Dockerfile.compute_worker_gpu

FROM --platform=linux/amd64 python:3.9

# This makes output not buffer and return immediately, nice for seeing results in stdout
ENV PYTHONUNBUFFERED 1

# Install Docker
RUN apt-get update && curl -fsSL https://get.docker.com | sh


# Nvidia Container Toolkit for cuda use with docker
# [source](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
RUN curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
    && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
RUN apt-get update -y;
RUN apt-get install -y nvidia-container-toolkit
# Make it explicit that we're using GPUs
# BB - not convinced we need this
ENV USE_GPU 1

RUN curl -sSL https://install.python-poetry.org | python3 - --version 1.8.3
# Poetry location so future commands (below) work
ENV PATH $PATH:/root/.local/bin
# Want poetry to use system python of docker container
RUN poetry config virtualenvs.create false
RUN poetry config virtualenvs.in-project false
COPY ./compute_worker/pyproject.toml ./
COPY ./compute_worker/poetry.lock ./
RUN poetry install

ADD compute_worker .

CMD celery -A compute_worker worker \
    -l info \
    -Q compute-worker \
    -n compute-worker@%n \
    --concurrency=1