1. Tracking Infrastructure Health for AI Workloads with New Relic


    To track the health of infrastructure, especially for AI workloads, it's important to have a robust monitoring and observability platform. New Relic is a popular choice for this as it provides deep insights into your application's performance and the health of your infrastructure. With Pulumi, you can automate the provisioning of New Relic resources in your cloud environment.

    Below, I will show you how to use Pulumi to create New Relic Dashboard that can be used to track the health of your AI infrastructure. We will set up a simple dashboard that can display various health metrics. Consider this the starting point, and based on your specific requirements, you can customize it by adding more widgets and fine-tuning the NRQL queries.

    First, we'll need to set up the New Relic provider and then create a dashboard. Our dashboard will include a widget that tracks CPU usage, which is a common metric you might want to observe for an AI workload.

    Here’s the Pulumi program written in Python that accomplishes this:

    import pulumi import pulumi_newrelic as newrelic # Create a New Relic dashboard ai_workload_dashboard = newrelic.OneDashboard("aiWorkloadDashboard", name="AI Workload Health", # Specify the New Relic account ID where this dashboard will be created. account_id=newrelic_account_id, # Define the pages and widgets in the dashboard. pages=[ # You can create different pages for different aspects of your infrastructure health. newrelic.OneDashboardPageArgs( name="Overview", description="Overview of AI Infrastructure Health", # Add your widgets here. widget_areas=[ # Each widget area can hold multiple widgets. Add as necessary. newrelic.OneDashboardWidgetAreaArgs( title="CPU Usage", row=1, column=1, width=6, # This determines the width of the widget, max is 12 height=3, # This determines the height of the widget nrql_queries=[ # The NRQL query can be customized to target specific metrics. newrelic.OneDashboardWidgetAreaNrqlQueryArgs( query="SELECT average(cpuPercent) FROM SystemSample TIMESERIES", # Replace with your New Relic account ID which holds the data. account_id=newrelic_account_id, ) ] ), # Add more widgets as you see fit for other metrics. ] ), ], # You can add more configuration settings such as permissions and variables if needed. ) # Export the URL of the dashboard so you can easily access it from the Pulumi output. pulumi.export('dashboard_url', ai_workload_dashboard.url)

    Replace newrelic_account_id with your actual New Relic account ID. The query SELECT average(cpuPercent) FROM SystemSample TIMESERIES is a NRQL query which retrieves the average CPU usage over time. The TIMESERIES modifier allows this to be graphed as a time series chart, which is ideal for monitoring trends.

    This is a basic example that you can expand upon. You can add more widgets for different metrics like memory usage, network I/O, AI model performance metrics (if available in New Relic), etc.

    Each widget can be fine-tuned with different queries and display settings to suit your specific use case. It's also possible to create alerts based on these metrics to be notified in real-time if certain thresholds are crossed, ensuring prompt response to any potential issues with your infrastructure.

    Remember that to run this Pulumi program, you'll need to have the Pulumi CLI installed and configured with access to your cloud provider and New Relic account. For each resource class and function, you can find more information in the Pulumi New Relic provider documentation.