1. AI Workload Troubleshooting with Azure Monitor Logs


    To troubleshoot AI workloads in Azure, you can leverage Azure Monitor Logs, part of the Azure Monitor suite. Azure Monitor collects and analyzes performance and operational telemetry to provide visibility into the performance and health of Azure resources, on-premises environments, and hybrid clouds.

    In this context, you'd typically use an instance of Azure Log Analytics Workspace, a feature within Azure Monitor Logs that allows you to collect and store logs and metrics in a central location. You can analyze this data using queries to quickly retrieve, consolidate, and analyze collected data. You'd also create queries to analyze the logs and set up alerts based on the metrics extracted from the logs.

    Below is a Pulumi program that demonstrates how to create a Log Analytics Workspace using the azure-native provider's Workspace class. We will then define a simple query that can be used to analyze logs from AI workloads.

    import pulumi from pulumi_azure_native import operationalinsights as insights # Create a new Log Analytics Workspace log_analytics_workspace = insights.Workspace( "my-log-analytics-workspace", resource_group_name="my-resource-group", sku=insights.WorkspaceSkuArgs(name="PerGB2018"), # Additional properties can include specific configurations such as retention settings or workspace capping. ) # Normally you would use analytics queries to analyze the logs. # Below we're creating a simple analytics query example. # In a real-world scenario, you would include more complex queries # that help analyze the AI workload logs for troubleshooting. analytics_query = insights.Query( "sample-ai-workload-query", resource_group_name="my-resource-group", workspace_name=log_analytics_workspace.name, body="AIWorkspace | where TimeGenerated > ago(1h) | summarize count() by bin(TimeGenerated, 1m), SeverityLevel", # The query string should be updated based on the telemetry schema of the AI workload # and what specific metrics or logs you need to troubleshoot the AI application. # This example assumes logs contain a TimeGenerated and SeverityLevel field, # and it summarizes the count of logs each minute for the last hour, grouped by the severity level. ) # Output the Workspace ID and Query Results (as an example, you'd need a way to execute the query) pulumi.export("workspace_id", log_analytics_workspace.id) pulumi.export("query_example", analytics_query.body)

    In this program:

    1. We import the necessary modules from the pulumi and pulumi_azure_native packages.
    2. We create an instance of the Workspace class from the operationalinsights module, specifying the required parameters like resource_group_name and the sku for the workspace. The workspace is where all the logs and metrics are stored and analyzed.
    3. We define a Query as an example of how one could analyze logs for AI workloads. The Query resource represents an analytics query that one can use to analyze data within your Log Analytics Workspace.
    4. Finally, we export the ID of the Log Analytics Workspace and the example query using pulumi.export, which outputs the values after the Pulumi program is deployed.

    For more detailed information, you can check the Pulumi Azure Native documentation.

    Remember, this program only sets up the infrastructure required for logging and an example query. You will need additional setup to collect logs from your AI workloads to this workspace, and you would write specific queries based on your data and what troubleshooting or analysis you need to perform.

    To execute the query, you could either use Azure Portal, Azure CLI, or programmatically using Azure SDKs, which falls outside the scope of a Pulumi program. Pulumi is used here to provision and manage the infrastructure needed for log analytics.