136 строки
3.9 KiB
Bash
Executable File
136 строки
3.9 KiB
Bash
Executable File
#!/bin/bash
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set -e
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# <set_variables>
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RAND=`echo $RANDOM`
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ENDPOINT_NAME="endpt-moe-$RAND"
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# </set_variables>
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BASE_PATH=endpoints/online/managed/inference-schema
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# <create_endpoint>
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az ml online-endpoint create -n $ENDPOINT_NAME
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# </create_endpoint>
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# Check if endpoint was successful
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endpoint_status=`az ml online-endpoint show --name $ENDPOINT_NAME --query "provisioning_state" -o tsv `
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echo $endpoint_status
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if [[ $endpoint_status == "Succeeded" ]]
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then
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echo "Endpoint created successfully"
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else
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echo "Endpoint creation failed"
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exit 1
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fi
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# <register_model>
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az ml model create -f $BASE_PATH/model.yml --set version=$RAND
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# </register_model>
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echo "Creating \"standard\" deployment..."
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# <create_standard_deployment>
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az ml online-deployment create -f $BASE_PATH/deployment-standard.yml \
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--set model="azureml:azureml-infschema:$RAND" \
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--set endpoint_name=$ENDPOINT_NAME \
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--all-traffic
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# </create_standard_deployment>
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# Check if deployment was successful
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deploy_status=`az ml online-deployment show --name infsrv-standard --endpoint $ENDPOINT_NAME --query "provisioning_state" -o tsv `
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echo $deploy_status
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if [[ $deploy_status == "Succeeded" ]]
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then
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echo "Deployment completed successfully"
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else
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echo "Deployment failed"
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exit 1
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fi
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# Get key
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echo "Getting access key..."
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KEY=$(az ml online-endpoint get-credentials -n $ENDPOINT_NAME --query primaryKey -o tsv )
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# Get scoring url
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echo "Getting scoring url..."
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SCORING_URL=$(az ml online-endpoint show -n $ENDPOINT_NAME --query scoring_uri -o tsv )
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echo "Scoring url is $SCORING_URL"
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# Get swagger url
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echo "Getting scoring url..."
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SWAGGER_URL=$(az ml online-endpoint show -n $ENDPOINT_NAME --query openapi_uri -o tsv )
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echo "Testing scoring... "
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# <test_standard_scoring>
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curl -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
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-d @$BASE_PATH/sample-inputs/standard.json $SCORING_URL
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# </test_standard_scoring>
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echo "Getting swagger..."
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# <get_standard_swagger>
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curl -H "Authorization: Bearer $KEY" $SWAGGER_URL
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# </get_standard_swagger>
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# <create_numpy_deployment>
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az ml online-deployment create -f $BASE_PATH/deployment-numpy.yml \
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--set model="azureml:azureml-infschema:$RAND" \
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--set endpoint_name=$ENDPOINT_NAME \
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--all-traffic
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# </create_numpy_deployment>
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# Check if deployment was successful
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deploy_status=`az ml online-deployment show --name infsrv-numpy --endpoint $ENDPOINT_NAME --query "provisioning_state" -o tsv `
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echo $deploy_status
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if [[ $deploy_status == "Succeeded" ]]
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then
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echo "Deployment completed successfully"
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else
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echo "Deployment failed"
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exit 1
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fi
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echo "Testing scoring... "
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# <test_numpy_scoring>
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curl -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
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-d @$BASE_PATH/sample-inputs/numpy.json $SCORING_URL
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# </test_numpy_scoring>
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echo "Getting swagger..."
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# <get_numpy_swagger>
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curl -H "Authorization: Bearer $KEY" $SWAGGER_URL
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# </get_numpy_swagger>
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# <create_pandas_deployment>
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az ml online-deployment create -f $BASE_PATH/deployment-pandas.yml \
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--set model="azureml:azureml-infschema:$RAND" \
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--set endpoint_name=$ENDPOINT_NAME \
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--all-traffic
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# </create_pandas_deployment>
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# Check if deployment was successful
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deploy_status=`az ml online-deployment show --name infsrv-pandas --endpoint $ENDPOINT_NAME --query "provisioning_state" -o tsv `
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echo $deploy_status
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if [[ $deploy_status == "Succeeded" ]]
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then
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echo "Deployment completed successfully"
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else
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echo "Deployment failed"
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exit 1
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fi
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echo "Testing scoring... "
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# <test_pandas_scoring>
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curl -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
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-d @$BASE_PATH/sample-inputs/pandas.json $SCORING_URL
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# </test_pandas_scoring>
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echo "Getting swagger..."
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# <get_pandas_swagger>
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curl -H "Authorization: Bearer $KEY" $SWAGGER_URL
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# </get_pandas_swagger>
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# <delete_online_endpoint>
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az ml online-endpoint delete -y -n $ENDPOINT_NAME --no-wait
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# </delete_online_endpoint>
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