#!/bin/sh
set -e

for tensorflow_version in 1.15 2.4.3 2.5.1 2.6.0; do
  #  Note: CUDA 11.0 only with CUDNN 8
  for BASE_IMAGE in \
    nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04 \
    nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 \
    nvidia/cuda:10.2-cudnn7-runtime-ubuntu18.04 \
    nvidia/cuda:11.0-cudnn8-runtime-ubuntu18.04 \
    nvidia/cuda:11.1-cudnn8-runtime-ubuntu18.04 \
    nvidia/cuda:11.2.1-cudnn8-runtime-ubuntu18.04 \
  ; do

    # According to the docs at https://www.tensorflow.org/install/gpu, we should
    # use different package names depending on the major version of TF.
    if echo $tensorflow_version | grep -q ^1; then
      tensorflow_package=tensorflow-gpu
    else
      tensorflow_package=tensorflow
    fi


    echo -n "$tensorflow_package $tensorflow_version $BASE_IMAGE "
    work_dir=`dirname $0`
    image_id=`docker build -q --build-arg test_nvidia_options="--quiet" --build-arg tensorflow_package=$tensorflow_package --build-arg tensorflow_version=$tensorflow_version --build-arg BASE_IMAGE=$BASE_IMAGE -f assets/Dockerfile $work_dir`
    docker run --gpus all -it --rm -e TF_CPP_MIN_LOG_LEVEL=2 $image_id
    docker rmi $image_id >/dev/null || true
  done
done