1. Monitoring AI Workload Latencies on GCP Monitoring Dashboard


    To monitor AI workload latencies on Google Cloud Platform (GCP), we can leverage the google-native.monitoring/v1.Dashboard resource from Pulumi's GCP provider. This resource allows us to create a custom monitoring dashboard that can include various widgets to visualize AI workload metrics, including latencies.

    Here's how to create a GCP Monitoring Dashboard using Pulumi in Python:

    1. Set up Pulumi GCP Provider: Ensure you have the Pulumi GCP provider installed and configured with appropriate credentials.

    2. Define the Dashboard Resource: Use the google_native.monitoring.v1.Dashboard class to define your dashboard with the desired configuration.

    3. Add Widgets: Include widgets that monitor latencies, such as XYChart or TimeSeriesTable, in the dashboard definition.

    4. Deploy: Run pulumi up to deploy your monitoring dashboard to your GCP project.

    Here's an example of how you might define such a dashboard:

    import pulumi import pulumi_google_native as google_native # Define your project configuration project = 'my-gcp-project' # Ensure this is set to your actual GCP project ID. # Define the dashboard resource monitoring_dashboard = google_native.monitoring.v1.Dashboard("ai-workload-dashboard", project=project, dashboard_json=pulumi.Output.all(project).apply(lambda args: f""" {{ "displayName": "AI Workload Latencies", "gridLayout": {{ "columns": "2", "widgets": [ {{ "title": "AI Workload Latency", "xyChart": {{ "dataSets": [ {{ "timeSeriesQuery": {{ "timeSeriesFilter": {{ "filter": "metric.type=\\"ai-platform.googleapis.com/prediction/request_latencies\\"" }}, "secondaryAggregation": {{ "aligner": "ALIGN_MEAN" }} }}, "plotType": "LINE", "legendTemplate": "${{dataset.labels.instance_id}} - mean latency" }} ], "timeshiftDuration": "0s", "yAxis": {{ "label": "Latency (ms)", "scale": "LINEAR" }} }} }} ] }} }} """) ) # Export the dashboard's URL for easy access pulumi.export("dashboard_url", f"https://console.cloud.google.com/monitoring/dashboards/custom/{monitoring_dashboard.id}?project={project}")

    In this example:

    • We specify a project, which should be replaced with your own GCP project ID.
    • We create a Dashboard resource using the Pulumi Google Native provider.
    • The dashboard_json attribute specifies the configuration for our dashboard in JSON format; you need to tailor this to suit your specific AI workload metrics.
    • We use an XYChart widget with a time series query to visualize latency metrics.
    • Finally, we export the URL of the dashboard for easy access.

    To get this running in your environment, make sure you replace the "my-gcp-project" with your actual GCP project ID, and customize the timeSeriesQuery to match your specific AI workload metric filters and labeling. Once you're ready, run pulumi up in your terminal to create the dashboard on GCP.

    For more information on using Pulumi with GCP, you can check out the Pulumi GCP documentation:

    Remember to review the queries, labels, and other specific details relevant to your AI workloads and adjust the dashboard configuration appropriately.