Tracking Model Inference Volume Using GCP Monitoring

Question

Pulumi · Accepted Answer

To track the model inference volume using GCP Monitoring, you will create a set of resources that allow you to capture and visualize metrics related to your model's inference requests. This involves creating custom metrics to record inference events, setting up a dashboard for visualization, and potentially setting up alerting based on specific conditions. The primary GCP services involved are Cloud Monitoring and possibly Cloud Logging if you want to capture logs as well.

Here’s how you could go about it using Pulumi in Python:

1. **Define a Custom Metric**: Start by creating a custom metric to track inference events. You would use the `MetricDescriptor` resource for this, and the metric kind should likely be `CUMULATIVE` to indicate that the metric represents a value that continuously increases over time, such as the total number of inferences made.

2. **Create a Dashboard**: To visualize the data from the custom metric, you can create a Dashboard using the `Dashboard` resource. This includes defining panels and charts that show your data in an understandable format.

3. **Alert Policies**: Optionally, you can create an alert policy using the `AlertPolicy` resource. This will trigger notifications or automated actions if, for example, the volume of inferences crosses certain thresholds or exhibits unexpected patterns.

4. **Log-Based Metrics**: If you also want to derive metrics from logs (e.g., error rates), you might need to configure log-based metrics, which involves Cloud Logging.

Below is a Pulumi program that sets up a custom metric and a simple dashboard. Please note that this code does not include the actual model serving infrastructure or the logic to emit metrics from your model (which would likely involve instrumenting your code with the appropriate Cloud Monitoring client library).

```python
import pulumi
import pulumi_gcp as gcp

# Replace 'YOUR_PROJECT_ID' with your GCP Project ID.
project_id = 'YOUR_PROJECT_ID'

# Define a custom metric for tracking inference requests.
inference_requests_metric = gcp.monitoring.MetricDescriptor("inference-requests-metric",
    description="The total number of model inference requests",
    display_name="Inference Requests",
    metric_kind="CUMULATIVE",
    value_type="INT64",
    type=f"custom.googleapis.com/inference_requests",
    unit="1",
    project=project_id
)

# Create a Monitoring Dashboard for the metrics.
dashboard = gcp.monitoring.Dashboard("inference-monitoring-dashboard",
    dashboard_json=pulumi.Output.all(inference_requests_metric.type).apply(lambda args: json.dumps({
        "displayName": "Model Inference Volume",
        "gridLayout": {
            "columns": 2,
            "widgets": [
                {
                    "title": "Total Inference Requests",
                    "xyChart": {
                        "chartOptions": {
                            "mode": "COLOR",
                        },
                        "dataSets": [
                            {
                                "timeSeriesQuery": {
                                    "timeSeriesFilter": {
                                        "filter": f"metric.type=\"{args[0]}\"",
                                        "aggregation": {
                                            "perSeriesAligner": "ALIGN_SUM",
                                        }
                                    }
                                }
                            }
                        ]
                    }
                },
            ]
        }
    })),
    project=project_id
)

# Export the dashboard URL so you can easily access it.
pulumi.export('dashboard_url', pulumi.Output.concat(
    "https://console.cloud.google.com/monitoring/dashboards/custom/",
    dashboard.dashboard_id,
    "?project=",
    project_id
))
```

This code sets up the metric and dashboard within the context of your GCP project. It assumes you're already tracking inference requests and just want to monitor them. If you need to create resources to serve your model or emit the actual inference request metrics, those would be additional steps beyond the scope of this code.

💡 **Important**: Ensure that your application or service emitting the metrics uses the same metric type and reports the data in the expected format. The metrics should correspond to those defined in the `MetricDescriptor`.

You will also need to ensure the Pulumi program is applied to a GCP project by having the correct permissions and having configured Pulumi with GCP credentials.

Upon running this code with `pulumi up`, it will deploy the defined resources into your GCP project, allowing you to track and visualize model inference volume in the Google Cloud Console.