MXNet Arm Cross Compilation Config and Dockerfile to build a relatively portable armv6 linux binary.

arm.crosscompile.mk

#-------------------------------------------------------------------------------
#  Template configuration for compiling mxnet
#
#  If you want to change the configuration, please use the following
#  steps. Assume you are on the root directory of mxnet. First copy the this
#  file so that any local changes will be ignored by git
#
#  $ cp make/config.mk .
#
#  Next modify the according entries, and then compile by
#
#  $ make
#
#  or build in parallel with 8 threads
#
#  $ make -j8
#-------------------------------------------------------------------------------

#---------------------
# We do not assign compilers here.  Often when cross-compiling these will already 
# be set correctly.
#--------------------

export NVCC = nvcc

# whether compile with options for MXNet developer
DEV = 0

# whether compile with debug
DEBUG = 0

# whether compiler with profiler
USE_PROFILER =

# the additional link flags you want to add
# TODO: Move flags here
ADD_LDFLAGS=-static-libstdc++

# 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 use CuDNN R3 library
USE_CUDNN = 0

# whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
USE_NVRTC = 0

# whether use opencv during compilation
# you can disable it, however, you will not able to use
# imbin iterator
USE_OPENCV = 0

# use openmp for parallelization
USE_OPENMP = 1

# MKL ML Library for Intel CPU/Xeon Phi
# Please refer to MKL_README.md for details

# MKL ML Library folder, need to be root for /usr/local
# Change to User Home directory for standard user
# For USE_BLAS!=mkl only
MKLML_ROOT=/usr/local

# whether use MKL2017 library
USE_MKL2017 = 0

# whether use MKL2017 experimental feature for high performance
# Prerequisite USE_MKL2017=1
USE_MKL2017_EXPERIMENTAL = 0

# whether use NNPACK library
USE_NNPACK = 0

# For arm builds we're using openblas
USE_BLAS = openblas

# 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 ($(USE_MKL2017), 0)
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
endif
else
USE_STATIC_MKL = NONE
endif

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

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

# whether to use torch integration. This requires installing torch.
# You also need to add TORCH_PATH/install/lib to your LD_LIBRARY_PATH
# TORCH_PATH = $(HOME)/torch
# MXNET_PLUGINS += plugin/torch/torch.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

Dockerfile

# -*- mode: dockerfile -*-
# Dockerfile to build libmxnet.so for armv6
FROM dockcross/linux-armv6

ENV ARCH armv6l
ENV BUILD_OPTS "USE_BLAS=openblas USE_SSE=0 USE_OPENCV=0"
ENV CC /usr/bin/arm-linux-gnueabihf-gcc
ENV CXX /usr/bin/arm-linux-gnueabihf-g++
ENV FC /usr/bin/arm-linux-gnueabihf-gfortran
ENV HOSTCC gcc

WORKDIR /work

# Build OpenBLAS
ADD https://api.github.com/repos/xianyi/OpenBLAS/git/refs/heads/master openblas_version.json
RUN git clone https://github.com/xianyi/OpenBLAS.git && \
    cd OpenBLAS && \
    git reset --hard 46c9357c72801f4e186b56abcf7b75d3fad767ab && \
    make -j$(nproc) TARGET=ARMV6 && \
    make install && \
    ln -s /opt/OpenBLAS/lib/libopenblas.so /usr/lib/gcc/arm-linux-gnueabihf/libopenblas.so && \
    ln -s /opt/OpenBLAS/lib/libopenblas.a /usr/lib/gcc/arm-linux-gnueabihf/libopenblas.a && \
    ln -s /opt/OpenBLAS/lib/libopenblas.a /usr/lib/gcc/arm-linux-gnueabihf/liblapack.a && \
    ln -s /opt/OpenBLAS/lib/libopenblas.a /usr/lib/liblapack.a

ENV LD_LIBRARY_PATH /opt/OpenBLAS/lib
ENV CPLUS_INCLUDE_PATH /opt/OpenBLAS/include

# Build MXNet
ADD https://api.github.com/repos/apache/incubator-mxnet/git/refs/heads/master mxnet_version.json
RUN git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet

WORKDIR /work/mxnet
ADD arm.crosscompile.mk make/config.mk
RUN make -j$(nproc) $BUILD_OPTS

WORKDIR /work/build/
RUN cp /work/mxnet/lib/* .

Thanks for your work here.

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