Autoscale an online endpoint

APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current)

Autoscale automatically runs the right amount of resources to handle the load on your application. Online endpoints supports autoscaling through integration with the Azure Monitor autoscale feature.

Azure Monitor autoscaling supports a rich set of rules. You can configure metrics-based scaling (for instance, CPU utilization >70%), schedule-based scaling (for example, scaling rules for peak business hours), or a combination. For more information, see Overview of autoscale in Microsoft Azure.

Today, you can manage autoscaling using either the Azure CLI, REST, ARM, or the browser-based Azure portal. Other Azure Machine Learning SDKs, such as the Python SDK, will add support over time.

Prerequisites

• A deployed endpoint. Deploy and score a machine learning model by using an online endpoint.
• To use autoscale, the role microsoft.insights/autoscalesettings/write must be assigned to the identity that manages autoscale. You can use any built-in or custom roles that allow this action. For general guidance on managing roles for Azure Machine Learning, see Manage users and roles. For more on autoscale settings from Azure Monitor, see Microsoft.Insights autoscalesettings.

Define an autoscale profile

To enable autoscale for an endpoint, you first define an autoscale profile. This profile defines the default, minimum, and maximum scale set capacity. The following example sets the default and minimum capacity as two VM instances, and the maximum capacity as five:

Azure CLI

APPLIES TO: Azure CLI ml extension v2 (current)

The following snippet sets the endpoint and deployment names:

# set your existing endpoint name
ENDPOINT_NAME=your-endpoint-name
DEPLOYMENT_NAME=blue

Next, get the Azure Resource Manager ID of the deployment and endpoint:

# ARM id of the deployment
DEPLOYMENT_RESOURCE_ID=$(az ml online-deployment show -e $ENDPOINT_NAME -n $DEPLOYMENT_NAME -o tsv --query "id")
# ARM id of the deployment. todo: change to --query "id"
ENDPOINT_RESOURCE_ID=$(az ml online-endpoint show -n $ENDPOINT_NAME -o tsv --query "properties.\"azureml.onlineendpointid\"")
# set a unique name for autoscale settings for this deployment. The below will append a random number to make the name unique.
AUTOSCALE_SETTINGS_NAME=autoscale-$ENDPOINT_NAME-$DEPLOYMENT_NAME-`echo $RANDOM`

The following snippet creates the autoscale profile:

az monitor autoscale create \
  --name $AUTOSCALE_SETTINGS_NAME \
  --resource $DEPLOYMENT_RESOURCE_ID \
  --min-count 2 --max-count 5 --count 2

Note

For more, see the reference page for autoscale

Python

APPLIES TO: Python SDK azure-ai-ml v2 (current)

Import modules:

from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
from azure.mgmt.monitor import MonitorManagementClient
from azure.mgmt.monitor.models import AutoscaleProfile, ScaleRule, MetricTrigger, ScaleAction, Recurrence, RecurrentSchedule
import random 
import datetime 

Define variables for the workspace, endpoint, and deployment:

subscription_id = "<YOUR-SUBSCRIPTION-ID>"
resource_group = "<YOUR-RESOURCE-GROUP>"
workspace = "<YOUR-WORKSPACE>"

endpoint_name = "<YOUR-ENDPOINT-NAME>"
deployment_name = "blue"

Get Azure Machine Learning and Azure Monitor clients:

credential = DefaultAzureCredential()
ml_client = MLClient(
    credential, subscription_id, resource_group, workspace
)

mon_client = MonitorManagementClient(
    credential, subscription_id
)

Get the endpoint and deployment objects:

deployment = ml_client.online_deployments.get(
    deployment_name, endpoint_name
)

endpoint = ml_client.online_endpoints.get(
    endpoint_name
)

Create an autoscale profile:

# Set a unique name for autoscale settings for this deployment. The below will append a random number to make the name unique.
autoscale_settings_name = f"autoscale-{endpoint_name}-{deployment_name}-{random.randint(0,1000)}"

mon_client.autoscale_settings.create_or_update(
    resource_group, 
    autoscale_settings_name, 
    parameters = {
        "location" : endpoint.location,
        "target_resource_uri" : deployment.id,
        "profiles" : [
            AutoscaleProfile(
                name="my-scale-settings",
                capacity={
                    "minimum" : 2, 
                    "maximum" : 5,
                    "default" : 2
                },
                rules = []
            )
        ]
    }
)

Studio

In Azure Machine Learning studio, select your workspace and then select Endpoints from the left side of the page. Once the endpoints are listed, select the one you want to configure.

From the Details tab for the endpoint, select Configure auto scaling.

Under Choose how to scale your resources, select Custom autoscale to begin the configuration. For the default scale condition, use the following values:

• Set Scale mode to Scale based on a metric.
• Set Minimum to 2.
• Set Maximum to 5.
• Set Default to 2.

