83 строки
3.2 KiB
Bash
83 строки
3.2 KiB
Bash
# Note: this is based on the deploy-managed-online-endpoint.sh: it just adds autoscale settings in the end
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set -e
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#set the endpoint name from the how-to-deploy excercise
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# <set_endpoint_deployment_name>
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# set your existing endpoint name
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ENDPOINT_NAME=your-endpoint-name
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DEPLOYMENT_NAME=blue
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# </set_endpoint_deployment_name>
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export ENDPOINT_NAME=autoscale-endpt-`echo $RANDOM`
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# create endpoint and deployment
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az ml online-endpoint create --name $ENDPOINT_NAME -f endpoints/online/managed/sample/endpoint.yml
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az ml online-deployment create --name blue --endpoint $ENDPOINT_NAME -f endpoints/online/managed/sample/blue-deployment.yml --all-traffic
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az ml online-endpoint show -n $ENDPOINT_NAME
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# <set_other_env_variables>
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# ARM id of the deployment
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DEPLOYMENT_RESOURCE_ID=$(az ml online-deployment show -e $ENDPOINT_NAME -n $DEPLOYMENT_NAME -o tsv --query "id")
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# ARM id of the deployment. todo: change to --query "id"
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ENDPOINT_RESOURCE_ID=$(az ml online-endpoint show -n $ENDPOINT_NAME -o tsv --query "properties.\"azureml.onlineendpointid\"")
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# set a unique name for autoscale settings for this deployment. The below will append a random number to make the name unique.
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AUTOSCALE_SETTINGS_NAME=autoscale-$ENDPOINT_NAME-$DEPLOYMENT_NAME-`echo $RANDOM`
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# </set_other_env_variables>
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# create autoscale settings. Note if you followed the how-to-deploy doc example, the instance count would have been 1. Now after applying this poilcy, it will scale up 2 (since min count and count are 2).
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# <create_autoscale_profile>
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az monitor autoscale create \
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--name $AUTOSCALE_SETTINGS_NAME \
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--resource $DEPLOYMENT_RESOURCE_ID \
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--min-count 2 --max-count 5 --count 2
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# </create_autoscale_profile>
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# Add rule to default profile: scale up if cpu util > 70 %
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# <scale_out_on_cpu_util>
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az monitor autoscale rule create \
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--autoscale-name $AUTOSCALE_SETTINGS_NAME \
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--condition "CpuUtilizationPercentage > 70 avg 5m" \
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--scale out 2
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# </scale_out_on_cpu_util>
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# Add rule to default profile: scale down if cpu util < 25 %
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# <scale_in_on_cpu_util>
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az monitor autoscale rule create \
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--autoscale-name $AUTOSCALE_SETTINGS_NAME \
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--condition "CpuUtilizationPercentage < 25 avg 5m" \
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--scale in 1
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# </scale_in_on_cpu_util>
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# add rule to default profile: scale up based on avg. request latency (endpoint metric)
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# <scale_up_on_request_latency>
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az monitor autoscale rule create \
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--autoscale-name $AUTOSCALE_SETTINGS_NAME \
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--condition "RequestLatency > 70 avg 5m" \
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--scale out 1 \
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--resource $ENDPOINT_RESOURCE_ID
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# </scale_up_on_request_latency>
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# create weekend profile: scale to 2 nodes in weekend
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# <weekend_profile>
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az monitor autoscale profile create \
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--name weekend-profile \
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--autoscale-name $AUTOSCALE_SETTINGS_NAME \
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--min-count 2 --count 2 --max-count 2 \
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--recurrence week sat sun --timezone "Pacific Standard Time"
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# </weekend_profile>
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# disable the autoscale profile
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# <disable_profile>
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az monitor autoscale update \
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--autoscale-name $AUTOSCALE_SETTINGS_NAME \
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--enabled false
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# </disable_profile>
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# <delete_endpoint>
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# delete the autoscaling profile
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az monitor autoscale delete -n "$AUTOSCALE_SETTINGS_NAME"
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# delete the endpoint
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az ml online-endpoint delete --name $ENDPOINT_NAME --yes --no-wait
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# </delete_endpoint>
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