1. Real-time Analysis of Large Language Models on Kubernetes using Elastic APM

    Python

    To run real-time analysis of large language models on Kubernetes and use Elastic APM for monitoring, we'll need to set up a few things:

    1. A Kubernetes cluster where our application and Elastic APM Server will run.
    2. An Elastic APM Server deployment within our Kubernetes cluster to collect performance metrics.
    3. Application pods that are instrumented with Elastic APM agents to send data to the Elastic APM Server.

    In this Pulumi program, you will see the following resources being created:

    • A Kubernetes cluster: This is the environment where your application and the Elastic APM Server will reside.
    • An Elastic APM Server on Kubernetes: This server will receive data from your application's APM agents.
    • A deployment for your application: A sample deployment, indicating where you would include the APM agent in a real scenario.

    This Pulumi program uses pulumi_kubernetes, which is a Pulumi package that allows you to manage Kubernetes resources with Pulumi. And while Pulumi does not have a native Elastic APM Kubernetes resource, we can use Kubernetes resource definitions within Pulumi to deploy the server as well as the application.

    First, ensure you have the following prerequisites:

    • Pulumi CLI installed and configured for your environment.
    • Access to a Kubernetes cluster (could be Minikube, EKS, GKE, AKS, etc.).
    • kubectl configured to connect to your Kubernetes cluster.
    • Docker installed, if you're going to build a container for your application.

    Here is the Pulumi Python program that accomplishes this setup:

    import pulumi from pulumi_kubernetes.apps.v1 import Deployment from pulumi_kubernetes.core.v1 import Service, Namespace, ConfigMap from pulumi_kubernetes.helm.v3 import Chart, ChartOpts # Create a namespace for the Elastic APM deployment apm_namespace = Namespace("apm-namespace", metadata={"name": "elastic-apm"}) # Deploy Elastic APM Server using Helm Chart apm_chart = Chart( "apm-server", config=ChartOpts( chart="apm-server", version="7.14.0", # Use the version of Elastic APM Server you need fetch_opts={"repo": "https://helm.elastic.co"}, namespace=apm_namespace.metadata["name"], values={"apmConfig": {"apm-server.yml": "apm-server:\n host: \"0.0.0.0:8200\"\n"}} ), opts=pulumi.ResourceOptions(namespace=apm_namespace.metadata["name"]) ) # Create a ConfigMap with configuration for your application's Elastic APM agent apm_agent_config = ConfigMap( "apm-agent-config", metadata={"namespace": apm_namespace.metadata["name"]}, data={ "elastic-apm.properties": """ service_name=my-application server_urls=http://apm-server.elastic-apm:8200 secret_token= """ } ) # Create a deployment for your application app_labels = {"app": "my-application"} app_deployment = Deployment( "app-deployment", metadata={"namespace": apm_namespace.metadata["name"]}, spec={ "selector": {"matchLabels": app_labels}, "replicas": 1, "template": { "metadata": {"labels": app_labels}, "spec": { "containers": [{ "name": "my-application-container", "image": "your-application-image", # Replace with the image of your application # Include APM agent in your application container here }], # Reference to the ConfigMap containing APM agent configuration "volumes": [{"name": "apm-agent-config", "configMap": {"name": apm_agent_config.metadata["name"]}}], "volumeMounts": [{"name": "apm-agent-config", "mountPath": "/etc/elastic-apm.properties"}] } } } ) # Expose your application via a Kubernetes service app_service = Service( "app-service", metadata={"namespace": apm_namespace.metadata["name"], "labels": app_labels}, spec={ "type": "LoadBalancer", "ports": [{"port": 80, "targetPort": 8080}], "selector": app_labels } ) # Export the URL to access the APM Server (this may vary based on your cloud provider's LoadBalancer configuration) apm_server_url = pulumi.Output.all(apm_namespace.metadata["name"], apm_chart.resources.apply(lambda resources: resources[0]["status"]["loadBalancer"]["ingress"][0]["ip"])).apply(lambda args: f"http://{args[1]}:8200") pulumi.export("apm_server_url", apm_server_url) # Export the URL to access your application app_service_url = pulumi.Output.all(app_service.status.apply(lambda status: status["loadBalancer"]["ingress"][0])).apply(lambda ingress: f"http://{ingress['ip']}" if ingress.get("ip") else f"http://{ingress['hostname']}") pulumi.export("app_service_url", app_service_url)

    Explanation of the components:

    • Namespace: A Kubernetes namespace to isolate our Elastic APM resources.
    • ChartOpts: The Helm chart options for deploying the Elastic APM Server.
    • Chart: We use Helm to deploy the Elastic APM Server as it simplifies the deployment.
    • ConfigMap: This stores the configuration for the APM agent that will be part of your application.
    • Deployment: A Kubernetes deployment for your application, where the Elastic APM agent would be included.
    • Service: This exposes your application and the Elastic APM Server to the outside world. Depending on how your cluster is set up, you may have to adjust the service type and ports.

    The pulumi.export statements at the end provide output URLs for your APM server and application, which will be accessible once the deployment is complete and external traffic is allowed by your Kubernetes configuration.

    You would also have to containerize your application with the Elastic APM agent as part of it, and replace "your-application-image" with the actual image repository and tag.

    Remember, you need to have pulumi and pulumi_kubernetes Python packages installed to run this program. Save the above code in a file named __main__.py, and run pulumi up to apply the configuration to your cluster.


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