Custom AI Resource Utilization Views with Azure Dashboards

Question

Pulumi · Accepted Answer

Creating a custom dashboard in Azure to view AI resource utilization involves creating an Azure Dashboard resource that pulls in the relevant data from your Azure AI and related services. A dashboard in Azure can display many different kinds of data and allows for a great deal of customization.

In Pulumi, we can define infrastructure as code using various resource definitions. To create a dashboard that shows AI resource utilization, we typically use the `Dashboard` resource from the `azure-native` provider because it offers the native Azure Resource Manager (ARM) API, which generally provides more functionality compared to the earlier `azure` provider.

Below is a Pulumi Python program that illustrates how you might define a basic Azure dashboard to monitor resource utilization. The dashboard includes a visual representation (like a chart or graph) of some data. Of course, the exact items you want to monitor and how you want them represented will depend on your specific needs.

For this example, we'll set up a simple dashboard with a text box as a placeholder – in a real-world scenario, you would replace this with queries and visualizations based on metrics relevant to your AI resources, such as CPU, memory usage, or prediction throughput.

Here is how you can do it:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure resource group if you don't already have one.
resource_group = azure_native.resources.ResourceGroup("my-resource-group")

# Define the dashboard.
dashboard = azure_native.portal.Dashboard("my-dashboard",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    properties={
        "lenses": [{
            "order": 1,
            "parts": [{
                "position": {
                    "x": 0,
                    "y": 0,
                    "rowSpan": 2,
                    "colSpan": 3
                },
                "metadata": {
                    "type": "Extension/HubsExtension/PartType/MarkdownPart",
                    "inputs": [{
                        "name": "Markdown",
                        "value": {
                            "settings": {
                                "content": "# Welcome to your AI Resource Utilization Dashboard
This is where you'll monitor your AI resources.",
                                "title": "Welcome",
                            }
                        }
                    }]
                }
            }]
        }],
        "metadata": {
            "model": {
                "timeRange": {
                    "value": {
                        "relative": {
                            "duration": 24,
                            "timeUnit": 1
                        }
                    },
                    "type": "MsPortalFx.Composition.Configuration.ValueTypes.TimeRange"
                },
            }
        },
    })

# Export the dashboard URL so you can easily access it.
dashboard_url = pulumi.Output.concat(
    "https://portal.azure.com/#asset/Microsoft_Azure_Monitoring/ComponentId/",
    "{'Name':'",
    dashboard.name,
    "','ResourceGroup':'",
    resource_group.name,
    "','SubscriptionId':'",
    pulumi.Config("azure").require("subscriptionId"),
    "','Type':'Extension/HubsExtension/Blade/PartType/DashboardPart'}"
)

pulumi.export('dashboard_url', dashboard_url)
```

This program does the following:

- Imports the required modules.
- Creates a new resource group (assuming you don't already have one to use).
- Defines the dashboard resource, including a basic layout with a text widget as a placeholder.
- Exports the URL to access the dashboard in the Azure Portal.

We've used the markdown part type here to add a simple welcome message, but you'd replace this with your actual visualization parts, which could include charts and graphs tied to your AI resource metrics. The `parts` array is where you can define different components of your dashboards, such as charts for CPU utilization or memory usage.

If you're collecting custom metrics from your AI resources, or if you want to combine utilization data with cost or other performance data, you might want to incorporate additional Azure services (like `Application Insights` or `Azure Monitor`) and tailor the dashboard parts to query and display that data.

Remember that this is just a starting point. You'll need to look into the specifics of your AI services and what metrics are available, then adjust the dashboard configuration to suit those specific requirements. Azure provides an array of widgets and visualization options which you can use to customize your dashboard to your heart's content.