln -s libcudnn.so.5.1.10 libcudnn.so.5
sudo ln -s libcudnn.so.5 libcudnn.so
sudo ldconfig -v

ldd ./build/tools/caffe.bin  | grep cudnn

$ sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler libgflags-dev libgoogle-glog-dev liblmdb-dev libatlas-base-dev git
$ sudo apt-get install --no-install-recommends libboost-all-dev
$ sudo apt-get install libatlas-base-dev # 安装 ATLAS
$ sudo apt-get install libopenblas-dev # 安装OpenBLAS
$ sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

$ git clone https://github.com/NVIDIA/nccl.git
$ cd nccl
$ sudo make install -j4
$ sudo ldconfig

$ git clone https://github.com/BVLC/caffe.git
$ cd caffe/
$ cp Makefile.config.example Makefile.config

- 编辑 Makefile.config，进行修改：   

取消 USE_CUDNN := 1 的注释，启用 cuDNN 加速；
取消 USE_NCCL := 1 的注释，启用在多个 GPU 上运行 Caffe 所需的 NCCL

- 编译安装Caffe   

$ make all -j8
$ make test -j8
$ make pycaffe  # python API 
$ make matcaffe # matlab API，需定义matlab路径

ldd build/tools/caffe # 查看所有的库
ldd build/tools/caffe | grep cudnn # 查看cudnn 版本信息
ldd build/tools/caffe | grep openblas # 查看 openblas 库信息

/path/to/imagenet/train/n01440764/n01440764_10026.JPEG
/path/to/imagenet/val/ILSVRC2012_val_00000001.JPEG

$ ./data/ilsvrc12/get_ilsvrc_aux.sh

#更改 examples/imagenet/create_imagenet.sh 脚本中的 TRAIN_DATA_ROOT 和 VAL_DATA_ROOT 为解压后的原始图像路径
# 设置 RESIZE=true 以便在将图像添加到数据库之前将其调整到适当大小

# 创建图像数据库
$ ./examples/imagenet/create_imagenet.sh

# 创建所需的图像均值文件
$ ./examples/imagenet/make_imagenet_mean.sh

$ export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/lib:$LD_LIBRARY_PATH

# 编辑 models/bvlc_alexnet/solver.prototxt 文件
$ ./build/tools/caffe train –solver=models/bvlc_alexnet/solver.prototxt –gpu 0

# 可以通过指定多个设备 ID（例如 0、1、2、3）或指定“-gpu all”以使用系统中所有可用的 GPU，在多个 GPU 上进行训练. 

$ sudo apt-get install python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags Cython ipython 
$ sudo apt-get install python-pip wget

$ sudo apt-get install liblmdb-dev
或
$ sudo pip install lmdb

$ sudo conda install opencv #  for Anaconda 
或
$ sudo apt-get install python-opencv

$ sudo apt-get install python-skimage 
$ sudo apt-get update
$ make pycaffe # 重新编译 python API

# 设置环境变量
$ sudo  gedit /etc/profile
# 添加环境变量：
    export PYTHONPATH=${HOME}/caffe-master/distribute/python:$PYTHONPATH
    export LD_LIBRARY_PATH=${HOME}/caffe-master/build/lib:$LD_LIBRARY_PATH
$ source /etc/profile # 使环境变量生效
$ echo $<环境变量名> # 查看环境变量

打开caffe目录下的Makefile.config文件，找到WITH_PYTHON_LAYER  := 1这一行，将前面的‘#’去掉，重新编译，最好是重新打开一个终端进行编译

$ sudo pip install easydict

$ wget  https://code.google.com/p/protobuf/wiki/Download?tm=2
$ 解压缩文件，并进入文件夹
$ ./configure
$ make
$ make check
$ make install
$ ./configure && make && cd python && python setup.py test && python setup.py install

$ sudo apt-get install python-protobuf
或
使用新立得软件包搜索“python-protobuf”安装

- Step 1： 在Makefile.config文件的第85行，添加/usr/include/hdf5/serial/ 到 INCLUDE_DIRS，也就是把下面第一行代码改为第二行代码：

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/

- Step 2： 在Makefile文件的第173行，把 hdf5_hl 和hdf5修改为hdf5_serial_hl 和 hdf5_serial，也就是把下面第一行代码改为第二行代码：   