Create a rule to scale out using metrics

A common scaling out rule is one that increases the number of VM instances when the average CPU load is high. The following example will allocate two more nodes (up to the maximum) if the CPU average a load of greater than 70% for five minutes::

Azure CLI

APPLIES TO: Azure CLI ml extension v2 (current)

az monitor autoscale rule create \
  --autoscale-name $AUTOSCALE_SETTINGS_NAME \
  --condition "CpuUtilizationPercentage > 70 avg 5m" \
  --scale out 2

The rule is part of the my-scale-settings profile (autoscale-name matches the name of the profile). The value of its condition argument says the rule should trigger when "The average CPU consumption among the VM instances exceeds 70% for five minutes." When that condition is satisfied, two more VM instances are allocated.

Note

For more information on the CLI syntax, see az monitor autoscale.

Python

APPLIES TO: Python SDK azure-ai-ml v2 (current)

Create the rule definition:

rule_scale_out = ScaleRule(
    metric_trigger = MetricTrigger(
        metric_name="CpuUtilizationPercentage",
        metric_resource_uri = deployment.id, 
        time_grain = datetime.timedelta(minutes = 1),
        statistic = "Average",
        operator = "GreaterThan", 
        time_aggregation = "Last",
        time_window = datetime.timedelta(minutes = 5), 
        threshold = 70
    ), 
    scale_action = ScaleAction(
        direction = "Increase", 
        type = "ChangeCount", 
        value = 2, 
        cooldown = datetime.timedelta(hours = 1)
    )
)

This rule is refers to the last 5 minute average of CPUUtilizationpercentage from the arguments metric_name, time_window and time_aggregation. When value of the metric is greater than the threshold of 70, two more VM instances are allocated.

Update the my-scale-settings profile to include this rule:

mon_client.autoscale_settings.create_or_update(
    resource_group, 
    autoscale_settings_name, 
    parameters = {
        "location" : endpoint.location,
        "target_resource_uri" : deployment.id,
        "profiles" : [
            AutoscaleProfile(
                name="my-scale-settings",
                capacity={
                    "minimum" : 2, 
                    "maximum" : 5,
                    "default" : 2
                },
                rules = [
                    rule_scale_out
                ]
            )
        ]
    }
)

Studio

In the Rules section, select Add a rule. The Scale rule page is displayed. Use the following information to populate the fields on this page:

• Set Metric name to CPU Utilization Percentage.
• Set Operator to Greater than and set the Metric threshold to 70.
• Set Duration (minutes) to 5. Leave the Time grain statistic as Average.
• Set Operation to Increase count by and set Instance count to 2.

Finally, select the Add button to create the rule.

Create a rule to scale in using metrics

When load is light, a scaling in rule can reduce the number of VM instances. The following example will release a single node, down to a minimum of 2, if the CPU load is less than 30% for 5 minutes:

Azure CLI

APPLIES TO: Azure CLI ml extension v2 (current)

az monitor autoscale rule create \
  --autoscale-name $AUTOSCALE_SETTINGS_NAME \
  --condition "CpuUtilizationPercentage < 25 avg 5m" \
  --scale in 1

Python

APPLIES TO: Python SDK azure-ai-ml v2 (current)

Create the rule definition:

rule_scale_in = ScaleRule(
    metric_trigger = MetricTrigger(
        metric_name="CpuUtilizationPercentage",
        metric_resource_uri = deployment.id, 
        time_grain = datetime.timedelta(minutes = 1),
        statistic = "Average",
        operator = "LessThan", 
        time_aggregation = "Last",
        time_window = datetime.timedelta(minutes = 5), 
        threshold = 30
    ), 
    scale_action = ScaleAction(
        direction = "Increase", 
        type = "ChangeCount", 
        value = 1, 
        cooldown = datetime.timedelta(hours = 1)
    )
)

Update the my-scale-settings profile to include this rule:

mon_client.autoscale_settings.create_or_update(
    resource_group, 
    autoscale_settings_name, 
    parameters = {
        "location" : endpoint.location,
        "target_resource_uri" : deployment.id,
        "profiles" : [
            AutoscaleProfile(
                name="my-scale-settings",
                capacity={
                    "minimum" : 2, 
                    "maximum" : 5,
                    "default" : 2
                },
                rules = [
                    rule_scale_out, 
                    rule_scale_in
                ]
            )
        ]
    }
)

Studio

In the Rules section, select Add a rule. The Scale rule page is displayed. Use the following information to populate the fields on this page:

• Set Metric name to CPU Utilization Percentage.
• Set Operator to Less than and the Metric threshold to 30.
• Set Duration (minutes) to 5.
• Set Operation to Decrease count by and set Instance count to 1.

Finally, select the Add button to create the rule.

If you have both scale out and scale in rules, your rules will look similar to the following screenshot. You've specified that if average CPU load exceeds 70% for 5 minutes, 2 more nodes should be allocated, up to the limit of 5. If CPU load is less than 30% for 5 minutes, a single node should be released, down to the minimum of 2.

