Dockerfile.gpu

# See all tag variants at https://hub.docker.com/r/tensorflow/tensorflow/tags/
# build with `ln -sf Dockerfile.gpu Dockerfile && docker build --network=host -t {container-name} .`
# NOTE(jwd) - if you wish to use this implementation, you must install nvidia-docker v2.0
# see https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0) for steps
FROM tensorflow/tensorflow:latest-gpu-py3-jupyter

## modify below
ARG username=gdl
ARG groupid=1000
ARG userid=1000
## end modify

RUN apt-get update && apt-get install -y \
    graphviz \
    libgraphviz-dev

COPY ./requirements.txt /
RUN python3 -m pip install --upgrade pip
RUN pip install -r /requirements.txt

# -m option creates a fake writable home folder for Jupyter.
RUN groupadd -g $groupid $username \
    && useradd -m -r -u $userid -g $username $username
USER $username

VOLUME ["/GDL"]
WORKDIR /GDL

CMD ["jupyter", "notebook", "--no-browser", "--ip=0.0.0.0", "/GDL"]