AI Inference Service Latency Measurement with VMServiceScrape

Question

Pulumi · Accepted Answer

To measure the latency of an AI Inference Service, you would typically deploy a monitoring solution that can scrape metrics from your service and provide a latency measurement. In the case of Kubernetes, this is often done using tools like Prometheus together with service monitors or pod annotations that allow Prometheus to discover and scrape your service's metrics endpoint.

`VMServiceScrape` is not a standard Kubernetes or cloud provider resource and there doesn't appear to be a direct Pulumi resource for it. It seems to be related to the monitoring systems that scrape metrics from services, potentially as part of the Prometheus operator in a Kubernetes cluster.

Instead, what we can do is set up a monitoring stack on Kubernetes that makes use of the Prometheus operator. This stack can include a `ServiceMonitor` or `PodMonitor`, which are custom resources made available by the Prometheus operator. They are designed to specify how Prometheus should discover and scrape targets.

Below, I will provide you with a Pulumi program that sets up a Kubernetes cluster using `pulumi_azure_native` resources, installs the Prometheus operator, and adds a `ServiceMonitor` to monitor an AI Inference service.

1. Create a Kubernetes cluster using Azure Kubernetes Service.
2. Use the Helm package manager to deploy the Prometheus operator onto the cluster.
3. Define a `ServiceMonitor` to scrape metrics from your AI Inference Service.

Before running the following program, make sure you have Pulumi and the required cloud provider CLI tools installed and configured appropriately.

```python
import pulumi
import pulumi_azure_native.containerservice as containerservice
import pulumi_azure_native.resources as resources
import pulumi_kubernetes as k8s
from pulumi_kubernetes.helm.v3 import Chart, ChartOpts

# Create an Azure Resource Group
resource_group = resources.ResourceGroup('rg')

# Create an Azure AKS cluster
managed_cluster_name = 'aks-cluster'
aks_cluster = containerservice.ManagedCluster(
    managed_cluster_name,
    resource_group_name=resource_group.name,
    agent_pool_profiles=[{
        'count': 2,
        'max_pods': 110,
        'mode': 'System',
        'name': 'agentpool',
        'os_type': 'Linux',
        'vm_size': 'Standard_DS2_v2',
    }],
    dns_prefix=resource_group.name,
)

# Export the Kubeconfig
kubeconfig = pulumi.Output.all(resource_group.name, aks_cluster.name).apply(
    lambda args: containerservice.list_managed_cluster_user_credentials(
        resource_group_name=args[0], resource_name=args[1]
    ).kubeconfigs[0].value.apply(
        lambda enc: enc.decode('utf-8')
    )
)

# Create a Kubernetes Provider instance using the kubeconfig.
k8s_provider = k8s.Provider('k8s-provider', kubeconfig=kubeconfig)

# Deploy the Prometheus operator to the cluster using the Helm chart.
prometheus_chart = Chart(
    'prometheus-operator',
    ChartOpts(
        chart='kube-prometheus-stack',
        version='13.13.1',
        fetch_opts=k8s.helm.v3.FetchOpts(
            repo='https://prometheus-community.github.io/helm-charts',
        ),
        namespace='monitoring',
        values={'prometheus': {'serviceMonitorSelectorNilUsesHelmValues': False}},
    ),
    opts=pulumi.ResourceOptions(provider=k8s_provider),
)

# Define the ServiceMonitor to scrape metrics from AI Inference Service.
# Replace 'your-service-name' with the actual service name of your AI Inference Service.
# And ensure it has labels matching 'app: your-ai-service-label'
service_monitor = k8s.apiextensions.CustomResource(
    'ai-service-monitor',
    api_version='monitoring.coreos.com/v1',
    kind='ServiceMonitor',
    metadata={'name': 'ai-service-monitor', 'namespace': 'monitoring'},
    spec={
        'selector': {
            'matchLabels': {'app': 'your-ai-service-label'}
        },
        'endpoints': [{
            'port': 'http-metrics', # Replace this with the actual port name your AI service uses for exposing metrics.
            'interval': '15s',
            'path': '/metrics' # Replace this with the actual path your AI service exposes metrics on, if different.
        }],
        'namespaceSelector': {
            'matchNames': ['default']
        }
    },
    opts=pulumi.ResourceOptions(provider=k8s_provider, depends_on=[prometheus_chart]),
)

# Export the kubeconfig to make use of it outside of Pulumi
pulumi.export('kubeconfig', kubeconfig)
```

In this program:
- We're setting up a resource group and an AKS cluster in Azure.
- The kubeconfig for accessing the AKS cluster is being retrieved and used to set up a Kubernetes provider for Pulumi.
- We're deploying the Prometheus operator using a Helm chart (`kube-prometheus-stack` from the `prometheus-community` Helm repository). This Prometheus setup includes Prometheus itself, Grafana for visualization, and Alertmanager for alerting.
- A `ServiceMonitor` is being created that is set up to monitor an AI Inference Service.

Remember to update the placeholders like `'your-service-name'` and `'your-ai-service-label'` with the actual names/labels of your AI Inference Service. This example assumes that your AI Inference Service is running in the `default` namespace and exposes metrics at the `/metrics` path on a port named `http-metrics`.

With this program, Prometheus would automatically begin scraping metrics as per the intervals specified, and you would be able to access them in Prometheus and create dashboards in Grafana to view the latency information.