1. Visualizing Model Performance Metrics with GCP Monitoring


    To visualize model performance metrics with Google Cloud Monitoring using Pulumi, you will need to create a dashboard that collects and displays the metrics of interest. Google Cloud Monitoring allows you to define custom metrics and set up dashboards for visualization.

    The following steps demonstrate how to create a custom metric and a dashboard using Pulumi:

    1. Define a custom metric: Create a MetricDescriptor that specifies the type of metric such as latency, error rate, etc. This includes defining labels for the metric if needed.

    2. Write and send metric data: Use the Google Cloud Monitoring API (or client libraries) to send data to your metric from your application or system.

    3. Create a monitoring dashboard: Use the Dashboard resource to define and configure a dashboard in Google Cloud Monitoring.

    4. Add charts to the dashboard: Define the layout and the data representation for the dashboard such as line charts, bar charts, etc.

    Let's create a simple Python program with Pulumi to demonstrate these steps:

    import pulumi import pulumi_gcp as gcp # Configure the project ID project_id = 'your-gcp-project-id' # Step 1: Define a custom metric # Replace 'custom_namespace', 'metric_name', 'metric description' with your metric's details custom_metric = gcp.monitoring.MetricDescriptor("custom-metric", type="custom.googleapis.com/custom_namespace/metric_name", metric_kind="GAUGE", value_type="DOUBLE", project=project_id, description="Metric descriptor for the custom metric", display_name="My Custom Metric", labels=[{ 'key': 'environment', 'value_type': 'STRING', 'description': 'The environment where the metric is collected', }] ) # Step 2: Write and send metric data is done through your model serving code using Google Cloud Monitoring API # Step 3: Create a monitoring dashboard # Define a simple dashboard with a single chart for the custom metric we created dashboard = gcp.monitoring.Dashboard("model-performance-dashboard", project=project_id, dashboard_json=pulumi.Output.all(custom_metric.type).apply(lambda metric_type: f""" {{ "displayName": "Model Performance Metrics", "widgets": [ {{ "title": "Custom Metric Over Time", "xyChart": {{ "dataSets": [ {{ "timeSeriesQuery": {{ "timeSeriesFilter": {{ "filter": "metric.type=\\"{metric_type}\\"", "aggregation": {{ "alignmentPeriod": "60s", "perSeriesAligner": "ALIGN_MEAN" }}, "secondaryAggregation": {{}} }}, "unitOverride": "1" }} }} ], "timeshiftDuration": "0s", "yAxis": {{ "label": "Value", "scale": "LINEAR" }}, "chartOptions": {{ "mode": "COLOR" }} }} }} ] }} """) ) # Export the URLs for the dashboard pulumi.export("dashboard_url", pulumi.Output.concat("https://console.cloud.google.com/monitoring/dashboards/custom/", dashboard.self_link.apply(lambda link: link.split("/")[-1])))

    This program defines a custom metric and a corresponding dashboard that contains a single chart to visualize the metric over time. Note the following about this program:

    • MetricDescriptor: Defines the custom metric with necessary details such as metric type, kind, value type, labels, and descriptions.
    • Dashboard: This JSON configuration constructs how your dashboard looks and what data it displays. The dashboard_json is a JSON string representing the dashboard structure, and it includes widgets to visualize your metric data through charts.
    • Export the URLs: At the end of the program, you will see the information exported as stack outputs. The dashboard_url will allow you to access the dashboard directly from the GCP console.

    Please replace 'your-gcp-project-id' with your actual Google Cloud project ID and modify the metric definition ('custom_namespace', 'metric_name', 'metric description') to match the specific details of your use-case.

    Running this program with Pulumi will create the custom metric and dashboard in your GCP project. You will need to have your GCP credentials and Pulumi configured to run the deployment.