Dockerfile for Facebook Detectron2

Dockerfile

FROM nvidia/cuda:11.1.1-cudnn8-devel-centos8

RUN cd /etc/yum.repos.d/ && \
    sed -i 's/mirrorlist/#mirrorlist/g' /etc/yum.repos.d/CentOS-* && \
    sed -i 's|#baseurl=http://mirror.centos.org|baseurl=http://vault.centos.org|g' /etc/yum.repos.d/CentOS-* && \
    dnf check-update; dnf install -y ca-certificates python38 python38-devel git sudo which gcc-c++ mesa-libGL && \
    dnf clean all

RUN alternatives --set python /usr/bin/python3 && alternatives --install /usr/bin/pip pip /usr/bin/pip3 1

# create a non-root user
ARG USER_ID=1000
RUN useradd -m --no-log-init --system  --uid ${USER_ID} appuser -g wheel
RUN echo '%wheel ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
USER appuser
WORKDIR /home/appuser

ENV PATH="/home/appuser/.local/bin:${PATH}"

# install dependencies
# See https://pytorch.org/ for other options if you use a different version of CUDA
ARG CXX="g++"
RUN pip install --user tensorboard ninja cmake opencv-python opencv-contrib-python  # cmake from apt-get is too old
RUN pip install --user torch==1.10 torchvision==0.11.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html

RUN pip install --user 'git+https://github.com/facebookresearch/fvcore'
# install detectron2
RUN git clone https://github.com/facebookresearch/detectron2 detectron2_repo
# set FORCE_CUDA because during `docker build` cuda is not accessible
ENV FORCE_CUDA="1"
# This will by default build detectron2 for all common cuda architectures and take a lot more time,
# because inside `docker build`, there is no way to tell which architecture will be used.
ARG TORCH_CUDA_ARCH_LIST="Kepler;Kepler+Tesla;Maxwell;Maxwell+Tegra;Pascal;Volta;Turing"
ENV TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST}"

RUN pip install --user -e detectron2_repo

# Set a fixed model cache directory.
ENV FVCORE_CACHE="/tmp"
WORKDIR /home/appuser/detectron2_repo

# run detectron2 under user "appuser":
# curl -o input.jpg http://images.cocodataset.org/val2017/000000439715.jpg
# python3 demo/demo.py  \
	#--config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
	#--input input.jpg --output outputs/ \
	#--opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl