Dockerfile

ARG TF_SERVING_VERSION=latest
ARG TF_SERVING_BUILD_IMAGE=tensorflow/serving:${TF_SERVING_VERSION}-devel

FROM ${TF_SERVING_BUILD_IMAGE} as build_image
FROM ubuntu:18.04

ARG TF_SERVING_VERSION_GIT_BRANCH=master
ARG TF_SERVING_VERSION_GIT_COMMIT=head

LABEL maintainer="gvasudevan@google.com"
LABEL tensorflow_serving_github_branchtag=${TF_SERVING_VERSION_GIT_BRANCH}
LABEL tensorflow_serving_github_commit=${TF_SERVING_VERSION_GIT_COMMIT}

RUN apt-get update && apt-get install -y --no-install-recommends \
        ca-certificates \
        && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

# Install TF Serving pkg
COPY --from=build_image /usr/local/bin/tensorflow_model_server /usr/bin/tensorflow_model_server
COPY ./saved_model_z /models/saved_model_z
# Expose ports
# gRPC
EXPOSE 8500

# REST
EXPOSE 8501

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

# The only required piece is the model name in order to differentiate endpoints
ENV MODEL_NAME=saved_model_z

# 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\
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

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