1. Resource Utilization Analysis for AI Services


    To perform a resource utilization analysis for AI services, you typically need to collect and analyze metrics such as CPU, memory, and storage usage. This can help you optimize costs and improve performance by identifying bottlenecks or underutilized resources. Pulumi does not directly provide a way to perform such analyses, as it is an infrastructure as code tool used for provisioning and managing cloud resources.

    However, you can use Pulumi to provision monitoring services that track resource utilization for AI services. For example, you can create a dashboard with Amazon CloudWatch or Google Stackdriver to visualize utilization metrics of cloud AI services.

    Let's create a simple setup with Pulumi using Google Cloud's AI and monitoring services as an example:

    1. We will provision a Google Compute Engine instance that might host an AI model serving API.
    2. Set up Google Stackdriver monitoring to keep track of the CPU and memory usage of the instance.
    3. Export the URL for the monitoring dashboard.

    Below is a Python Pulumi program that demonstrates the setup:

    import pulumi import pulumi_gcp as gcp # Create a new Google Compute instance to host our AI services ai_instance = gcp.compute.Instance("ai-instance", machine_type="n1-standard-1", zone="us-central1-a", boot_disk=gcp.compute.InstanceBootDiskArgs( initialize_params=gcp.compute.InstanceBootDiskInitializeParamsArgs( image="debian-cloud/debian-9", ), ), network_interfaces=[gcp.compute.InstanceNetworkInterfaceArgs( network="default", access_configs=[gcp.compute.InstanceNetworkInterfaceAccessConfigArgs()], )], ) # Setting up Stackdriver Monitoring # NOTE: Stackdriver Workspaces and monitoring must be set up manually in the Google Cloud console first. # After workspace setup, your monitoring project identifier will be known and replace `YOUR_MONITORING_PROJECT_ID`. stackdriver_dashboard = gcp.monitoring.Dashboard("stackdriver-dashboard", dashboard_json=pulumi.Output.all(ai_instance.zone, ai_instance.id).apply( lambda args: f''' {{ "displayName": "AI Instance Utilization", "gridLayout": {{ "columns": 2, "widgets": [ {{ "title": "CPU Usage", "xyChart": {{ "dataSets": [{{ "timeSeriesQuery": {{ "timeSeriesFilter": {{ "filter": "metric.type=\\"compute.googleapis.com/instance/cpu/utilization\\" resource.type=\\"gce_instance\\" resource.label.\\"instance_id\\"=\\"{args[1]}\\"", "aggregation": {{ "alignmentPeriod": "60s", "perSeriesAligner": "ALIGN_MEAN" }} }}, "unitOverride": "Percent" }} ]}, "chartOptions": {{ "mode": "COLOR" }} }} }}, {{ "title": "Memory Usage", "xyChart": {{ "dataSets": [{{ "timeSeriesQuery": {{ "timeSeriesFilter": {{ "filter": "metric.type=\\"agent.googleapis.com/memory/percent_used\\" resource.type=\\"gce_instance\\" resource.label.\\"instance_id\\"=\\"{args[1]}\\"", "aggregation": {{ "alignmentPeriod": "60s", "perSeriesAligner": "ALIGN_MEAN" }} }}, "unitOverride": "Percent" }} ]}, "chartOptions": {{ "mode": "COLOR" }} }} }} ] }} }} ''' ), project="YOUR_MONITORING_PROJECT_ID", ) # Exporting the dashboard selfLink, which is the URL to access this specific dashboard in Google Cloud pulumi.export('stackdriver_dashboard_url', stackdriver_dashboard.self_link)

    In this script:

    • We create a gcp.compute.Instance which could represent an instance hosting an AI service.
    • We set up a gcp.monitoring.Dashboard that contains a JSON payload defining the layout and widgets of a monitoring dashboard. In this case, we are adding two charts: one for CPU Utilization and another for Memory Usage of the AI instance.
    • Finally, we export the URL for the monitoring dashboard so it can be accessed directly from the Pulumi outputs.

    This setup assumes you have a Google Cloud project and you've set up Stackdriver monitoring in the Google Cloud console before running the script. Please note you'll have to replace YOUR_MONITORING_PROJECT_ID with the actual project ID where the Stackdriver monitoring is set up.