1. Cost Analysis of Machine Learning Services with GCP Monitoring


    To analyze the cost of Machine Learning services on Google Cloud Platform (GCP), you'll need to implement monitoring and logging to keep track of the resources your ML services are consuming, such as compute instances, storage, and data processing services. Monitoring these resources will help you understand your usage patterns and potentially optimize costs.

    In this demonstration, we'll create a set of resources using Pulumi that will enable cost monitoring for GCP's Machine Learning services. This includes setting up a custom dashboard to visualize costs and setting up alert policies to notify you when costs exceed certain thresholds. We'll leverage GCP's Monitoring Dashboard and Alert Policy resources.

    1. GCP Monitoring Dashboard: This resource allows you to create a custom dashboard for monitoring your GCP resources. You would configure it to display the metrics relevant to the ML services' costs.
    2. GCP Monitoring Alert Policy: This resource allows you to create alerting policies based on metrics that can indicate when cost thresholds are crossed. You can configure it to notify you through various channels when alerts are triggered.

    The following program demonstrates how to set up these resources using Pulumi with Python:

    import pulumi import pulumi_gcp as gcp # Assuming you've already set up GCP provider and configured your project. # Create a custom Dashboard for monitoring ML services. ml_cost_dashboard = gcp.monitoring.Dashboard("mlCostDashboard", dashboard_json=pulumi.Output.all().apply(lambda args: json.dumps({ # Construct the dashboard JSON configuration here. # Include the necessary widgets and charts that display ML service cost metrics. # You would use metrics like `serviceruntime.googleapis.com/api/request_count` for usage patterns, # and to estimate costs, you could use `billing.googleapis.com/billing/export` data. })) ) # Create an alert policy that triggers when the estimated cost surpasses a specific threshold. cost_alert_policy = gcp.monitoring.AlertPolicy("costAlertPolicy", combiner="OR", # This could also be 'AND' depending on how you want to combine conditions. conditions=[{ "conditionThreshold": { "comparison": "GREATER_THAN", "duration": "3600s", # The duration over which the metric is aggregated. "filter": 'metric.type="costing_metric_here" resource.type="ml_service_type_here"', "thresholdValue": 100.00, # The threshold value to trigger the alert. "trigger": { "count": 1, # The number of times the threshold must be met. }, }, "displayName": "Cost exceeds $100", }], # Add configurations for notification channels (email, SMS,...). # You need to create these channels beforehand and provide their identifiers here. notification_channels=["your-notification-channel-id"] ) # Export the Dashboard and Alert Policy details to use or retrieve them later. pulumi.export("mlDashboardLink", ml_cost_dashboard.self_link) pulumi.export("costAlertPolicyName", cost_alert_policy.name)

    Here's what we're doing in the above program:

    • We're defining a Dashboard to visualize the Machine Learning services costs. The dashboard_json attribute is set to the configuration of the dashboard, which you would assemble by including metrics and other settings. This JSON structure would depend on the specific metrics you're tracking.
    • An AlertPolicy is also defined to create an alert when the costs exceed a certain threshold. We use the conditionThreshold to specify the conditions for triggering an alert. This includes the metric type, the resource type (which you would replace with your actual ML services metrics), the duration to consider, the comparison operator, and the threshold value—which, in this example, is set to $100.
    • Notification channels for alerts are mentioned as a placeholder, which would be a part of the setup outside of this code snippet.
    • We export the dashboard link and the alert policy name for easy access and reference, allowing you to navigate to these resources within the GCP console or use these details to reference the resources in the future.

    Lastly, please customize the dashboard_json and replace placeholders like "costing_metric_here", "ml_service_type_here", and "your-notification-channel-id" according to your actual setup and requirements.

    Remember to ensure that the appropriate permissions are in place for Pulumi to create and manage these resources in your GCP project.