Plusmonkey opened a new issue #15473: mac mxnet cpu compile error
URL: https://github.com/apache/incubator-mxnet/issues/15473
 
 
   Note: Providing complete information in the most concise form is the best 
way to get help. This issue template serves as the checklist for essential 
information to most of the technical issues and bug reports. For non-technical 
issues and feature requests, feel free to present the information in what you 
believe is the best form.
   
   For Q & A and discussion, please start a discussion thread at 
https://discuss.mxnet.io 
   
   ## Description
   (Brief description of the problem in no more than 2 sentences.)
   Compiling mac no CUDA, but happen build error.
   ## Environment info (Required)
   
   ----------Python Info----------
   Version      : 3.7.1
   Compiler     : Clang 4.0.1 (tags/RELEASE_401/final)
   Build        : ('default', 'Dec 14 2018 13:28:58')
   Arch         : ('64bit', '')
   ------------Pip Info-----------
   Version      : 18.1
   Directory    : /Users/wenba/anaconda3/lib/python3.7/site-packages/pip
   ----------MXNet Info-----------
   objc[6126]: Class CaptureDelegate is implemented in both 
/usr/local/Cellar/opencv/4.1.0_2/lib/libopencv_videoio.4.1.dylib (0x110bf5950) 
and 
/Users/wenba/anaconda3/lib/python3.7/site-packages/cv2/cv2.cpython-37m-darwin.so
 (0x1a31c286a8). One of the two will be used. Which one is undefined.
   Version      : 1.5.0
   Directory    : 
/Users/wenba/Documents/project/mxnet/incubator-mxnet/python/mxnet
   Hashtag not found. Not installed from pre-built package.
   ----------System Info----------
   Platform     : Darwin-17.7.0-x86_64-i386-64bit
   system       : Darwin
   node         : wenbadeMBP-7.lan
   release      : 17.7.0
   version      : Darwin Kernel Version 17.7.0: Thu Jun 21 22:53:14 PDT 2018; 
root:xnu-4570.71.2~1/RELEASE_X86_64
   ----------Hardware Info----------
   machine      : x86_64
   processor    : i386
   b'machdep.cpu.brand_string: Intel(R) Core(TM) i7-4770HQ CPU @ 2.20GHz'
   b'machdep.cpu.features: FPU VME DE PSE TSC MSR PAE MCE CX8 APIC SEP MTRR PGE 
MCA CMOV PAT PSE36 CLFSH DS ACPI MMX FXSR SSE SSE2 SS HTT TM PBE SSE3 PCLMULQDQ 
DTES64 MON DSCPL VMX EST TM2 SSSE3 FMA CX16 TPR PDCM SSE4.1 SSE4.2 x2APIC MOVBE 
POPCNT AES PCID XSAVE OSXSAVE SEGLIM64 TSCTMR AVX1.0 RDRAND F16C'
   b'machdep.cpu.leaf7_features: SMEP ERMS RDWRFSGS TSC_THREAD_OFFSET BMI1 AVX2 
BMI2 INVPCID FPU_CSDS'
   b'machdep.cpu.extfeatures: SYSCALL XD 1GBPAGE EM64T LAHF LZCNT RDTSCP TSCI'
   ----------Network Test----------
   Setting timeout: 10
   Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0098 
sec, LOAD: 1.6437 sec.
   Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 1.3267 sec, LOAD: 
1.2439 sec.
   Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 1.6387 sec, LOAD: 
0.9030 sec.
   Timing for FashionMNIST: 
https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz,
 DNS: 0.5090 sec, LOAD: 1.1270 sec.
   Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0345 sec, LOAD: 
1.7158 sec.
   Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0084 sec, 
LOAD: 1.0226 sec.
   ```
   What to do:
   1. Download the diagnosis script from 
https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py
   2. Run the script using `python diagnose.py` and paste its output here.
   
   ```
   
   Package used (Python/R/Scala/Julia):
   (I'm using ...)
   C++
   For Scala user, please provide:
   1. Java version: (`java -version`)
   2. Maven version: (`mvn -version`)
   3. Scala runtime if applicable: (`scala -version`)
   
   For R user, please provide R `sessionInfo()`:
   
   ## Build info (Required if built from source)
   
   Compiler (gcc/clang/mingw/visual studio):
   clang
   MXNet commit hash:
   (Paste the output of `git rev-parse HEAD` here.)
   612b9d1ed441e5af239cab6064b648beba3b99bb
   Build config:
   (Paste the content of config.mk, or the build command.)
   
