azureml-examples/cli/deploy-triton-managed-online-endpoint.sh

set -e

BASE_PATH=endpoints/online/triton/single-model

# <installing-requirements>
pip install numpy
pip install tritonclient[http]
pip install pillow
pip install gevent
# </installing-requirements>

# <set_endpoint_name>
export ENDPOINT_NAME=triton-single-endpt-`echo $RANDOM`
# </set_endpoint_name>

# <create_endpoint>
az ml online-endpoint create -n $ENDPOINT_NAME -f $BASE_PATH/create-managed-endpoint.yaml
# </create_endpoint>

# <create_deployment>
az ml online-deployment create --name blue --endpoint $ENDPOINT_NAME -f $BASE_PATH/create-managed-deployment.yaml --all-traffic
# </create_deployment>

# <get_status>
az ml online-endpoint show -n $ENDPOINT_NAME
# </get_status>

# check if create was successful
endpoint_status=`az ml online-endpoint show --name $ENDPOINT_NAME --query "provisioning_state" -o tsv`
echo $endpoint_status
if [[ $endpoint_status == "Succeeded" ]]
then
  echo "Endpoint created successfully"
else
  echo "Endpoint creation failed"
  exit 1
fi

deploy_status=`az ml online-deployment show --name blue --endpoint $ENDPOINT_NAME --query "provisioning_state" -o tsv`
echo $deploy_status
if [[ $deploy_status == "Succeeded" ]]
then
  echo "Deployment completed successfully"
else
  echo "Deployment failed"
  exit 1
fi

# <get_scoring_uri>
scoring_uri=$(az ml online-endpoint show -n $ENDPOINT_NAME --query scoring_uri -o tsv)
scoring_uri=${scoring_uri%/*}
# </get_scoring_uri>

# <get_token>
auth_token=$(az ml online-endpoint get-credentials -n $ENDPOINT_NAME --query accessToken -o tsv)
# </get_token>

# <check_scoring_of_model>
python $BASE_PATH/triton_densenet_scoring.py --base_url=$scoring_uri --token=$auth_token --image_path $BASE_PATH/data/peacock.jpg
# </check_scoring_of_model>

# <delete_endpoint>
az ml online-endpoint delete -n $ENDPOINT_NAME --yes
# </delete_endpoint>