start.sh

#!/bin/bash
IMAGE_NAME="pytorch/torchserve:latest"

for arg in "$@"
do
    case $arg in
        -h|--help)
          echo "options:"
          echo "-h, --help  show brief help"
          echo "-g, --gpu specify to use gpu"
          echo "-d, --gpu_devices to use specific gpu device ids"
          exit 0
          ;;
        -g|--gpu)
          DOCKER_RUNTIME="--gpus all"
          IMAGE_NAME="pytorch/torchserve:dev-gpu"
          shift
          ;;
	-d|--gpu_devices)
          if test $
          then
            IMAGE_NAME="pytorch/torchserve:dev-gpu"
            GPU_DEVICES='--gpus '"\"device=$2\""''
            shift
          fi
          shift
          ;;
    esac
done
echo "Starting $IMAGE_NAME docker image"

docker run $DOCKER_RUNTIME $GPU_DEVICES -d --rm -it -p 127.0.0.1:8080:8080 -p 127.0.0.1:8081:8081 $IMAGE_NAME > /dev/null 2>&1

container_id=$(docker ps --filter="ancestor=$IMAGE_NAME" -q | xargs)

sleep 30

echo "Successfully started torchserve in docker"

echo "Registering resnet-18 model"
response=$(curl --write-out %{http_code} --silent --output /dev/null --retry 5 -X POST "http://localhost:8081/models?url=https://torchserve.pytorch.org/mar_files/resnet-18.mar&initial_workers=1&synchronous=true")

if [ ! "$response" == 200 ]
then
    echo "failed to register model with torchserve"
else
    echo "successfully registered resnet-18 model with torchserve"
fi

echo "TorchServe is up and running with resnet-18 model"
echo "Management APIs are accessible on http://127.0.0.1:8081"
echo "Inference APIs are accessible on http://127.0.0.1:8080"
echo "For more details refer TorchServe documentation"
echo "To stop docker container for TorchServe use command : docker container stop $container_id"