   ## Error Message:
   (Paste the complete error message, including stack trace.)
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(cudnn_algoreg.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(cudnn_batch_norm.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_act.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_base.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_concat.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_convolution.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_copy.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_deconvolution.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_fully_connected.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_pooling.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_reshape.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_slice.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_softmax.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_softmax_output.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_sum.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_transpose.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_quantized_act.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_quantized_concat.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_quantized_conv.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_quantized_elemwise_add.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_quantized_fully_connected.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_quantized_pooling.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_conv.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_fc.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(mkldnn_subgraph_property.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(nnvm_to_onnx.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(onnx_to_tensorrt.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(tensorrt.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(nnpack_util.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(rtc.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(c_lapack_api.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(nvtx.o) has no symbols
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/ranlib:
 file: lib/libmxnet.a(vtune.o) has no symbols
   ## Minimum reproducible example
   (If you are using your own code, please provide a short script that 
reproduces the error. Otherwise, please provide link to the existing example.)
   
   ## config message
   
   #---------------------
   # choice of compiler
   #--------------------
   
   export CC = gcc
   export CXX = g++
   export NVCC = nvcc
   
   # whether compile with options for MXNet developer
   DEV = 0
   
   # whether compile with debug
   DEBUG = 0
   
   # the additional link flags you want to add
   ADD_LDFLAGS =
   
   # the additional compile flags you want to add
   ADD_CFLAGS =
   
   #---------------------------------------------
   # matrix computation libraries for CPU/GPU
   #---------------------------------------------
   
   # whether use CUDA during compile
   USE_CUDA = 0
   
   # add the path to CUDA library to link and compile flag
   # if you have already add them to environment variable, leave it as NONE
   # USE_CUDA_PATH = /usr/local/cuda
   USE_CUDA_PATH = NONE
   
   # whether to enable CUDA runtime compilation
   ENABLE_CUDA_RTC = 1
   
   # whether use CUDNN R3 library
   USE_CUDNN = 0
   
   # whether use opencv during compilation
   # you can disable it, however, you will not able to use
   # imbin iterator
   USE_OPENCV = 1
   # Add OpenCV include path, in which the directory `opencv2` exists
   USE_OPENCV_INC_PATH = NONE
   # Add OpenCV shared library path, in which the shared library exists
   USE_OPENCV_LIB_PATH = NONE
   
   # use openmp for parallelization
   # apple-clang by default does not have openmp built-in
   USE_OPENMP = 0
   
   # choose the version of blas you want to use
   # can be: mkl, blas, atlas, openblas, apple
   USE_BLAS = apple
   
   # whether use lapack during compilation
   # only effective when compiled with blas versions openblas/apple/atlas/mkl
   USE_LAPACK = 1
   
   # by default, disable lapack when using MKL
   # switch on when there is a full installation of MKL available (not just 
MKL_ML)
   ifeq ($(USE_BLAS), mkl)
   USE_LAPACK = 0
   endif
   
   # add path to intel library, you may need it for MKL, if you did not add the 
path
   # to environment variable
   USE_INTEL_PATH = NONE
   
   #----------------------------
   # distributed computing
   #----------------------------
   
   # whether or not to enable multi-machine supporting
   USE_DIST_KVSTORE = 0
   
   # whether or not allow to read and write HDFS directly. If yes, then hadoop 
is
   # required
   USE_HDFS = 0
   
   # path to libjvm.so. required if USE_HDFS=1
   LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
   
   # whether or not allow to read and write AWS S3 directly. If yes, then
   # libcurl4-openssl-dev is required, it can be installed on Ubuntu by
   # sudo apt-get install -y libcurl4-openssl-dev
   USE_S3 = 0
   
   #----------------------------
   # additional operators
   #----------------------------
   
   # path to folders containing projects specific operators that you don't want 
to put in src/operators
   EXTRA_OPERATORS =
   
   #----------------------------
   # other features
   #----------------------------
   
   # Create C++ interface package
   USE_CPP_PACKAGE = 0
   
   # Use int64_t type to represent the total number of elements in a tensor
   # This will cause performance degradation reported in issue #14496
   # Set to 1 for large tensor with tensor size greater than INT32_MAX i.e. 
2147483647
   # Note: the size of each dimension is still bounded by INT32_MAX
   USE_INT64_TENSOR_SIZE = 0
   
   #----------------------------
   # plugins
   #----------------------------
   
   # whether to use torch integration. This requires installing torch.
   # TORCH_PATH = $(HOME)/torch
   # MXNET_PLUGINS += plugin/torch/torch.mk
   USE_BLAS = apple
   ADD_LDFLAGS += -L/usr/local/lib/graphviz/
   
   ## Steps to reproduce
   (Paste the commands you ran that produced the error.)
   
   1.
   2.
   
   ## What have you tried to solve it?
   
   1.
   2.
   

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