1. Monitoring AI Model Serving Latencies with Jaeger on Kubernetes


    Monitoring AI model serving latencies is crucial for maintaining service quality and performance. Jaeger is a distributed tracing system that can be used to monitor the latencies in your services. To set up monitoring with Jaeger on Kubernetes, you need to deploy Jaeger components and configure your services to send traces to the Jaeger collector.

    Here's a guide to deploying a basic Jaeger setup on Kubernetes using Pulumi:

    1. Create a Kubernetes Namespace: You need a namespace where all the Jaeger components will reside. A namespace helps organize resources in a Kubernetes cluster.

    2. Deploy Jaeger Operator: Jaeger provides a Kubernetes Operator that simplifies the deployment of Jaeger components. The Operator manages the Jaeger instances.

    3. Create a Jaeger Instance: Once the Operator is running, you can create a Jaeger instance. This instance will include the necessary components such as the collector, agent, and UI.

    4. Configure your AI model serving application: Modify the configuration of your AI model serving application to send traces to the Jaeger collector. For gRPC-based services, you may use client interceptors that report tracing data.

    5. Install and configure the Jaeger agent: Depending on how your AI models are served, you might need to deploy the Jaeger agent as a sidecar container within your model serving pods to collect and forward traces to the collector.

    6. Use the Jaeger UI: Access the Jaeger UI through Kubernetes port-forwarding, ingress, or a load balancer to visualize the traces and latencies.

    Below is a Pulumi program written in Python that sets up the Jaeger Operator and a Jaeger instance in a Kubernetes cluster:

    import pulumi import pulumi_kubernetes as k8s # Create a new Kubernetes namespace for Jaeger components jaeger_namespace = k8s.core.v1.Namespace("jaeger-namespace", metadata={"name": "jaeger"}) # Deploy the Jaeger Operator using Helm jaeger_operator_chart = k8s.helm.v3.Chart( "jaeger-operator", k8s.helm.v3.ChartOpts( chart="jaeger-operator", version="2.19.0", fetch_opts=k8s.helm.v3.FetchOpts( repo="https://jaegertracing.github.io/helm-charts" ), namespace=jaeger_namespace.metadata["name"] ), opts=pulumi.ResourceOptions(depends_on=[jaeger_namespace]) ) # Define a Jaeger instance with an in-memory storage type. # This is suitable for testing or development purposes. jaeger_instance = k8s.apiextensions.CustomResource( "jaeger-instance", api_version="jaegertracing.io/v1", kind="Jaeger", metadata={"name": "my-jaeger", "namespace": jaeger_namespace.metadata["name"]}, spec={"strategy": "allInOne", "allInOne": {"options": {"memory": {"max-traces": 50000}}}, "storage": {"type": "memory"}}, opts=pulumi.ResourceOptions(depends_on=[jaeger_operator_chart]) ) # Export the Jaeger instance name pulumi.export("jaeger_instance_name", jaeger_instance.metadata["name"])

    This program will set up an all-in-one Jaeger deployment which is suitable for development. It includes the Jaeger UI that can be used to visualize traces and latencies. For production environments, you would need to consider a more robust storage backend and possibly a more complex setup like a production or streaming strategy for Jaeger deployment.

    Remember that to fully integrate Jaeger into your Kubernetes cluster, additional configuration and set up may be required depending on your specific use case and chosen technologies for your AI model serving.