Linking CXX shared library ../../lib/libcaffe.dylib and lib/libcaffe.1.0.0-rc3.dylib

[Deniz]

when I try make all, I continue to get this error. I think there is a linking issue between libcaffe.dylib and libcaffe.1.0.0-rc3.dylib. 

I am using OSX El Capitan 10.11.6.

I tried various solutions. The following are some of the solutions I've seen as relevant, but didn't understand how to apply:

1.  I don't know how to use this. The Makefile that I got upon cloning Caffe from git is much different than the one I have right now - which is appearently generated by cmake. I am running cmake $CAFFE_ROOT and then make all. Is this wrong?

On OSX El Capitan, I added one line to the Makefile after the exisiting LDFLAGS. No need to run @dougalsutherland's script:

LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) $(PKG_CONFIG) \

                $(foreach library,$(LIBRARIES),-l$(library))

LDFLAGS += -Wl,-rpath,/usr/local/cuda/lib

Source: Forges0 @developers.nvidia.com

I can make a PR, but I'm not sure how this change would affect other OS'es?

2. This commit feels relevant, however its already a commit - and I still have an error. 

https://github.com/BVLC/caffe/pull/3696/commits/b46133aff47d5ac9d2f0f1289c6d5a9c57b3c2c5

3. I tried this but made no difference : 

After make test in $CAFFE_ROOT:

cp -a .build_release/lib/. /usr/local/lib/

Any help?

Linking CXX shared library ../../lib/libcaffe.dylib

Undefined symbols for architecture x86_64:

  "_H5LTfind_dataset", referenced from:

      caffe::SGDSolver<float>::RestoreSolverStateFromHDF5(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) in sgd_solver.cpp.o

      caffe::SGDSolver<double>::RestoreSolverStateFromHDF5(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) in sgd_solver.cpp.o

      void caffe::hdf5_load_nd_dataset_helper<float>(long long, char const*, int, int, caffe::Blob<float>*) in hdf5.cpp.o

      void caffe::hdf5_load_nd_dataset_helper<double>(long long, char const*, int, int, caffe::Blob<double>*) in hdf5.cpp.o

  "_H5LTget_dataset_info", referenced from:

      void caffe::hdf5_load_nd_dataset_helper<float>(long long, char const*, int, int, caffe::Blob<float>*) in hdf5.cpp.o

      void caffe::hdf5_load_nd_dataset_helper<double>(long long, char const*, int, int, caffe::Blob<double>*) in hdf5.cpp.o

      caffe::hdf5_load_string(long long, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) in hdf5.cpp.o

  "_H5LTget_dataset_ndims", referenced from:

      void caffe::hdf5_load_nd_dataset_helper<float>(long long, char const*, int, int, caffe::Blob<float>*) in hdf5.cpp.o

      void caffe::hdf5_load_nd_dataset_helper<double>(long long, char const*, int, int, caffe::Blob<double>*) in hdf5.cpp.o

  "_H5LTmake_dataset_double", referenced from:

      void caffe::hdf5_save_nd_dataset<double>(long long, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&, caffe::Blob<double> const&, bool) in hdf5.cpp.o

  "_H5LTmake_dataset_float", referenced from:

      void caffe::hdf5_save_nd_dataset<float>(long long, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&, caffe::Blob<float> const&, bool) in hdf5.cpp.o

  "_H5LTmake_dataset_int", referenced from:

      caffe::hdf5_save_int(long long, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&, int) in hdf5.cpp.o

  "_H5LTmake_dataset_string", referenced from:

      caffe::hdf5_save_string(long long, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) in hdf5.cpp.o

  "_H5LTread_dataset_double", referenced from:

      void caffe::hdf5_load_nd_dataset<double>(long long, char const*, int, int, caffe::Blob<double>*) in hdf5.cpp.o

  "_H5LTread_dataset_float", referenced from:

      void caffe::hdf5_load_nd_dataset<float>(long long, char const*, int, int, caffe::Blob<float>*) in hdf5.cpp.o

  "_H5LTread_dataset_int", referenced from:

      caffe::hdf5_load_int(long long, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) in hdf5.cpp.o

  "_H5LTread_dataset_string", referenced from:

      caffe::hdf5_load_string(long long, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&) in hdf5.cpp.o

ld: symbol(s) not found for architecture x86_64

clang: error: linker command failed with exit code 1 (use -v to see invocation)

make[2]: *** [lib/libcaffe.1.0.0-rc3.dylib] Error 1

make[1]: *** [src/caffe/CMakeFiles/caffe.dir/all] Error 2

make: *** [all] Error 2

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 := 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 *_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 := 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 := /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/Headers

BLAS_LIB := /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A

# 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)/anaconda

# 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 \

# /usr/local/Cellar/python/2.7.12/Frameworks/Python.framework/Versions/2.7/include/python2.7

# PYTHON_LIB += $(shell brew --prefix numpy)/lib \

# /usr/local/Cellar/python/2.7.12/Frameworks/Python.framework/Versions/2.7/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/local/cuda/include /usr/local/cuda/targets/x86_64-linux/include/thrust/system/cuda/detail/

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/local/cuda/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

# 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 ?= @