google-research

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ARG TF_SERVING_BUILD_IMAGE=google/tf-serving-scann:latest-devel
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FROM ${TF_SERVING_BUILD_IMAGE} as build_image
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FROM ubuntu:20.04
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ARG TF_SERVING_VERSION_GIT_BRANCH=r2.11
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ARG TF_SERVING_VERSION_GIT_COMMIT=7c9bd6b224061a9d4e2df8d80100807ba80f8f4b
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LABEL maintainer="sunphil@google.com"
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LABEL tensorflow_serving_github_branchtag=${TF_SERVING_VERSION_GIT_BRANCH}
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LABEL tensorflow_serving_github_commit=${TF_SERVING_VERSION_GIT_COMMIT}
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RUN apt-get update && apt-get install -y --no-install-recommends \
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    software-properties-common && add-apt-repository -y ppa:ubuntu-toolchain-r/test && \
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    apt-get update && apt-get install -y g++-9 && \
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    apt-get clean && \
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    rm -rf /var/lib/apt/lists/*
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# Install TF Serving pkg
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COPY --from=build_image /usr/local/bin/tensorflow_model_server /usr/bin/tensorflow_model_server
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# Expose ports
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# gRPC
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EXPOSE 8500
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# REST
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EXPOSE 8501
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# Set where models should be stored in the container
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ENV MODEL_BASE_PATH=/models
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RUN mkdir -p ${MODEL_BASE_PATH}
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# The only required piece is the model name in order to differentiate endpoints
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ENV MODEL_NAME=model
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# Create a script that runs the model server so we can use environment variables
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# while also passing in arguments from the docker command line
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RUN echo '#!/bin/bash \n\n\
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tensorflow_model_server --port=8500 --rest_api_port=8501 \
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--model_name=${MODEL_NAME} --model_base_path=${MODEL_BASE_PATH}/${MODEL_NAME} \
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"$@"' > /usr/bin/tf_serving_entrypoint.sh \
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&& chmod +x /usr/bin/tf_serving_entrypoint.sh
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ENTRYPOINT ["/usr/bin/tf_serving_entrypoint.sh"]
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