1. Auto-scaling ML Model Inference with Azure Runbooks


    Auto-scaling is a technique that allows you to dynamically adjust the number of compute resources allocated to your application based on its current load, ensuring that your application is responsive while minimizing costs. In an Azure context, you can leverage Azure Runbooks as part of Azure Automation to orchestrate scaling actions. I'll provide a program using Pulumi in Python that sets up auto-scaling for ML model inference using Azure Runbooks.

    First, let's understand the core resources we'll use in this Pulumi program:

    1. Azure Automation Account: This is a container for your Azure Automation resources. It holds the runbooks, configurations, and other assets used by Azure Automation.

    2. Runbook: A Runbook is a compilation of routine procedures and operations that the system administrator or an operator carries out. In Azure Automation, Runbooks can be scripts written in PowerShell, PowerShell Workflow, or Python that help in automating tasks.

    3. Azure Machine Learning: A service provided by Azure for building and deploying machine learning models. It typically includes a provisioning process for compute resources, which can be set up to auto-scale.

    4. Scale Actions: These are triggered by the Runbook when certain conditions are met to scale the compute resources up or down.

    Now, let's dive into creating the Pulumi program:

    import pulumi import pulumi_azure_native as azure_native # This pulumi program sets up auto-scaling for an ML model inference service using Azure Automation Runbooks. # Create an Azure resource group for organizing resources resource_group = azure_native.resources.ResourceGroup("resource-group") # Create an Azure Automation Account within the resource group automation_account = azure_native.automation.AutomationAccount("automation-account", resource_group_name=resource_group.name, sku=azure_native.automation.SkuArgs( name="Basic", ) ) # Create an AzureML Workspace within the resource group ml_workspace = azure_native.machinelearningservices.Workspace("ml-workspace", resource_group_name=resource_group.name, sku=azure_native.machinelearningservices.SkuArgs(name="Enterprise"), location=resource_group.location, ) # Create a Runbook that defines the auto-scaling logic runbook = azure_native.automation.Runbook("scaling-runbook", resource_group_name=resource_group.name, automation_account_name=automation_account.name, location=resource_group.location, runbook_type="Python", log_verbose=True, log_progress=True, description="Runbook to auto-scale Azure ML inference compute", # Here, you would insert your Runbook code, defining the logic for scaling. If you have the actual code, # it can be passed to the `runbook_content` variable below in a serialized fashion. For example: # draft_content_link=azure_native.automation.ContentLinkArgs( # uri="http://example.com/path/to/your/runbook/script.py", # version="" # ) ) # Publish the runbook. In a real-world scenario, you might want to use CI/CD integrations to handle runbook updates and publishing. runbook_publish_operation = azure_native.automation.RunbookDraftPublish("publish-runbook-operation", runbook_name=runbook.name, resource_group_name=resource_group.name, automation_account_name=automation_account.name, resource_name=runbook.name, opts=pulumi.ResourceOptions(depends_on=[runbook]) ) # Create an Azure ML compute instance (or, depending on your scenario, a compute cluster) that the Runbook will scale compute_instance = azure_native.machinelearningservices.Compute("inference-compute", resource_group_name=resource_group.name, location=resource_group.location, compute_name="InferenceCompute", properties=azure_native.machinelearningservices.ComputeInstancePropertiesArgs( compute_type="ComputeInstance", # Here, define the properties of your instance, such as VM size and auto-scaling settings ), workspace_name=ml_workspace.name, ) # We assume that an event or schedule will trigger the Runbook to perform scaling. You can use Azure Logic Apps or a similar service to handle scheduling. # Finally, export the important URLs and IDs that you might need to interact with pulumi.export("automation_account_id", automation_account.id) pulumi.export("runbook_id", runbook.id) pulumi.export("ml_workspace_url", ml_workspace.workspace_url) pulumi.export("inference_compute_id", compute_instance.id)

    In this program, we're setting up the following:

    • An Azure resource group that serves as a container for our resources.
    • An Azure Automation account where runbooks and other automation assets are stored.
    • A Python-type runbook that holds the auto-scaling logic. The actual logic would involve code that inspects the load on the current model inference compute resources and scales them up or down depending on certain metrics. This code is represented by a placeholder comment that would be replaced with the runbook's script URI and version.
    • A published version of the runbook such that it can be triggered.
    • A Compute instance to manage the ML tasks. This resource is where the ML model inference will run.
    • Export statements to yield the IDs and URLs of the created resources.

    Notice that this is a simplified setup and does not include the actual auto-scaling script or trigger mechanisms (like monitoring or event grid logic) which would involve more complex integration of other Azure services like Azure Monitor, Logic Apps, or Event Grid. This setup provides the initial infrastructure upon which the auto-scaling logic will operate.