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ARG PYTORCH="1.9.0"
ARG CUDA="11.1"
ARG CUDNN="8"
FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
ARG MMCV="2.0.0rc4"
ARG MMDET="3.3.0"
ARG MMDET3D="1.4.0"
ENV PYTHONUNBUFFERED TRUE
# Avoid Public GPG key error
# https://github.com/NVIDIA/nvidia-docker/issues/1631
RUN rm /etc/apt/sources.list.d/cuda.list \
&& rm /etc/apt/sources.list.d/nvidia-ml.list \
&& apt-key del 7fa2af80 \
&& apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub \
&& apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
# (Optional, use Mirror to speed up downloads)
# RUN sed -i 's/http:\/\/archive.ubuntu.com\/ubuntu\//http:\/\/mirrors.aliyun.com\/ubuntu\//g' /etc/apt/sources.list
# Install the required packages
RUN apt-get update && \
DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
ca-certificates \
g++ \
openjdk-11-jre-headless \
# MMDet3D Requirements
ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
&& rm -rf /var/lib/apt/lists/*
ENV PATH="/opt/conda/bin:$PATH" \
FORCE_CUDA="1"
# TORCHSEVER
RUN pip install torchserve torch-model-archiver
# MMLAB
ARG PYTORCH
ARG CUDA
RUN pip install openmim
RUN mim install mmengine
RUN mim install mmcv==${MMCV}
RUN mim install mmdet==${MMDET}
RUN mim install mmdet3d==${MMDET3D}
RUN useradd -m model-server \
&& mkdir -p /home/model-server/tmp
COPY entrypoint.sh /usr/local/bin/entrypoint.sh
RUN chmod +x /usr/local/bin/entrypoint.sh \
&& chown -R model-server /home/model-server
COPY config.properties /home/model-server/config.properties
RUN mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store
EXPOSE 8080 8081 8082
USER model-server
WORKDIR /home/model-server
ENV TEMP=/home/model-server/tmp
ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
CMD ["serve"]
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