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update dependency
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‎README.md

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@@ -12,6 +12,7 @@ Here we list the latest versions of our supported kernel images.
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| Deep-Learning Framework | Image Name | Version | Batch | Query | Input Hook | TTY | Runtime Impl. |
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|-------------------------|----------------------------|---------|-------|-------|------------|-----|-------------------|
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| TensorFlow | `lablup/python-tensorflow` | 1.13.0 | O | O\* | O | | Bundled w/Keras 2 |
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| TensorFlow | `lablup/python-tensorflow` | 1.14.0 | O | O\* | O | | Bundled w/Keras 2 |
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| TensorFlow | `lablup/python-tensorflow` | 1.15.4 | O | O\* | O | | Bundled w/Keras 2 |
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| TensorFlow | `lablup/python-tensorflow` | 2.0.3 | O | O\* | O | | Bundled w/Keras 2 |

‎ffmpeg/Dockerfile

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@@ -161,6 +161,10 @@ RUN set -xe && \
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apt-get clean && \
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rm -rf /usr/local/ffmpeg_sources
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# Stream transcoding
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COPY ./scripts/bootstrap.sh /opt/container/bootstrap.sh
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RUN chmod +x /opt/container/bootstrap.sh
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# Backend.AI specifics
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COPY ./service-defs /etc/backend.ai/service-defs
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COPY ./policy.yml /etc/backend.ai/jail/policy.yml

‎ffmpeg/scripts/bootstrap.sh

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#!/bin/sh
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USER_ID=${LOCAL_USER_ID:-9001}
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USER_NAME=work
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export HOME="/home/$USER_NAME"
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su - $USER_NAME -c "touch /home/work/ffmpeglog"
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if (! [ -z ${INPUT_STREAM}] ) && ( ! [ -z ${OUTPUT_STREAM} ] ); then
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echo "nohup /usr/local/bin/ffmpeg -i $INPUT_STREAM -vcodec hevc_nvenc -f rtsp $OUTPUT_STREAM >> /home/work/ffmpeglog &" >> /home/work/ffmpeglog
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su - $USER_NAME -c "nohup /usr/local/bin/ffmpeg -i $INPUT_STREAM -vcodec hevc_nvenc -f rtsp $OUTPUT_STREAM >> /home/work/ffmpeglog &"
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else
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echo "INPUT_STREAM: $INPUT_STREAM" >> /home/work/ffmpeglog
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echo "OUTPUT_STREAM: $OUTPUT_STREAM" >> /home/work/ffmpeglog
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fi

‎python-ff/requirements.txt

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@@ -80,7 +80,7 @@ six==1.12.0
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syntax
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tensorboard==1.13.1
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tensorflow-estimator==1.13.0
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tensorflow-gpu==1.13.1+nv
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tensorflow-gpu==2.4.0
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termcolor==1.1.0
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terminado==0.8.2
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testpath==0.4.2

