cuda8.0和cuDNN 5.1的Ubuntu 16.04上Caffe安装错误

Question

我正在使用GTX 1080支持的UBUNUTU 16.04 XenialPC进行深度学习。然而，我面临着一个小问题，从BLVC或NVIDIA源代码编译caffe。在安装了所有依赖项并链接了全局变量之后，我仍然缺少一些编译caffe的东西。我已经构建了opencv3.1.0和OpenBLAS等，现在从https://github.com/BVLC/caffe克隆并输入

cd caffe
 mkdir build
cd build 
cmake ..

给出以下错误-

^{pr2}$

我使用的是python3.5.2。●还安装了Anaconda 4.2.0（64位）、Python 2.7版。我根据以下内容编辑了Makefile.config

## 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 := 3
# 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

# 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 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_50,code=compute_50

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
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 := /usr/local/include
BLAS_LIB := /usr/local/lib

# 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)/anaconda2
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
         $(ANACONDA_HOME)/include/python2.7 \
         $(ANACONDA_HOME)/lib/python2.7/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
LIBRARY_DIRS += /usr/lib/x86_64-linux-gnu/
LIBRARY_DIRS += $(ANACONDA_HOME)/lib

# 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

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 ?= @ 

# shared object suffix name to differentiate branches
LIBRARY_NAME_SUFFIX := -nv

可能的错误是什么？请帮忙！！在

Answer 1

Caffe的安装对于体系结构来说是很有挑战性的。anaconda的问题是它没有使用python提供的相同的protobuf包来支持Caffe所要求的google协议支持。关于水蟒咖啡的安装，请参考this repository。在

Answer 2

Ubuntu16.04上HDF5的安装目录对于cmake FindHDF5模块来说似乎不太好用。我花了好几个小时研究如何“很好地”修复这个问题，但最后只是修补了cmake/Dependencies.cmake文件，以使caffe能够正确编译。在

无论如何，这是一个补丁：ubuntu1604_caffe_hdf5.patch

如果您正在寻找一种快速的方法将其包含到构建脚本中（比如Dockerfile），我已经创建了一个吉奥您可以在基本caffe目录中运行到修补程序cmake/Dependencies.cmake的链接：

wget -O- https://git.io/vHcP3 | patch -p0

Answer 3

您首先需要使用这个命令sudo apt-get install libhdf5-serial-dev安装HD5F，并将change your library dir添加到这个+LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial/ 但是库的版本会添加到它们的扩展中，所以您需要创建具有正式名称的符号链接，caffe在执行这样的构建时将查找这些链接$ sudo ln -s /usr/lib/x86_64-linux-gnu/libhdf5_serial.so.10 /usr/lib/x86_64-linux-gnu/libhdf5.so $ sudo ln -s /usr/lib/x86_64-linux-gnu/libhdf5_serial_hl.so.10 /usr/lib/x86_64-linux-gnu/libhdf5_hl.so。我希望这能有所帮助