Dockerfile

# Edit the base image here, e.g., to use 
# TENSORFLOW (https://hub.docker.com/r/tensorflow/tensorflow/) 
# or a different PYTORCH (https://hub.docker.com/r/pytorch/pytorch/) base image
FROM pytorch/pytorch:1.6.0-cuda10.1-cudnn7-runtime

RUN apt-get update
RUN apt-get install -y gcc

RUN groupadd -r algorithm && useradd -m --no-log-init -r -g algorithm algorithm

RUN mkdir -p /opt/algorithm /input /output \
    && chown algorithm:algorithm /opt/algorithm /input /output
USER algorithm

WORKDIR /opt/algorithm

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

RUN python -m pip install --user -U pip

# Copy all required files such that they are available within the docker image (code, weights, ...)
COPY --chown=algorithm:algorithm requirements.txt /opt/algorithm/

COPY --chown=algorithm:algorithm model/ /opt/algorithm/model/
COPY --chown=algorithm:algorithm util/ /opt/algorithm/util/
COPY --chown=algorithm:algorithm model_weights/ /opt/algorithm/checkpoints/
COPY --chown=algorithm:algorithm process.py /opt/algorithm/
COPY --chown=algorithm:algorithm detection.py /opt/algorithm/

# Install required python packages via pip - you may adapt the requirements.txt to your needs
RUN python -m pip install --user -rrequirements.txt

# Entrypoint to your python code - executes process.py as a script
ENTRYPOINT python -m process $0 $@

## ALGORITHM LABELS ##

# These labels are required
LABEL nl.diagnijmegen.rse.algorithm.name=MitosisDetection

# These labels are required and describe what kind of hardware your algorithm requires to run.
LABEL nl.diagnijmegen.rse.algorithm.hardware.cpu.count=2
LABEL nl.diagnijmegen.rse.algorithm.hardware.cpu.capabilities=()
LABEL nl.diagnijmegen.rse.algorithm.hardware.memory=16G
LABEL nl.diagnijmegen.rse.algorithm.hardware.gpu.count=1
LABEL nl.diagnijmegen.rse.algorithm.hardware.gpu.cuda_compute_capability=6.0
LABEL nl.diagnijmegen.rse.algorithm.hardware.gpu.memory=8G