Ubuntu 16.04 CUDA 8.0＆cuDNN 5.1のCaffeインストールエラー

Question

深い学習のためにGTX 1080によるUBUNUTU 16.04 Xenial PCを使用しています。しかし、私はBLVCまたはNVIDIAのソースからのCaffeのコンパイルにほとんど問題に直面しています。すべての依存関係をインストールしてグローバル変数をリンクした後、ImはまだCaffeをコンパイルするための何かを見逃しています。私は、Python 3.5.2を使用してい

-- The C compiler identification is GNU 5.4.0 
-- The CXX compiler identification is GNU 5.4.0 
-- Check for working C compiler: /usr/bin/cc 
-- Check for working C compiler: /usr/bin/cc -- works 
-- Detecting C compiler ABI info 
-- Detecting C compiler ABI info - done 
-- Detecting C compile features 
-- Detecting C compile features - done 
-- Check for working CXX compiler: /usr/bin/c++ 
-- Check for working CXX compiler: /usr/bin/c++ -- works 
-- Detecting CXX compiler ABI info 
-- Detecting CXX compiler ABI info - done 
-- Detecting CXX compile features 
-- Detecting CXX compile features - done 
-- Boost version: 1.58.0 
-- Found the following Boost libraries: 
-- system 
-- thread 
-- filesystem 
-- Looking for include file pthread.h 
-- Looking for include file pthread.h - found 
-- Looking for pthread_create 
-- Looking for pthread_create - not found 
-- Looking for pthread_create in pthreads 
-- Looking for pthread_create in pthreads - not found 
-- Looking for pthread_create in pthread 
-- Looking for pthread_create in pthread - found 
-- Found Threads: TRUE 
-- Found GFlags: /usr/include 
-- Found gflags (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libgflags.so) 
-- Found Glog: /usr/include 
-- Found glog (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libglog.so) 
-- Found PROTOBUF: /usr/lib/x86_64-linux-gnu/libprotobuf.so 
-- Found PROTOBUF Compiler: /usr/bin/protoc 
CMake Error at /usr/local/share/cmake-3.2/Modules/FindPackageHandleStandardArgs.cmake:138 (message): 
    Could NOT find HDF5 (missing: HDF5_INCLUDE_DIRS) 
Call Stack (most recent call first): 
    /usr/local/share/cmake-3.2/Modules/FindPackageHandleStandardArgs.cmake:374 (_FPHSA_FAILURE_MESSAGE) 
    /usr/local/share/cmake-3.2/Modules/FindHDF5.cmake:360 (find_package_handle_standard_args) 
    cmake/Dependencies.cmake:27 (find_package) 
    CMakeLists.txt:43 (include) 


-- Configuring incomplete, errors occurred! 
See also "/home/xhuv/testcaffe/caffe/build/CMakeFiles/CMakeOutput.log". 
See also "/home/xhuv/testcaffe/caffe/build/CMakeFiles/CMakeError.log". 

- 私は今https://github.com/BVLC/caffeからのクローニングおよび

cd caffe 
mkdir build 
cd build 
cmake .. 

は私に次のエラーを与える入るのOpenCV 3.1.0 & OpenBLASなどを構築しました。 :: Anaconda 4.2.0（64-bit）、Pythonバージョン2.7もインストールされています。私は次のようにMakefile.configを編集しました

## 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 

可能性のあるエラーは何ですか？助けてください！！

Answer 1

Caffeのインストールは、アーキテクチャー固有のものです。 anacondaの問題は、Caffeが必要とするGoogleプロトコルのサポートのためにPythonが同じprotobufパッケージを使用していないことです。 Anacondaのインストールについては、this repositoryを参照してください。

Answer 2

Ubuntu 16.04のHDF5のインストールディレクトリは、cmake FindHDF5モジュールでうまく動作しないようです。これを "うまく"修正するために数時間を掘り下げましたが、ちょうどcmake/Dependencies.cmakeファイルにパッチを当てて、Caffeを正しくコンパイルするようにしました。いずれの場合においても

は、ここでのパッチです：ubuntu1604_caffe_hdf5.patch

そして、あなたは（Dockerfileのような）ビルドスクリプトにそれを含めるための簡単な方法を探しているなら、私はgit.ioリンクあなたを作りましたcmake/Dependencies.cmakeにパッチを適用するために、あなたのベース・カフェディレクトリに実行できます。

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

Answer 3

あなたにあなたのライブラリディレクトリを変更するには、このコマンドsudo apt-get install libhdf5-serial-devを使用してHD5Fをインストールして追加する最初の必要性この+LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial/ が、そのバージョンが付属しますライブラリはあなたので、その延長に追加正式な名前を持つシンボリックリンクを作成する必要がありますカフェでは、この$ 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 .Iように構築する実行中を探しますそれは