test-nvidia/run-docker

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#!/bin/sh
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set -e
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for tensorflow_version in 1.15 2.4.3 2.5.1 2.6.0; do
BASE_IMAGE=invalid
case "$tensorflow_version" in
1.15) BASE_IMAGE=nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04;;
2.4.3) BASE_IMAGE=nvidia/cuda:11.0-cudnn8-runtime-ubuntu18.04;;
2.5.1) BASE_IMAGE=nvidia/cuda:11.2.1-cudnn8-runtime-ubuntu18.04;;
2.6.0) BASE_IMAGE=nvidia/cuda:11.2.1-cudnn8-runtime-ubuntu18.04;;
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esac
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# 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 "== $tensorflow_package $tensorflow_version $BASE_IMAGE"
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work_dir=`dirname $0`
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image_id=`docker build -q --build-arg tensorflow_package=$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=1 $image_id
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docker rmi $image_id >/dev/null || true
done