# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

ARG PYTORCH="1.6.0"
ARG CUDA="10.1"
ARG CUDNN="7"

FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel

MAINTAINER Randy DeFauw <rddefauw@amazon.com>

ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX"
ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../"

RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub


RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 nginx ca-certificates \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*

# Install MMCV, MMDetection and MMSegmentation
RUN pip install mmcv-full==1.5.0 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
RUN pip install mmdet==2.19.0
RUN pip install mmsegmentation==0.20.0

# Install MMDetection3D
RUN conda clean --all
RUN git clone https://github.com/open-mmlab/mmdetection3d.git /mmdetection3d
WORKDIR /mmdetection3d
ENV FORCE_CUDA="1"
RUN pip install -r requirements/build.txt
RUN pip install --no-cache-dir -e .

RUN pip install flask gevent gunicorn

# Set some environment variables. PYTHONUNBUFFERED keeps Python from buffering our standard
# output stream, which means that logs can be delivered to the user quickly. PYTHONDONTWRITEBYTECODE
# keeps Python from writing the .pyc files which are unnecessary in this case. We also update
# PATH so that the train and serve programs are found when the container is invoked.

ENV PYTHONUNBUFFERED=TRUE
ENV PYTHONDONTWRITEBYTECODE=TRUE
ENV PATH="/opt/program:${PATH}"

# Set up the program in the image
COPY mm3d /opt/program
RUN chmod +x /opt/program/serve
WORKDIR /opt/program