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

# 在多个 GPU 上运行 Caffe 需要使用 NVIDIA NCCL
$ git clone https://github.com/NVIDIA/nccl.git
$ cd nccl
$ sudo make install -j4
# NCCL 库和文件头将安装在 /usr/local/lib 和 /usr/local/include 中
$ sudo  ldconfig # 该命令不执行会出现错误： error while loading shared libraries: libnccl.so.1: cannot open shared object file: No such file or directory 

$ sudo apt-get install python-protobuf
或   
可以先下载安装包，自行编译和安装。可参考：http://blog.csdn.net/paynetiger/article/details/8197326
建议使用第一种方法，下面是关键：
如果使用Anaconda，而以上两种方法无论哪一种都会将prototxt相关文件安装到/usr/local/lib/python2.7/dist-packages， 需要将相关文件复制到Anaconda/lib/python2.7/site-packages下，才能正常使用.  

$ sudo apt-get install graphviz # 安装graphviz
$ sudo pip install pydot # 安装pydot
如果使用Anaconda，需要将相关文件从/usr/local/lib/python2.7/dist-packages复制到Anaconda/lib/python2.7/site-packages下. 

$ sudo ldconfig /usr/local/cuda/lib64
$ sudo ldconfig /usr/local/cuda-7.5/lib64

$ sudo ldconfig /usr/local/cuda-7.5/lib64

import sys
sys.path.append("/（你的caffe-master路径）/caffe-master/python")
sys.path.append("/（你的caffe-master路径）/caffe-master/python/caffe")
import caffe

# 下载protobuf-2.3.0：  
$ wget http://protobuf.googlecode.com/files/protobuf-2.5.0.zip
$ unzip protobuf-2.5.0
$ cd protobuf-2.5.0
$ chmod 777 configure
$ ./configure
$ make -j4
$ make check -j4
$ make install    

# 编译python接口   
$ cd ./python 
$ python setup.py build 
$ python setup.py test 
$ python setup.py install
$ protoc -version  # 验证使用命令 

>> import google.protobuf   

$ sudo apt-get install automake autoconf libtool

# This issue arises when you have libopenblas-base and libatlas3-base installed, but don't haveliblapack3 installed. This combination of packages installs conflicting versions of libblas.so (from OpenBLAS) and liblapack.so (from ATLAS).

# Solution 1 (my favorite): You can keep both OpenBLAS and ATLAS on your machine if you also install liblapack3.
$ sudo apt-get install liblapack3

# Solution 2: Uninstall ATLAS (this will actually install liblapack3 for you automatically because of some deb package shenanigans)
$ sudo apt-get uninstall libatlas3-base

# Solution 3: Uninstall OpenBLAS
$ sudo apt-get uninstall libopenblas-base
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
# Bad configuration
$ dpkg -l | grep 'openblas\|atlas\|lapack'
ii  libatlas3-base                                        3.10.1-4                                            amd64        Automatically Tuned Linear Algebra Software, generic shared
ii  libopenblas-base                                      0.2.8-6ubuntu1                                      amd64        Optimized BLAS (linear algebra) library based on GotoBLAS2
$ update-alternatives --get-selections | grep 'blas\|lapack'
libblas.so.3                   auto     /usr/lib/openblas-base/libblas.so.3
liblapack.so.3                 auto     /usr/lib/atlas-base/atlas/liblapack.so.3
$ python -c 'import numpy'
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/usr/lib/python2.7/dist-packages/numpy/__init__.py", line 153, in <module>
    from . import add_newdocs
  File "/usr/lib/python2.7/dist-packages/numpy/add_newdocs.py", line 13, in <module>
    from numpy.lib import add_newdoc
  File "/usr/lib/python2.7/dist-packages/numpy/lib/__init__.py", line 18, in <module>
    from .polynomial import *
  File "/usr/lib/python2.7/dist-packages/numpy/lib/polynomial.py", line 19, in <module>
    from numpy.linalg import eigvals, lstsq, inv
  File "/usr/lib/python2.7/dist-packages/numpy/linalg/__init__.py", line 50, in <module>
    from .linalg import *
  File "/usr/lib/python2.7/dist-packages/numpy/linalg/linalg.py", line 29, in <module>
    from numpy.linalg import lapack_lite, _umath_linalg
ImportError: /usr/lib/liblapack.so.3: undefined symbol: ATL_chemv

