1. Anomaly Detection Alerts for LLM Training on GCP

    Python

    When running large language model (LLM) training on Google Cloud Platform (GCP), you might want to set up anomaly detection alerts to monitor your training jobs and alert you if something goes wrong. You can use Google Cloud's Monitoring and Logging services to keep track of metrics and logs that may indicate anomalies. Pulumi allows you to codify the infrastructure as code to configure these services automatically.

    Here is how you could set up anomaly detection alerts with Pulumi in Python:

    1. Use Google Cloud Monitoring to create custom metrics and alert policies. You can define conditions to detect anomalies, such as sudden changes in resource usage or error rates.
    2. Set up notifications for these alerts with channels like email or PagerDuty, so your team gets notified when an anomaly is detected.

    Below, you'll find a Pulumi program written in Python that demonstrates how to set up an Alert Policy for anomaly detection in GCP, keeping an eye on the GPU utilization which is a commonly monitored metric for machine learning models training tasks:

    import pulumi import pulumi_gcp as gcp # Create a custom metric for monitoring GPU utilization. gpu_utilization_metric = gcp.monitoring.MetricDescriptor("gpu-utilization-metric", description="GPU utilization during LLM training", display_name="GPU Utilization", type="custom.googleapis.com/gpu/utilization", metric_kind="GAUGE", value_type="DOUBLE", unit="{GPU}" ) # Create an Alert Policy targeting the GPU utilization metric. anomaly_alert_policy = gcp.monitoring.AlertPolicy("anomaly-alert-policy", combiner="OR", conditions=[{ "displayName": "GPU Utilization High", "condition_threshold": { "filter": f'metric.type="custom.googleapis.com/gpu/utilization"', "comparison": "COMPARISON_GT", "thresholdValue": 80, # Trigger alert if GPU utilization is above 80% "duration": "300s", # Duration over which the condition must hold true "aggregations": [{ "alignmentPeriod": "60s", "perSeriesAligner": "ALIGN_MEAN" }] } }], notification_channels=[], # Specify the notification channel id(s) here # You would first need to create notification channels in Google Cloud and then refer them here. documentation={ "content": "Anomaly detected in GPU Utilization for LLM training job.", "mime_type": "text/markdown" } ) # Export the alert policy name for reference pulumi.export("alert_policy_name", anomaly_alert_policy.name)

    This Pulumi program declares two resources:

    1. gpu_utilization_metric: This is a custom Monitoring Metric Descriptor that defines the GPU utilization metric we're interested in. It specifies the type of data (double), that it's a gauge (a measurement at a point in time), and gives it a display name.

    2. anomaly_alert_policy: This is an Alert Policy that targets the GPU utilization metric. It defines conditions for when an alert should be triggered—in this case, if the GPU utilization is greater than 80% for a period of 5 minutes. Whenever the condition is met, any configured notification channels would receive an alert. The notification channels are identified by their IDs, which need to be created in GCP beforehand and then referenced in the program.

    Don't forget to create the necessary notification channels on Google Cloud (such as email, SMS, Slack, etc.), grab their IDs, and populate the notification_channels list with them.

    To run this program:

    1. Set up Pulumi with Google Cloud by installing Pulumi and configuring your GCP credentials.
    2. Write this code to a file __main__.py in a directory.
    3. Run pulumi up from the command line in that directory.

    This will start the deployment process, where Pulumi will create the defined resources in your GCP account. If any anomalies are detected according to your specified conditions, alerts will be sent to the configured notification channels.


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