‎torchserve/Dockerfile

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# syntax = docker/dockerfile:experimental
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#
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# This file can build images for cpu and gpu env. By default it builds image for CPU.
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# Use following option to build image for cuda/GPU: --build-arg BASE_IMAGE=nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
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# Here is complete command for GPU/cuda -
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# $ DOCKER_BUILDKIT=1 docker build --file Dockerfile --build-arg BASE_IMAGE=nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 -t torchserve:latest .
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#
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# Following comments have been shamelessly copied from https://github.com/pytorch/pytorch/blob/master/Dockerfile
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#
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# NOTE: To build this you will need a docker version > 18.06 with
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# experimental enabled and DOCKER_BUILDKIT=1
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#
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# If you do not use buildkit you are not going to have a good time
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#
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# For reference:
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# https://docs.docker.com/develop/develop-images/build_enhancements/
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ARG BASE_IMAGE=ubuntu:18.04
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FROM ${BASE_IMAGE} AS compile-image
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ARG BASE_IMAGE=ubuntu:18.04
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ENV PYTHONUNBUFFERED TRUE
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RUN --mount=type=cache,id=apt-dev,target=/var/cache/apt \
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apt-get update && \
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DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
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ca-certificates \
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g++ \
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python3-dev \
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python3-distutils \
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python3-venv \
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openjdk-11-jre-headless \
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curl \
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&& rm -rf /var/lib/apt/lists/* \
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&& cd /tmp \
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&& curl -O https://bootstrap.pypa.io/get-pip.py \
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&& python3 get-pip.py
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RUN python3 -m venv /home/venv
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ENV PATH="/home/venv/bin:$PATH"
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RUN update-alternatives --install /usr/bin/python python /usr/bin/python3 1 \
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&& update-alternatives --install /usr/local/bin/pip pip /usr/local/bin/pip3 1
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# This is only useful for cuda env
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RUN export USE_CUDA=1
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ARG CUDA_VERSION=""
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RUN TORCH_VER=$(curl --silent --location https://pypi.org/pypi/torch/json | python -c "import sys, json, pkg_resources; releases = json.load(sys.stdin)['releases']; print(sorted(releases, key=pkg_resources.parse_version)[-1])") && \
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TORCH_VISION_VER=$(curl --silent --location https://pypi.org/pypi/torchvision/json | python -c "import sys, json, pkg_resources; releases = json.load(sys.stdin)['releases']; print(sorted(releases, key=pkg_resources.parse_version)[-1])") && \
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if echo "$BASE_IMAGE" | grep -q "cuda:"; then \
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# Install CUDA version specific binary when CUDA version is specified as a build arg
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if [ "$CUDA_VERSION" ]; then \
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pip install --no-cache-dir torch==$TORCH_VER+$CUDA_VERSION torchvision==$TORCH_VISION_VER+$CUDA_VERSION -f https://download.pytorch.org/whl/torch_stable.html; \
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# Install the binary with the latest CUDA version support
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else \
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pip install --no-cache-dir torch torchvision; \
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fi \
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# Install the CPU binary
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else \
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pip install --no-cache-dir torch==$TORCH_VER+cpu torchvision==$TORCH_VISION_VER+cpu -f https://download.pytorch.org/whl/torch_stable.html; \
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fi
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RUN pip install --no-cache-dir captum torchtext torchserve torch-model-archiver
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# Final image for production
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FROM ${BASE_IMAGE} AS runtime-image
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ENV PYTHONUNBUFFERED TRUE
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RUN --mount=type=cache,id=apt-dev,target=/var/cache/apt \
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apt-get update && \
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DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
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python3 \
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python3-distutils \
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python3-dev \
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openjdk-11-jre-headless \
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build-essential \
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&& rm -rf /var/lib/apt/lists/* \
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&& cd /tmp
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#RUN useradd -m model-server \
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# && mkdir -p /home/model-server/tmp
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RUN mkdir -p /home/model-server/tmp
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#COPY --chown=model-server --from=compile-image
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COPY --from=compile-image /home/venv /home/venv
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ENV PATH="/home/venv/bin:$PATH"
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COPY dockerd-entrypoint.sh /usr/local/bin/dockerd-entrypoint.sh
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RUN chmod +x /usr/local/bin/dockerd-entrypoint.sh
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#RUN chown -R /home/model-server
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COPY config.properties /home/model-server/config.properties
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RUN mkdir /home/model-server/model-store
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#&& chown -R model-server /home/work/model-server/model-store
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EXPOSE 8080 8081 8082 7070 7071
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#USER model-server
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#WORKDIR /home/model-server
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#ENV TEMP=/home/model-server/tmp
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#ENTRYPOINT ["/usr/local/bin/dockerd-entrypoint.sh"]
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#CMD ["serve"]
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# Backend.AI specifics
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COPY service-defs /etc/backend.ai/service-defs
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LABEL ai.backend.kernelspec="1" \
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ai.backend.envs.corecount="OPENBLAS_NUM_THREADS,OMP_NUM_THREADS,NPROC" \
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ai.backend.features="batch query uid-match user-input" \
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ai.backend.resource.min.cpu="1" \
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ai.backend.resource.min.mem="256m" \
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ai.backend.base-distro="ubuntu18.04" \
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ai.backend.runtime-type="python" \
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ai.backend.runtime-path="/bin/false" \
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ai.backend.service-ports="jupyter:http:8070"
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COPY policy.yml /etc/backend.ai/jail/policy.yml

