FROM ubuntu:18.04

LABEL maintainer="Amazon AI"
LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true

ARG PYTHON=python3
ARG PIP=pip3
ARG TFS_SHORT_VERSION=1.14

# See http://bugs.python.org/issue19846
ENV LANG=C.UTF-8
# Python won’t try to write .pyc or .pyo files on the import of source modules
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
ENV SAGEMAKER_TFS_VERSION="${TFS_SHORT_VERSION}"
ENV PATH="$PATH:/sagemaker"
ENV LD_LIBRARY_PATH='/usr/local/lib:$LD_LIBRARY_PATH'
ENV MODEL_BASE_PATH=/models
# The only required piece is the model name in order to differentiate endpoints
ENV MODEL_NAME=model

# nginx + njs
RUN apt-get update \
 && apt-get -y install --no-install-recommends curl gnupg2 ca-certificates git wget vim build-essential zlib1g-dev \
 && curl -s http://nginx.org/keys/nginx_signing.key | apt-key add - \
 && echo 'deb http://nginx.org/packages/ubuntu/ bionic nginx' >> /etc/apt/sources.list \
 && apt-get update \
 && apt-get -y install --no-install-recommends nginx nginx-module-njs python3 python3-pip python3-setuptools \
 && apt-get clean \
 && rm -rf /var/lib/apt/lists/*

RUN ${PIP} --no-cache-dir install --upgrade pip setuptools

# cython, falcon, gunicorn, grpc
RUN ${PIP} install --no-cache-dir \
    awscli==1.16.196 \
    cython==0.29.12 \
    falcon==2.0.0 \
    gunicorn==19.9.0 \
    gevent==1.4.0 \
    requests==2.22.0 \
    grpcio==1.24.1 \
    protobuf==3.10.0 \
# using --no-dependencies to avoid installing tensorflow binary
 && ${PIP} install --no-dependencies --no-cache-dir \
    tensorflow-serving-api==1.14.0

COPY ./ /

# Some TF tools expect a "python" binary
RUN ln -s $(which ${PYTHON}) /usr/local/bin/python

RUN curl https://tensorflow-aws.s3-us-west-2.amazonaws.com/MKL-Libraries/libiomp5.so -o /usr/local/lib/libiomp5.so
RUN curl https://tensorflow-aws.s3-us-west-2.amazonaws.com/MKL-Libraries/libmklml_intel.so -o /usr/local/lib/libmklml_intel.so

RUN curl https://tensorflow-aws.s3-us-west-2.amazonaws.com/1.14/Serving/CPU-WITH-MKL/tensorflow_model_server -o tensorflow_model_server && \
chmod 555 tensorflow_model_server && cp tensorflow_model_server /usr/bin/tensorflow_model_server && \
rm -f tensorflow_model_server

# Expose ports
# gRPC and REST
EXPOSE 8500 8501

# Set where models should be stored in the container
RUN mkdir -p ${MODEL_BASE_PATH}

# Create a script that runs the model server so we can use environment variables
# while also passing in arguments from the docker command line
RUN echo '#!/bin/bash \n\n' > /usr/bin/tf_serving_entrypoint.sh \
 && echo '/usr/bin/tensorflow_model_server --port=8500 --rest_api_port=8501 --model_name=${MODEL_NAME} --model_base_path=${MODEL_BASE_PATH}/${MODEL_NAME} "$@"' >> /usr/bin/tf_serving_entrypoint.sh \
 && chmod +x /usr/bin/tf_serving_entrypoint.sh

CMD ["/usr/bin/tf_serving_entrypoint.sh"]