Add dockerfile and usage to README

Dockerfile

@@ -0,0 +1,32 @@
+FROM nvidia/cuda:8.0-cudnn7-devel-ubuntu16.04
+LABEL author=github/tahsinkose
+
+ADD . $HOME/graphnn
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get -y install gcc g++ gfortran wget cpio && \
+  cd /tmp && \
+  wget http://registrationcenter-download.intel.com/akdlm/irc_nas/tec/15275/l_mkl_2019.3.199.tgz && \
+  tar -xzf l_mkl_2019.3.199.tgz && \
+  cd l_mkl_2019.3.199 && \
+  sed -i 's/ACCEPT_EULA=decline/ACCEPT_EULA=accept/g' silent.cfg && \
+  sed -i 's/ARCH_SELECTED=ALL/ARCH_SELECTED=INTEL64/g' silent.cfg && \
+  sed -i 's/COMPONENTS=DEFAULTS/COMPONENTS=;intel-comp-l-all-vars__noarch;intel-comp-nomcu-vars__noarch;intel-openmp__x86_64;intel-tbb-libs__x86_64;intel-mkl-common__noarch;intel-mkl-installer-license__noarch;intel-mkl-core__x86_64;intel-mkl-core-rt__x86_64;intel-mkl-doc__noarch;intel-mkl-doc-ps__noarch;intel-mkl-gnu__x86_64;intel-mkl-gnu-rt__x86_64;intel-mkl-common-ps__noarch;intel-mkl-core-ps__x86_64;intel-mkl-common-c__noarch;intel-mkl-core-c__x86_64;intel-mkl-common-c-ps__noarch;intel-mkl-tbb__x86_64;intel-mkl-tbb-rt__x86_64;intel-mkl-gnu-c__x86_64;intel-mkl-common-f__noarch;intel-mkl-core-f__x86_64;intel-mkl-gnu-f-rt__x86_64;intel-mkl-gnu-f__x86_64;intel-mkl-f95-common__noarch;intel-mkl-f__x86_64;intel-mkl-psxe__noarch;intel-psxe-common__noarch;intel-psxe-common-doc__noarch;intel-compxe-pset/g' silent.cfg && \
+  ./install.sh -s silent.cfg && \
+  cd .. && rm -rf * && \
+  rm -rf /opt/intel/.*.log /opt/intel/compilers_and_libraries_2019.3.199/licensing && \
+  echo "/opt/intel/mkl/lib/intel64" >> /etc/ld.so.conf.d/intel.conf && \
+  ldconfig && \
+  echo "source /opt/intel/mkl/bin/mklvars.sh intel64" >> /etc/bash.bashrc
+
+RUN apt-get -y install libtbb-dev
+
+RUN apt-get install -y mesa-utils -y module-init-tools
+# install nvidia driver
+RUN apt-get install -y binutils
+ADD NVIDIA*.run /tmp/NVIDIA-DRIVER.run
+RUN sh /tmp/NVIDIA-DRIVER.run -a -N --ui=none --no-kernel-module
+RUN rm /tmp/NVIDIA-DRIVER.run
+
+RUN apt-get install -y nano
+ENV CUDA_HOME=/usr/local/cuda
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
+ENV LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH

README.md

@@ -34,6 +34,17 @@ Tested under Ubuntu 14.04, 16.04 and Mac OSX 10.12.6
 
     source {path_to_your_intel_root/name_of_parallel_tool_box}/bin/psxevars.sh
 
+
+#### Docker
+  Dockerfile contains all the required installations (including Intel MKL and TBB) above. Only additional requirement is to provide `NVIDIA*.run` script that will load the same NVIDIA driver of host into the target. Then to build the container, execute:
+
+    docker build -t "graphnn:test" .
+
+  To run it:
+
+    docker run --runtime=nvidia graphnn:test bash
+
+  If above command fails for a reason, refer to https://github.com/NVIDIA/nvidia-docker. If no error occurs, you can simply follow the below instructions and execute them in the container without failure.
 #### Build static library
 
     cp make_common.example make_common