‎torchserve/Dockerfile.dev

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# syntax = docker/dockerfile:experimental
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#
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# Following comments have been shamelessly copied from https://github.com/pytorch/pytorch/blob/master/Dockerfile
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#
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# NOTE: To build this you will need a docker version > 18.06 with
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# experimental enabled and DOCKER_BUILDKIT=1
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#
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# If you do not use buildkit you are not going to have a good time
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#
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# For reference:
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# https://docs.docker.com/develop/develop-images/build_enhancements/
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ARG BASE_IMAGE=ubuntu:18.04
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ARG BUILD_TYPE=dev
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FROM ${BASE_IMAGE} AS compile-image
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ARG BASE_IMAGE
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ARG BRANCH_NAME=master
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ARG MACHINE_TYPE=cpu
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ARG CUDA_VERSION
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ENV PYTHONUNBUFFERED TRUE
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RUN --mount=type=cache,id=apt-dev,target=/var/cache/apt \
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apt-get update && \
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DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
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fakeroot \
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ca-certificates \
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dpkg-dev \
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sudo \
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g++ \
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git \
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python3-dev \
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build-essential \
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openjdk-11-jdk \
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curl \
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vim \
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&& rm -rf /var/lib/apt/lists/* \
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&& cd /tmp \
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&& curl -O https://bootstrap.pypa.io/get-pip.py \
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&& python3 get-pip.py
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RUN update-alternatives --install /usr/bin/python python /usr/bin/python3 1 \
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&& update-alternatives --install /usr/local/bin/pip pip /usr/local/bin/pip3 1
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RUN pip install -U pip setuptools
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# Build Dev Image
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FROM compile-image AS dev-image
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ARG MACHINE_TYPE=cpu
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ARG CUDA_VERSION
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RUN if [ "$MACHINE_TYPE" = "gpu" ]; then export USE_CUDA=1; fi \
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&& git clone https://github.com/pytorch/serve.git \
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&& cd serve \
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&& git checkout ${BRANCH_NAME} \
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&& if [ -z "$CUDA_VERSION" ]; then python ts_scripts/install_dependencies.py --environment=dev; else python ts_scripts/install_dependencies.py --environment=dev --cuda $CUDA_VERSION; fi \
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&& python ts_scripts/install_from_src.py \
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&& useradd -m model-server \
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&& mkdir -p /home/model-server/tmp \
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&& cp docker/dockerd-entrypoint.sh /usr/local/bin/dockerd-entrypoint.sh \
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&& chmod +x /usr/local/bin/dockerd-entrypoint.sh \
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&& chown -R model-server /home/model-server \
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&& cp docker/config.properties /home/model-server/config.properties \
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&& mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store
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EXPOSE 8080 8081 8082 7070 7071
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USER model-server
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WORKDIR /home/model-server
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ENV TEMP=/home/model-server/tmp
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ENTRYPOINT ["/usr/local/bin/dockerd-entrypoint.sh"]
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CMD ["serve"]
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# Build CodeBuild Image
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FROM compile-image AS codebuild-image
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ENV JAVA_VERSION=11 \
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JAVA_HOME="/usr/lib/jvm/java-11-openjdk-amd64" \
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JDK_HOME="/usr/lib/jvm/java-11-openjdk-amd64" \
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JRE_HOME="/usr/lib/jvm/java-11-openjdk-amd64" \
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ANT_VERSION=1.10.3 \
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MAVEN_HOME="/opt/maven" \
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MAVEN_VERSION=3.5.4 \
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MAVEN_CONFIG="/root/.m2" \
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MAVEN_DOWNLOAD_SHA1="22cac91b3557586bb1eba326f2f7727543ff15e3"
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# Install Maven
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RUN set -ex \
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&& mkdir -p $MAVEN_HOME \
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&& curl -LSso /var/tmp/apache-maven-$MAVEN_VERSION-bin.tar.gz https://apache.org/dist/maven/maven-3/$MAVEN_VERSION/binaries/apache-maven-$MAVEN_VERSION-bin.tar.gz \
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&& echo "$MAVEN_DOWNLOAD_SHA1 /var/tmp/apache-maven-$MAVEN_VERSION-bin.tar.gz" | sha1sum -c - \
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&& tar xzvf /var/tmp/apache-maven-$MAVEN_VERSION-bin.tar.gz -C $MAVEN_HOME --strip-components=1 \
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&& update-alternatives --install /usr/bin/mvn mvn /opt/maven/bin/mvn 10000 \
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&& mkdir -p $MAVEN_CONFIG
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FROM ${BUILD_TYPE}-image AS final-image
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ARG BUILD_TYPE
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RUN echo "${BUILD_TYPE} image creation completed"

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