# Solution 1
$ dpkg -l | grep 'openblas\|atlas\|lapack'
ii  libatlas3-base                                        3.10.1-4                                            amd64        Automatically Tuned Linear Algebra Software, generic shared
ii  liblapack3                                            3.5.0-2ubuntu1                                      amd64        Library of linear algebra routines 3 - shared version
ii  libopenblas-base                                      0.2.8-6ubuntu1                                      amd64        Optimized BLAS (linear algebra) library based on GotoBLAS2
$ update-alternatives --get-selections | grep 'blas\|lapack'
libblas.so.3                   auto     /usr/lib/openblas-base/libblas.so.3
liblapack.so.3                 auto     /usr/lib/lapack/liblapack.so.3
$ python -c 'import numpy'

# Solution 2
$ dpkg -l | grep 'openblas\|atlas\|lapack'
ii  liblapack3                                            3.5.0-2ubuntu1                                      amd64        Library of linear algebra routines 3 - shared version
ii  libopenblas-base                                      0.2.8-6ubuntu1                                      amd64        Optimized BLAS (linear algebra) library based on GotoBLAS2
$ update-alternatives --get-selections | grep 'blas\|lapack'
libblas.so.3                   auto     /usr/lib/openblas-base/libblas.so.3
liblapack.so.3                 auto     /usr/lib/lapack/liblapack.so.3
$ python -c 'import numpy'

# Solution 3
$ dpkg -l | grep 'openblas\|atlas\|lapack'
ii  libatlas3-base                                        3.10.1-4                                            amd64        Automatically Tuned Linear Algebra Software, generic shared
$ update-alternatives --get-selections | grep 'blas\|lapack'
libblas.so.3                   auto     /usr/lib/atlas-base/atlas/libblas.so.3
liblapack.so.3                 auto     /usr/lib/atlas-base/atlas/liblapack.so.3
$ python -c 'import numpy'

# 检查环境变量设置  
$ echo $PATH
$ echo $LD_LIBRARY_PATH 
# 将一些文件复制到/usr/local/lib文件夹下：
$ sudo cp /usr/local/cuda-7.5/lib64/libcudart.so.7.5 /usr/local/lib/libcudart.so.7.5 && sudo ldconfig
$ sudo cp /usr/local/cuda-7.5/lib64/libcublas.so.7.5 /usr/local/lib/libcublas.so.7.5 && sudo ldconfig
$ sudo cp /usr/local/cuda-7.5/lib64/libcurand.so.7.5 /usr/local/lib/libcurand.so.7.5 && sudo ldconfig

$ sudo apt-get update
# 出现错误 - W: GPG error: http://archive.ubuntukylin.com:10006 xenial InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 8D5A09DC9B929006
# 是密钥的问题，解决办法：
$ sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 8D5A09DC9B929006
# 注意提示的密钥和你上述命令中的密钥是一样的，每个人的电脑都不一样

$ sudo apt-get install python-yaml
或源码安装，如 PyYAML 3.11 ：
$ wget http://pyyaml.org/download/pyyaml/PyYAML-3.11.tar.gz 
$ tar -zxvf PyYAML-3.11.tar.gz  
$ python setup.py install

# 通过终端安装程序sudo apt-get install xxx时出错：
#   E: Could not get lock /var/lib/dpkg/lock - open (11: Resource temporarily unavailable)
#   E: Unable to lock the administration directory (/var/lib/dpkg/), is another process using it
# 出现这个问题可能是有另外一个程序正在运行，导致资源被锁不可用。而导致资源被锁的原因可能是上次运行安装或更新时没有正常完成，进而出现此状况，解决的办法：
$ sudo rm /var/cache/apt/archives/lock
$ sudo rm /var/lib/dpkg/lock

# 问题描述：
#   服务器为Ubuntu14.04，NVIDIA驱动由352.39自动升级到352.63，导致显卡不能使用，错误为：运行nvidia-smi指令，得到的信息为“Failed to initialize NVML: GPU access blocked by the operating sestem”
#    系统：Ubuntu14.04
#    cuda：7.5

