azureml-examples/cli/batch-score.sh

az --version

set -e

# <set_variables>
export ENDPOINT_NAME="<YOUR_ENDPOINT_NAME>"
export DEPLOYMENT_NAME="<YOUR_DEPLOYMENT_NAME>"
# </set_variables>

export ENDPOINT_NAME=endpt-`echo $RANDOM`
export DEPLOYMENT_NAME="mnist-torch-dpl"

echo "Creating compute"
# <create_compute>
az ml compute create -n batch-cluster --type amlcompute --min-instances 0 --max-instances 5
# </create_compute>

echo "Creating batch endpoint $ENDPOINT_NAME"
# <create_batch_endpoint>
az ml batch-endpoint create --name $ENDPOINT_NAME
# </create_batch_endpoint>

echo "Creating batch deployment nonmlflowdp for endpoint $ENDPOINT_NAME"
# <create_batch_deployment_set_default>
az ml batch-deployment create --file endpoints/batch/deploy-models/mnist-classifier/deployment-torch/deployment.yml --endpoint-name $ENDPOINT_NAME --set-default
# </create_batch_deployment_set_default>

echo "Showing details of the batch endpoint"
# <check_batch_endpooint_detail>
az ml batch-endpoint show --name $ENDPOINT_NAME
# </check_batch_endpooint_detail>

echo "Showing details of the batch deployment"
# <check_batch_deployment_detail>
az ml batch-deployment show --name $DEPLOYMENT_NAME --endpoint-name $ENDPOINT_NAME
# </check_batch_deployment_detail>

sleep 60

echo "Invoking batch endpoint with public URI (MNIST)"
# <start_batch_scoring_job>
JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata.blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
# </start_batch_scoring_job>

echo "Showing job detail"
# <show_job_in_studio>
az ml job show -n $JOB_NAME --web
# </show_job_in_studio>

echo "Stream job logs to console"
# <stream_job_logs_to_console>
az ml job stream -n $JOB_NAME
# </stream_job_logs_to_console>

# <check_job_status>
STATUS=$(az ml job show -n $JOB_NAME --query status -o tsv)
echo $STATUS
if [[ $STATUS == "Completed" ]]
then
  echo "Job completed"
elif [[ $STATUS ==  "Failed" ]]
then
  echo "Job failed"
  exit 1
else 
  echo "Job status not failed or completed"
  exit 2
fi
# </check_job_status>

echo "Invoke batch endpoint with specific output file name"
# <start_batch_scoring_job_configure_output_settings>
export OUTPUT_FILE_NAME=predictions_`echo $RANDOM`.csv
JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata.blob.core.windows.net/data/mnist/sample --input-type uri_folder --output-path azureml://datastores/workspaceblobstore/paths/$ENDPOINT_NAME --set output_file_name=$OUTPUT_FILE_NAME --query name -o tsv)
# </start_batch_scoring_job_configure_output_settings>

echo "Invoke batch endpoint with specific overwrites"
# <start_batch_scoring_job_overwrite>
export OUTPUT_FILE_NAME=predictions_`echo $RANDOM`.csv
JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata.blob.core.windows.net/data/mnist/sample --input-type uri_folder --mini-batch-size 20 --instance-count 5 --query name -o tsv)
# </start_batch_scoring_job_overwrite>

echo "Stream job detail"
# <stream_job_logs_to_console>
az ml job stream -n $JOB_NAME
# </stream_job_logs_to_console>

# <check_job_status>
STATUS=$(az ml job show -n $JOB_NAME --query status -o tsv)
echo $STATUS
if [[ $STATUS == "Completed" ]]
then
  echo "Job completed"
elif [[ $STATUS ==  "Failed" ]]
then
  echo "Job failed"
  exit 1
else 
  echo "Job status not failed or completed"
  exit 2
fi
# </check_job_status>

echo "List all jobs under the batch deployment"
# <list_all_jobs>
az ml batch-deployment list-jobs --name $DEPLOYMENT_NAME --endpoint-name $ENDPOINT_NAME --query [].name
# </list_all_jobs>

echo "Create a new batch deployment (mnist-keras-dpl), not setting it as default this time"
# <create_new_deployment_not_default>
az ml batch-deployment create --file endpoints/batch/deploy-models/mnist-classifier/deployment-keras/deployment.yml --endpoint-name $ENDPOINT_NAME
# </create_new_deployment_not_default>

echo "Invoke batch endpoint with public data"
# <test_new_deployment>
DEPLOYMENT_NAME="mnist-keras-dpl"
JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --deployment-name $DEPLOYMENT_NAME --input https://azuremlexampledata.blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
# </test_new_deployment>

echo "Show job detail"
# <show_job_in_studio>
az ml job show -n $JOB_NAME --web
# </show_job_in_studio>

echo "Stream job logs to console"
# <stream_job_logs_to_console>
az ml job stream -n $JOB_NAME
# </stream_job_logs_to_console>

