Monitoring AI Workload Latencies on GCP Monitoring Dashboard

Question

Pulumi · Accepted Answer

To monitor AI workload latencies on Google Cloud Platform (GCP), we can leverage the `google-native.monitoring/v1.Dashboard` resource from Pulumi's GCP provider. This resource allows us to create a custom monitoring dashboard that can include various widgets to visualize AI workload metrics, including latencies.

Here's how to create a GCP Monitoring Dashboard using Pulumi in Python:

1. **Set up Pulumi GCP Provider**: Ensure you have the Pulumi GCP provider installed and configured with appropriate credentials.

2. **Define the Dashboard Resource**: Use the `google_native.monitoring.v1.Dashboard` class to define your dashboard with the desired configuration.

3. **Add Widgets**: Include widgets that monitor latencies, such as `XYChart` or `TimeSeriesTable`, in the dashboard definition.

4. **Deploy**: Run `pulumi up` to deploy your monitoring dashboard to your GCP project.

Here's an example of how you might define such a dashboard:

```python
import pulumi
import pulumi_google_native as google_native

# Define your project configuration
project = 'my-gcp-project'  # Ensure this is set to your actual GCP project ID.

# Define the dashboard resource
monitoring_dashboard = google_native.monitoring.v1.Dashboard("ai-workload-dashboard",
    project=project,
    dashboard_json=pulumi.Output.all(project).apply(lambda args: f"""
        {{
            "displayName": "AI Workload Latencies",
            "gridLayout": {{
                "columns": "2",
                "widgets": [
                    {{
                        "title": "AI Workload Latency",
                        "xyChart": {{
                            "dataSets": [
                                {{
                                    "timeSeriesQuery": {{
                                        "timeSeriesFilter": {{
                                            "filter": "metric.type=\"ai-platform.googleapis.com/prediction/request_latencies\""
                                        }},
                                        "secondaryAggregation": {{
                                            "aligner": "ALIGN_MEAN"
                                        }}
                                    }},
                                    "plotType": "LINE",
                                    "legendTemplate": "${{dataset.labels.instance_id}} - mean latency"
                                }}
                            ],
                            "timeshiftDuration": "0s",
                            "yAxis": {{
                                "label": "Latency (ms)",
                                "scale": "LINEAR"
                            }}
                        }}
                    }}
                ]
            }}
        }}
    """)
)

# Export the dashboard's URL for easy access
pulumi.export("dashboard_url", f"https://console.cloud.google.com/monitoring/dashboards/custom/{monitoring_dashboard.id}?project={project}")
```

In this example:
- We specify a `project`, which should be replaced with your own GCP project ID.
- We create a `Dashboard` resource using the Pulumi Google Native provider.
- The `dashboard_json` attribute specifies the configuration for our dashboard in JSON format; you need to tailor this to suit your specific AI workload metrics.
- We use an `XYChart` widget with a time series query to visualize latency metrics.
- Finally, we export the URL of the dashboard for easy access.

To get this running in your environment, make sure you replace the `"my-gcp-project"` with your actual GCP project ID, and customize the `timeSeriesQuery` to match your specific AI workload metric filters and labeling. Once you're ready, run `pulumi up` in your terminal to create the dashboard on GCP.

For more information on using Pulumi with GCP, you can check out the Pulumi GCP documentation:

- [Pulumi's GCP provider overview](https://www.pulumi.com/docs/intro/cloud-providers/gcp/)
- [Google Native monitoring/v1.Dashboard documentation](https://www.pulumi.com/registry/packages/google-native/api-docs/monitoring/v1/dashboard/)

Remember to review the queries, labels, and other specific details relevant to your AI workloads and adjust the dashboard configuration appropriately.