Create a scaling rule based on endpoint metrics

The previous rules applied to the deployment. Now, add a rule that applies to the endpoint. In this example, if the request latency is greater than an average of 70 milliseconds for 5 minutes, allocate another node.

Azure CLI

APPLIES TO: Azure CLI ml extension v2 (current)

az monitor autoscale rule create \
 --autoscale-name $AUTOSCALE_SETTINGS_NAME \
 --condition "RequestLatency > 70 avg 5m" \
 --scale out 1 \
 --resource $ENDPOINT_RESOURCE_ID

Python

APPLIES TO: Python SDK azure-ai-ml v2 (current)

Create the rule definition:

rule_scale_out_endpoint = ScaleRule(
    metric_trigger = MetricTrigger(
        metric_name="RequestLatency",
        metric_resource_uri = endpoint.id, 
        time_grain = datetime.timedelta(minutes = 1),
        statistic = "Average",
        operator = "GreaterThan", 
        time_aggregation = "Last",
        time_window = datetime.timedelta(minutes = 5), 
        threshold = 70
    ), 
    scale_action = ScaleAction(
        direction = "Increase", 
        type = "ChangeCount", 
        value = 1, 
        cooldown = datetime.timedelta(hours = 1)
    )
)

This rule's metric_resource_uri field now refers to the endpoint rather than the deployment.

Update the my-scale-settings profile to include this rule:

mon_client.autoscale_settings.create_or_update(
    resource_group, 
    autoscale_settings_name, 
    parameters = {
        "location" : endpoint.location,
        "target_resource_uri" : deployment.id,
        "profiles" : [
            AutoscaleProfile(
                name="my-scale-settings",
                capacity={
                    "minimum" : 2, 
                    "maximum" : 5,
                    "default" : 2
                },
                rules = [
                    rule_scale_out, 
                    rule_scale_in,
                    rule_scale_out_endpoint
                ]
            )
        ]
    }
)

Studio

From the bottom of the page, select + Add a scale condition.

Select Scale based on metric, and then select Add a rule. The Scale rule page is displayed. Use the following information to populate the fields on this page:

• Set Metric source to Other resource.
• Set Resource type to Machine Learning online endpoints.
• Set Resource to your endpoint.
• Set Metric name to Request latency.
• Set Operator to Greater than and set Metric threshold to 70.
• Set Duration (minutes) to 5.
• Set Operation to Increase count by and set Instance count to 1

Create scaling rules based on a schedule

You can also create rules that apply only on certain days or at certain times. In this example, the node count is set to 2 on the weekend.

Azure CLI

APPLIES TO: Azure CLI ml extension v2 (current)

az monitor autoscale profile create \
  --name weekend-profile \
  --autoscale-name $AUTOSCALE_SETTINGS_NAME \
  --min-count 2 --count 2 --max-count 2 \
  --recurrence week sat sun --timezone "Pacific Standard Time"

Python

APPLIES TO: Python SDK azure-ai-ml v2 (current)

mon_client.autoscale_settings.create_or_update(
    resource_group, 
    autoscale_settings_name, 
    parameters = {
        "location" : endpoint.location,
        "target_resource_uri" : deployment.id,
        "profiles" : [
            AutoscaleProfile(
                name="Default",
                capacity={
                    "minimum" : 2, 
                    "maximum" : 2,
                    "default" : 2
                },
                recurrence = Recurrence(
                    frequency = "Week", 
                    schedule = RecurrentSchedule(
                        time_zone = "Pacific Standard Time", 
                        days = ["Saturday", "Sunday"], 
                        hours = [], 
                        minutes = []
                    )
                )
            )
        ]
    }
)

Studio

From the bottom of the page, select + Add a scale condition. On the new scale condition, use the following information to populate the fields:

• Select Scale to a specific instance count.
• Set the Instance count to 2.
• Set the Schedule to Repeat specific days.
• Set the schedule to Repeat every Saturday and Sunday.

Delete resources

If you are not going to use your deployments, delete them:

Azure CLI

APPLIES TO: Azure CLI ml extension v2 (current)

# delete the autoscaling profile
az monitor autoscale delete -n "$AUTOSCALE_SETTINGS_NAME"

# delete the endpoint
az ml online-endpoint delete --name $ENDPOINT_NAME --yes --no-wait

Python

APPLIES TO: Python SDK azure-ai-ml v2 (current)

mon_client.autoscale_settings.delete(
    resource_group, 
    autoscale_settings_name
)

ml_client.online_endpoints.begin_delete(endpoint_name)

Studio

1. Go to the Azure Machine Learning studio.
2. In the left navigation bar, select the Endpoints page.
3. Select an endpoint by checking the circle next to the model name.
4. Select Delete.

Alternatively, you can delete a managed online endpoint directly in the endpoint details page.

Next steps

To learn more about autoscale with Azure Monitor, see the following articles:

• Understand autoscale settings
• Overview of common autoscale patterns
• Best practices for autoscale
• Troubleshooting Azure autoscale