# <check_job_status>
STATUS=$(az ml job show -n $JOB_NAME --query status -o tsv)
echo $STATUS
if [[ $STATUS == "Completed" ]]
then
  echo "Job completed"
elif [[ $STATUS ==  "Failed" ]]
then
  echo "Job failed"
  exit 1
else 
  echo "Job status not failed or completed"
  exit 2
fi
# </check_job_status>

echo "Update the batch deployment as default for the endpoint"
# <update_default_deployment>
az ml batch-endpoint update --name $ENDPOINT_NAME --set defaults.deployment_name=$DEPLOYMENT_NAME
# </update_default_deployment>

echo "Verify default deployment. In this example, it should be mlflowdp."
# <verify_default_deployment>
az ml batch-endpoint show --name $ENDPOINT_NAME --query "{Name:name, Defaults:defaults}"
# </verify_default_deployment>

echo "Invoke batch endpoint with the new default deployment with public URI"
# <test_new_default_deployment>
JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata.blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
# </test_new_default_deployment>

echo "Stream job logs to console"
# <stream_job_logs_to_console>
az ml job stream -n $JOB_NAME
# </stream_job_logs_to_console>

# <check_job_status>
STATUS=$(az ml job show -n $JOB_NAME --query status -o tsv)
echo $STATUS
if [[ $STATUS == "Completed" ]]
then
  echo "Job completed"
elif [[ $STATUS ==  "Failed" ]]
then
  echo "Job failed"
  exit 1
else 
  echo "Job status not failed or completed"
  exit 2
fi
# </check_job_status>

echo "Get Scoring URI"
# <get_scoring_uri>
SCORING_URI=$(az ml batch-endpoint show --name $ENDPOINT_NAME --query scoring_uri -o tsv)
# </get_scoring_uri>

echo "Get Token"
# <get_token>
AUTH_TOKEN=$(az account get-access-token --resource https://ml.azure.com --query accessToken -o tsv)
# </get_token>

echo "Invoke batch endpoint with REST API call"
# <start_batch_scoring_job_rest>
RESPONSE=$(curl --location --request POST "$SCORING_URI" \
--header "Authorization: Bearer $AUTH_TOKEN" \
--header "Content-Type: application/json" \
--data-raw "{
  \"properties\": {
    \"dataset\": {
      \"dataInputType\": \"DataUrl\",
      \"Path\": \"https://azuremlexampledata.blob.core.windows.net/data/mnist/sample\"
    }
  }
}")
# </start_batch_scoring_job_rest>

# <check_job_status_rest>
# define how to wait  
wait_for_completion () {
    operation_id=$1
    access_token=$2
    status="unknown"

    while [[ $status != "Completed" && $status != "Succeeded" && $status != "Failed" && $status != "Canceled" ]]
    do
        echo "Getting operation status from: $operation_id"
        operation_result=$(curl --location --request GET $operation_id --header "Authorization: Bearer $access_token")
        # TODO error handling here
        status=$(echo $operation_result | jq -r '.status')
        if [[ -z $status || $status == "null" ]]
        then
            status=$(echo $operation_result | jq -r '.properties.status')
        fi

        # Fail early if job submission failed and there is nothing to poll on
        if [[ -z $status || $status == "null" ]]
        then
            echo "No status found on operation, setting to failed."
            status="Failed"
        fi

        echo "Current operation status: $status"
        sleep 10
    done

    if [[ $status == "Failed" ]]
    then
        error=$(echo $operation_result | jq -r '.error')
        echo "Error: $error"
    fi
}

# get job from invoke response and wait for completion
JOB_ID=$(echo $RESPONSE | jq -r '.id')
JOB_ID_SUFFIX=$(echo ${JOB_ID##/*/})
wait_for_completion $SCORING_URI/$JOB_ID_SUFFIX $AUTH_TOKEN
# </check_job_status_rest>

# <delete_deployment>
az ml batch-deployment delete --name nonmlflowdp --endpoint-name $ENDPOINT_NAME --yes
# </delete_deployment>

# <delete_endpoint>
az ml batch-endpoint delete --name $ENDPOINT_NAME --yes
# </delete_endpoint>