Added a Dockerfile for running convenience and building convenience

Dockerfile

@@ -0,0 +1,74 @@
+FROM ubuntu:18.04
+
+# Due to nVidia not officially supporting CUDA 9.0 in Ubuntu 18.04
+# CUDA 9.0 is manually installed from the Ubuntu 16.04 repositories
+# If in the future Nvidia releases CUDA 9.0 on Ubuntu 18.04, you can skip this part
+RUN apt-get update && apt-get install -y --no-install-recommends ca-certificates apt-transport-https gnupg2 curl && \
+        curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub | apt-key add - && \
+        echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/cuda.list && \
+        echo "deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list
+
+ENV CUDA_VERSION 9.0.176
+ENV NVIDIA_VISIBLE_DEVICES all
+ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
+ENV NVIDIA_REQUIRE_CUDA "cuda>=9.0"
+
+ENV NCCL_VERSION 2.2.12
+ENV CUDNN_VERSION 7.1.4.18
+ENV CUDA_PKG_VERSION 9-0=$CUDA_VERSION-1
+
+ENV ELF_FOLDER /go-elf
+ENV MINICONDA_INSTALL_SCRIPT_NAME Miniconda3.sh
+
+# nvidia-docker 1.0
+LABEL com.nvidia.volumes.needed="nvidia_driver"
+LABEL com.nvidia.cuda.version="${CUDA_VERSION}"
+LABEL com.nvidia.cudnn.version="${CUDNN_VERSION}"
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        cuda-cudart-$CUDA_PKG_VERSION && \
+        ln -s cuda-9.0 /usr/local/cuda
+
+
+RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && \
+echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf
+
+ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
+ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64
+
+# install CUDA libs
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        cuda-libraries-$CUDA_PKG_VERSION \
+        cuda-cublas-9-0=9.0.176.3-1 \
+        libnccl2=$NCCL_VERSION-1+cuda9.0
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        libcudnn7=$CUDNN_VERSION-1+cuda9.0 && \
+        rm -rf /var/lib/apt/lists/*
+
+# CUDA Has been fully installed, now install the dependencies for ELF
+
+RUN mkdir -p ${ELF_FOLDER}
+
+RUN apt update -y && apt install -y cmake git libboost-all-dev libzmq3-dev
+
+ADD https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh ${ELF_FOLDER}/${MINICONDA_INSTALL_SCRIPT_NAME}
+RUN chmod +x ${ELF_FOLDER}/${MINICONDA_INSTALL_SCRIPT_NAME}
+RUN ${ELF_FOLDER}/${MINICONDA_INSTALL_SCRIPT_NAME} -b
+ENV PATH="${PATH}:/root/miniconda3/bin"
+RUN conda install -c pytorch pytorch-nightly cuda90 numpy zeromq pyzmq
+
+RUN mkdir -p ${ELF_FOLDER}/ELF
+ADD . ${ELF_FOLDER}/ELF
+WORKDIR ${ELF_FOLDER}/ELF
+
+RUN git submodule sync && git submodule update --init --recursive 
+
+# Use the Conda environment to compile ELF
+RUN bash -c "source activate base && make -j4"
+
+# Install the pretrained model
+ADD https://github.com/pytorch/ELF/releases/download/pretrained-go-19x19-v0/pretrained-go-19x19-v0.bin ${ELF_FOLDER}/ELF
+
+# Set up the interactive environment
+CMD bash

README.rst

@@ -92,6 +92,8 @@ You also need to install PyTorch from source (see instructions `here`__). As an
 
 __ https://github.com/pytorch/pytorch#from-source
 
+A Dockerfile has been provided if you wish to build ELF using Docker.
+
 Building
 ========