1. Real-time Visualization of AI Workload Metrics on Datadog


    To create real-time visualization of AI workload metrics on Datadog using Pulumi, you'll need to work with the Datadog provider for Pulumi. You will be able to define and push custom metrics from your AI workloads and create Dashboard widgets on Datadog to visualize the metrics in real-time.

    This program will showcase how to define a custom metric and then set up a dashboard with a time series widget to visualize that metric. First, you need to install the Pulumi Datadog provider:

    pip install pulumi_datadog

    Ensure that you have the correct Datadog API and app keys set up in your Pulumi configuration or environment variables:

    pulumi config set datadog:apiKey <your-datadog-api-key> --secret pulumi config set datadog:appKey <your-datadog-app-key> --secret

    Now, here's the Pulumi program in Python that does the setup:

    import pulumi import pulumi_datadog as datadog # Define a custom metric that will be used to track an AI workload metric ai_workload_metric = datadog.MetricMetadata( "aiWorkloadMetricMetadata", metric="ai.workload.duration", type="gauge", description="Duration of AI workload processing", short_name="AI Workload Duration", unit="second" ) # Create a new Datadog Dashboard ai_workload_dashboard = datadog.Dashboard( "aiWorkloadDashboard", title="AI Workload Metrics", description="Dashboard for monitoring AI workloads", widgets=[ # Add a time series widget for the AI workload duration metric datadog.DashboardWidgetArgs( timeseries_definition=datadog.DashboardWidgetTimeseriesDefinitionArgs( requests=[ datadog.DashboardWidgetTimeseriesRequestArgs( q="avg:ai.workload.duration{*}", display_type="line" ) ] ) ) ] ) # Export the URL of the new Datadog Dashboard pulumi.export('dashboard_url', ai_workload_dashboard.url)

    In this program, we first define a custom metric named ai.workload.duration of type gauge using the datadog.MetricMetadata class, which holds the metadata for a Datadog metric (docs). The description and short_name properties are for readability and easier identification within Datadog.

    Next, we create a Datadog dashboard with the datadog.Dashboard resource, giving it a title and a description. Within this dashboard, we add a widget using the datadog.DashboardWidgetArgs class. This widget is a time series type, configured to display the average of our custom metric over all scopes ({*}).

    Finally, the URL of the newly created dashboard is exported using pulumi.export(), allowing you to easily access the dashboard once the Pulumi program is deployed.

    To visualize metrics in real time, your AI workloads would have to push the relevant metric data to Datadog. This program only sets up the visualization part; the metrics should be sent from your AI application using the Datadog API or Datadog's client libraries.

    Please note that the provided code snippet assumes that you have a Datadog account and have set up the Pulumi configuration with your Datadog API and App Keys. If your AI workloads are not already instrumented to send metrics to Datadog, you'll need to implement that instrumentation as well, either directly via API calls, through Datadog's client libraries, or by using Pulumi to create any needed integrations.