1. Distributed Tracing of AI Pipelines using Humio on Kubernetes


    Distributed tracing is the practice of monitoring and visualizing the life cycle of requests as they flow through a system of microservices. When working with AI pipelines, especially in a Kubernetes environment, it is crucial to have a good tracing mechanism to understand the system performance, identify bottlenecks, and troubleshoot issues.

    While there isn't a specific resource in the provided registry search results for Humio—the log management platform you’re looking to use for tracing—there is an approach we can take with Pulumi to set up a Kubernetes cluster and integrate it with a monitoring solution such as Dynatrace, which also provides log management and analytics capabilities similar to Humio.

    We can begin by setting up our Kubernetes cluster using Pulumi, then deploy Dynatrace to our cluster using its Kubernetes Operator which will give us the observability features—including distributed tracing. This setup will enable AI pipeline monitoring, which can be considered similar to setting up Humio for monitoring purposes, although the specifics of the monitoring tool and the interfaces would differ.

    Below is a Pulumi Python program that demonstrates how to create a Kubernetes cluster and then deploy the Dynatrace Operator to that cluster. You would need to have the pulumi_kubernetes package installed and your cloud provider CLI configured with the appropriate access credentials. Although we are not using Humio here, you can consider this as a starting point and adapt the concepts to integrate with Humio if needed.

    import pulumi import pulumi_kubernetes as kubernetes from pulumi_kubernetes.helm.v3 import Chart, ChartOpts # Create a Kubernetes cluster using one of the cloud providers, e.g., AWS, GCP, Azure, etc. # Here is an example of creating an EKS cluster using the `pulumi_eks` module. This will require # the `pulumi_eks` package to be installed. import pulumi_eks as eks # Create an EKS cluster with the default configuration. cluster = eks.Cluster("my-cluster") # Now install the Dynatrace Operator using a Helm chart. # Please adjust the settings according to your Dynatrace environment. dynatrace_chart = Chart( "dynatrace-operator", ChartOpts( chart="dynatrace-operator", version="0.1.0", # Specify the version of the chart to use. fetch_opts=kubernetes.helm.v3.FetchOpts( # Setting up the Helm repository that contains the Dynatrace Operator chart. repo="https://raw.githubusercontent.com/Dynatrace/helm-charts/master/repos/stable" ), ), opts=pulumi.ResourceOptions(provider=cluster.provider), ) # Export the cluster's kubeconfig. pulumi.export("kubeconfig", cluster.kubeconfig)

    We start by creating a Kubernetes cluster using the pulumi_eks package (which must be installed). Then, we install the Dynatrace Operator using a Helm chart from the Dynatrace Helm chart repository.

    Once you have the cluster and the monitoring operator installed, you could deploy your AI pipelines into the Kubernetes cluster. Kubernetes resources, such as deployments and services, can be defined in Pulumi using the pulumi_kubernetes package. Make sure the AI pipeline services are appropriately instrumented for distributed tracing using the SDK or instruments compatible with Dynatrace.

    Finally, we export the kubeconfig from the cluster, which is necessary for you to interact with your cluster using kubectl and to set up other integrations or deployments.

    Keep in mind, specific configurations for the AI pipelines and the exact way you set up distributed tracing will depend on the tools and languages your AI pipelines use. This Pulumi program provides you with the foundational infrastructure to begin that journey.

    Remember to replace "https://raw.githubusercontent.com/Dynatrace/helm-charts/master/repos/stable" with the URL of your Helm chart repository if it's different and to provide the specific version of the chart you want to deploy. If you decide to integrate Humio at a later stage, you would need to locate the appropriate Helm chart for Humio and follow similar steps to install and configure it within your Kubernetes cluster.