# 解决方案：
# 1. 首先关闭系统包括的所有更新
$ sudo vim /etc/apt/apt.conf.d/50unattended-upgrades
#（注释掉其中的更新部分）
#参考链接：http://www.linuxdiyf.com/Linux/15997.html

# 2. 卸载cuda驱动并重新安装
# （1）彻底卸载
$ sudo apt-get remove --purge nvidia*
$ sudo apt-get autoremove
$ sudo apt-get clean
$ dpkg -l |grep ^rc|awk '{print $2}' |sudo xargs dpkg -P 
# 参考链接：
# https://devtalk.nvidia.com/default/topic/900899/cuda-setup-and-installation/unable-to-detect-cuda-capable-device-after-automatic-forced-nvidia-updated/
# http://zhidao.baidu.com/link?url=smwXar3NPdAi1WxnZJ2_sARCEPoNcxLwB0RwmEnDPiqyrbdz64aVCoabN9azod-AQrJP0OjeiL8-y8mFRHZDma

# （2）重装cuda
# 由于之前系统Ubuntu14.04在配置好caffe环境后，编译matlab接口时将gcc由4.8降级为4.7。若直接安装cuda，会提示错误，“Unable to find the kernel source tree for the currently running kernel. Please make sure you have installed the kernel source files for your kernel and that they are properly configured; on Red Hat Linux systems, for example, be sure you have the 'kernel-source' or 'kernel-devel' RPM installed.If you know the correct kernel source files are installed, you may specify the kernel source path with the '--kernel-source-path' command line option.”.如果按照此条路径思索，在尝试添加了“--kernel-source-path”之后，仍存在问题。继续往下走，会提示给系统内核降级。
# 考虑到之前曾对系统的gcc降级，这导致了上述错误，对gcc升级。
$ sudo apt-get install gcc-4.7
$ cd /usr/bin
$ sudo mv gcc gcc.bak
$ sudo ln -s gcc-4.7 gcc
$ sudo mv g++ g++.bak
$ sudo ln -s g++-4.7 g++
# 参考链接：http://www.mamicode.com/info-detail-876185.html

# 然后按照常规方法重装cuda驱动即可解决问题。

# 转自： http://blog.csdn.net/u012494820/article/details/52289095

# 开机，在 GRUB 选择界面按 E，这时界面变成了一个编辑器。
# 在倒数几行找到 ro quiet splash
# 然后删掉quiet，改成text，接着按F10
# 这时你已进入操作系统（ctrl+alt+F1-F6），输入用户名密码登录。
# 然后输入以下代码：
$ sudo add-apt-repository ppa:bumblebee/stable
$ sudo apt-get update
$ sudo apt-get install bumblebee bumblebee-nvidia

# 从官网(http://opencv.org/downloads.html)下载OpenCV,并将其解压到安装的位置，假设解压到了/home/opencv

# 创建编译文件夹：
$ cd ~/opencv
$ mkdir build
$ cd build

# 配置：
$ cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local .. 

# 编译：
make -j8 #-j8表示并行计算，也可直接 make  

# opencv安装
$ sudo make install

# 解决办法是将一些文件复制到/usr/local/lib文件夹下：
# 注意CUDA版本号
$ sudo cp /usr/local/cuda-8.0/lib64/libcudart.so.8.0 /usr/local/lib/libcudart.so.8.0 && sudo ldconfig
$ sudo cp /usr/local/cuda-8.0/lib64/libcublas.so.8.0 /usr/local/lib/libcublas.so.8.0 && sudo ldconfig
$ sudo cp /usr/local/cuda-8.0/lib64/libcurand.so.8.0 /usr/local/lib/libcurand.so.8.0 && sudo ldconfig

$ sudo apt-get install libmatio-dev
或源码安装：  
# 下载 matio (https://sourceforge.net/projects/matio/)
$ tar zxf matio-X.Y.Z.tar.gz
$ cd matio-X.Y.Z
$ ./configure
$ make
$ make check
$ make install  # 安装
$ export LD_LIBRARY_PATH=/path/to/libmatio.so.2

