1. Resource Utilization Alerts for Training AI on GCP


    Creating resource utilization alerts is important when you are training AI models on Google Cloud Platform (GCP). It helps to avoid overutilization or underutilization of resources which can lead to higher costs or suboptimal training times. In GCP, you can set up alert policies for various metrics like CPU usage, disk IO, memory usage, etc., for your AI training jobs.

    Pulumi allows you to define infrastructure in code, and that includes setting up alerts. To set up resource utilization alerts, we'll use the google-native.monitoring/v3.AlertPolicy resource. This will let us define policies that trigger when specified metrics hit certain thresholds.

    Here's an example of how to create a Pulumi program in Python that sets up an alert for high CPU utilization on a GCP AI training job:

    import pulumi import pulumi_google_native as google_native # Initialize a GCP project and monitoring policies using Pulumi project_id = 'your-gcp-project-id' # Replace with your GCP project ID alert_policy_name = 'high-cpu-usage-alert' # Name of the alert policy # Define the AlertPolicy resource for high CPU usage high_cpu_usage_alert_policy = google_native.monitoring.v3.AlertPolicy( 'highCpuUsageAlertPolicy', project=project_id, display_name=alert_policy_name, conditions=[{ 'displayName': 'High CPU utilization', 'conditionThreshold': { 'filter': 'metric.type="compute.googleapis.com/instance/cpu/utilization" AND resource.type="gce_instance"', 'comparison': 'COMPARISON_GT', # Greater than 'duration': '60s', # The amount of time that the condition must hold true 'thresholdValue': 0.8, # Threshold set at 80% CPU utilization 'aggregations': [{ 'alignmentPeriod': '60s', 'perSeriesAligner': 'ALIGN_RATE' }], 'trigger': { 'count': 1 # Number of instances the condition must be true for the threshold to trigger }, } }], combiner='AND', enabled=True ) # Export the alert policy's name pulumi.export('alert_policy_name', high_cpu_usage_alert_policy.display_name)

    In this example:

    • We've set up a single condition within the AlertPolicy to monitor the CPU utilization.
    • The filter targets the CPU utilization of compute instances used for the training job.
    • The policy triggers when the CPU utilization is greater than 80% for at least 60 seconds.
    • The alignmentPeriod and the perSeriesAligner are used to specify how the data points are aligned and what statistical function applies to them.

    Remember to replace 'your-gcp-project-id' with your actual GCP project ID. Also, ensure you have the appropriate permissions and API access enabled for creating alert policies in your GCP account.

    After creating your Pulumi program, simply run it using the Pulumi CLI. It will prompt you to save the file and then run pulumi up to deploy the changes to your cloud environment. You'll see an interactive console through which you can confirm the details and apply the deployment.

    When running this program, if an instance being monitored goes above the set threshold, it will trigger an alert. You can add more conditions or modify the metric to suit other resources like memory or disk usage as your requirements grow.