container_simple.def

# For more information on container definition files,
# visit https://sylabs.io/guides/3.7/user-guide/definition_files.html

Bootstrap: docker
# this basically provides pytorch and OS
From: nvcr.io/nvidia/pytorch:20.08-py3
# we don't use pytorch in this example, so you can also get a smaller container
# by using the following base image instead: (uncomment below and comment above)
# From: python:slim

%files
    # Specify here what should be copied to the container when it is built
    # copy prediction script (and other code if necessary)
    ./predict_simple.py /code/predict.py

    # Copy model files (in particular model weights) [not used here], .e.g
    # /path/to/checkpoints/model.ckpt /params/model.ckpt

    # and maybe other stuff
    ./requirements_simple.txt /code/requirements.txt

%post
    # Commands in this block are executed after the files have been copied in %files
    # Install ubuntu packages
    apt update && apt upgrade -y
    # Install your python requirements
    pip install -r /code/requirements.txt

%runscript
    # This is executed inside the container when it is run
    # The following arguments will be passed:
    # -i: path to input folder
    # -o: path to output folder
    echo "Starting prediction..."
    python /code/predict.py "$@"

%labels
    # Please add these fields
    Author "Maximilian Zenk"
    Team "Organizer"
    Institution "German cancer research center (DKFZ)"
    Email "m.zenk@dkfz-heidelberg.de"

%help
    # optional
    This singularity container predicts segmentations on the images in the input folder (-i)
    and saves them to the output folder (-o). This example uses a dummy prediction algorithm.