# 在caffe 的 Makefile.config 中的INCLUDE_DIRS 中添加 matio 的 src路径， LIBRARY_DIRS 中添加 src/.libs，如：
#   INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include  /path/to/matio-1.5.2/src
#   LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /path/to/matio-1.5.2/src/.libs

# 参考： http://blog.csdn.net/houqiqi/article/details/46469981

# cython_bbox 和cython_nms 问题
$ cd fast_rcnn_root/lib
$ python setup.py install

# setup.py安装完成后，
$ cd python_root/Lib/site-packages/utils
# 可以找到两个文件cython_bbox.so和cython_nms.so，把这两个文件复制到fast_rcnn_root/lib/utils中即可.
# 参考： http://blog.csdn.net/happynear/article/details/46822109

将common.cuh 进行如下修改，注意最后的endif

  1. #ifndef CAFFE_COMMON_CUH_  
  2. #define CAFFE_COMMON_CUH_  
  3.   
  4.   
  5. #include <cuda.h>  
  6. #if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600  
  7. #else  
  8. // CUDA: atomicAdd is not defined for doubles  
  9. static __inline__ __device__ double atomicAdd(double *address, double val) {  
  10.   unsigned long long int* address_as_ull = (unsigned long long int*)address;  
  11.   unsigned long long int old = *address_as_ull, assumed;  
  12.   if (val==0.0)  
  13.     return __longlong_as_double(old);  
  14.   do {  
  15.     assumed = old;  
  16.     old = atomicCAS(address_as_ull, assumed, __double_as_longlong(val +__longlong_as_double(assumed)));  
  17.   } while (assumed != old);  
  18.   return __longlong_as_double(old);  
  19. }  
  20. #endif  
  21. #endif  

  至此基本就可以make通过了.   

$ nvcc -V
>> The program 'nvcc' is currently not installed. You can install it by typing:
sudo apt-get install nvidia-cuda-toolkit

$ sudo gedit ~/.bashrc
在最后面，64位的话粘贴以下内容：
export PATH=/usr/local/cuda-7.5/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH

$ source ~/.bashrc 
使其立即生效即可 

有时在编写caffe代码时为了简便可能会使用C++98之后的一些新特性，如果不对caffe的默认编译选项进行修改则会产生诸如“xxx is not a member of ‘std’”或者某些函数没有匹配声明的错误.

在Makefile中的g++编译命令里添加-std=c++11的flag.
caffe 的 Makefile 中，

CXXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WRANINGS)

之后添加 -std=c++11

../lib/libcaffe.so.1.0.0: undefined reference to `caffe::BlockingQueue<boost::shared_ptr<caffe::DataReader::QueuePair> >::size() const'
../lib/libcaffe.so.1.0.0: undefined reference to `caffe::BlockingQueue<boost::shared_ptr<caffe::DataReader::QueuePair> >::push(boost::shared_ptr<caffe::DataReader::QueuePair> const&)'
../lib/libcaffe.so.1.0.0: undefined reference to `caffe::BlockingQueue<caffe::Datum*>::push(caffe::Datum* const&)'
../lib/libcaffe.so.1.0.0: undefined reference to `caffe::BlockingQueue<caffe::Datum*>::BlockingQueue()'
../lib/libcaffe.so.1.0.0: undefined reference to `caffe::BlockingQueue<boost::shared_ptr<caffe::DataReader::QueuePair> >::pop(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)'
../lib/libcaffe.so.1.0.0: undefined reference to `caffe::BlockingQueue<caffe::Datum*>::pop(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)'
../lib/libcaffe.so.1.0.0: undefined reference to `caffe::BlockingQueue<caffe::Datum*>::try_pop(caffe::Datum**)'
../lib/libcaffe.so.1.0.0: undefined reference to `caffe::BlockingQueue<boost::shared_ptr<caffe::DataReader::QueuePair> >::BlockingQueue()'

//在blocking_queue.cpp中添加数据类型的泛型实例化的声明
template class BlockingQueue<Datum*>;
template class BlockingQueue<shared_ptr<DataReader::QueuePair> >;

import sys
sys.path.insert(0,caffe_root+'python)
import caffe

WITH_PYTHON_LAYER := 1

sudo apt-get install gfortran