Build error

[Johnny-dai-git]

Hi, I try to build the mxnet with make command follow the instruction within this link:
https://mxnet.apache.org/versions/1.7.0/get_started/build_from_source#install-mxnet-for-python

I am using mxnet 1.6.0 source code running on ubuntu 20.04 LTS version.

I did not change any thing within the config.mk file.(All default options):

My PC is only CPU platform.

However, after I cd into the root of mxnet folder and try to compile with make command. Error shows up like this.

usr/bin/ld: cannot find -lcblas
collect2: error: ld returned 1 exit status
make: *** [Makefile:596: lib/libmxnet.so] Error 1
make: *** Waiting for unfinished jobs....
/usr/bin/ld: cannot find -lcblas
collect2: error: ld returned 1 exit status

Do I need to use cmake instread

My config.mk file.

#---------------------
# choice of compiler
#--------------------

ifndef CC
export CC = gcc
endif
ifndef CXX
export CXX = g++
endif
ifndef NVCC
export NVCC = nvcc
endif

# whether compile with options for MXNet developer
DEV = 0

# whether compile with debug
DEBUG = 0

# whether to turn on segfault signal handler to log the stack trace
USE_SIGNAL_HANDLER =

# the additional link flags you want to add
ADD_LDFLAGS =

# the additional compile flags you want to add
ADD_CFLAGS =

# whether to build operators written in TVM
USE_TVM_OP = 0

#---------------------------------------------
# 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 to use NVTX when profiling
USE_NVTX = 0

#whether to use NCCL library
USE_NCCL = 0
#add the path to NCCL library
USE_NCCL_PATH = NONE

# 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

#whether use libjpeg-turbo for image decode without OpenCV wrapper
USE_LIBJPEG_TURBO = 0
#add the path to libjpeg-turbo library
USE_LIBJPEG_TURBO_PATH = NONE

# use openmp for parallelization
USE_OPENMP = 1

# whether use MKL-DNN library: 0 = disabled, 1 = enabled
# if USE_MKLDNN is not defined, MKL-DNN will be enabled by default on x86 Linux.
# you can disable it explicity with USE_MKLDNN = 0
USE_MKLDNN =

# whether use NNPACK library
USE_NNPACK = 0

# choose the version of blas you want to use
# can be: mkl, blas, atlas, openblas
# in default use atlas for linux while apple for osx
UNAME_S := $(shell uname -s)
ifeq ($(UNAME_S), Darwin)
USE_BLAS = apple
else
USE_BLAS = atlas
endif

# whether use lapack during compilation
# only effective when compiled with blas versions openblas/apple/atlas/mkl
USE_LAPACK = 1

# path to lapack library in case of a non-standard installation
USE_LAPACK_PATH =

# 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

# If use MKL only for BLAS, choose static link automatically to allow python wrapper
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
else
USE_STATIC_MKL = NONE
endif

#----------------------------
# Settings for power and arm arch
#----------------------------
ARCH := $(shell uname -a)
ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
	USE_SSE=0
	USE_F16C=0
else
	USE_SSE=1
endif

#----------------------------
# F16C instruction support for faster arithmetic of fp16 on CPU
#----------------------------
# For distributed training with fp16, this helps even if training on GPUs
# If left empty, checks CPU support and turns it on.
# For cross compilation, please check support for F16C on target device and turn off if necessary.
USE_F16C =

#----------------------------
# 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

#----------------------------
# performance settings
#----------------------------
# Use operator tuning
USE_OPERATOR_TUNING = 1

# Use gperftools if found
# Disable because of #8968
USE_GPERFTOOLS = 0

# path to gperftools (tcmalloc) library in case of a non-standard installation
USE_GPERFTOOLS_PATH =

# Link gperftools statically
USE_GPERFTOOLS_STATIC =

# Use JEMalloc if found, and not using gperftools
USE_JEMALLOC = 1

# path to jemalloc library in case of a non-standard installation
USE_JEMALLOC_PATH =

# Link jemalloc statically
USE_JEMALLOC_STATIC =

#----------------------------
# 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

# Python executable. Needed for cython target
PYTHON = python

#----------------------------
# plugins
#----------------------------

# whether to use caffe integration. This requires installing caffe.
# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
# CAFFE_PATH = $(HOME)/caffe
# MXNET_PLUGINS += plugin/caffe/caffe.mk

# WARPCTC_PATH = $(HOME)/warp-ctc
# MXNET_PLUGINS += plugin/warpctc/warpctc.mk

# whether to use sframe integration. This requires build sframe
# git@github.com:dato-code/SFrame.git
# SFRAME_PATH = $(HOME)/SFrame
# MXNET_PLUGINS += plugin/sframe/plugin.mk

[szha]

From this error message, it looks like the linker wasn't able to find your atlas library: /usr/bin/ld: cannot find -lcblas

I would recommend to use openblas instead. To do that, you can:

• change USE_BLAS = atlas to USE_BLAS = openblas
• install libopenblas-dev using apt

[Johnny-dai-git]

Thank you. I have already make it. Best Regrads, johnny

…

On Fri, Nov 13, 2020 at 10:10 AM Sheng Zha ***@***.***> wrote: From this error message, it looks like the linker wasn't able to find your atlas library: /usr/bin/ld: cannot find -lcblas I would recommend to use openblas instead. To do that, you can: - change USE_BLAS = atlas to USE_BLAS = openblas - install libopenblas-dev using apt — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#19519 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ANDGLBZ3FC4VNUGOI7LAEDTSPVD5DANCNFSM4TS3TL6A> .

-- Yuanjun Dai (he/him) P.hd Department of Computer and Data Sciences Case Western Reserve University Phone: (216)-235-8330 Office: Glennan 505