1. AlertmanagerConfig for AI Workload Scaling Events


    To configure an AlertmanagerConfig for AI Workload Scaling Events, it's assumed that the events you're referring to are related to scaling operations for an AI application, such as machine learning model training or inference serving. These events could include scaling up the number of instances when the workload increases or scaling down when the workload decreases.

    We can tackle this problem by creating an alerting system that notifies us when certain scaling events occur. Pulumi doesn't have a direct concept of an AlertmanagerConfig specific to AI workload scaling events, but we can combine different cloud services to achieve this functionality.

    Let's say we're working with a Kubernetes cluster where our AI workload is containerized. We want to monitor this workload and send alerts based on specific events related to the autoscaling activities. How we might do this with Pulumi can involve the following steps:

    1. Use a HorizontalPodAutoscaler resource to manage the scaling of our AI workload based on CPU or memory usage, or custom metrics.
    2. Deploy Prometheus to our Kubernetes cluster to collect metrics.
    3. Create an Alertmanager configuration in Prometheus to define the conditions under which alerts should fire.
    4. Configure Alertmanager to send these alerts to a notification channel like an email, Slack, or a webhook, where they can be observed or further processed.

    For the purpose of this example, we will assume that Prometheus and Kubernetes are already set up, and we'll focus on defining Alertmanager rules that will be triggered by AI workload scaling events.

    Here's how you might set this up using Pulumi with Python as the programming language:

    import pulumi import pulumi_kubernetes as k8s # Configuration for the HorizontalPodAutoscaler, # which will automatically scale our AI application based on CPU utilization. hpa = k8s.autoscaling.v1.HorizontalPodAutoscaler( "ai-workload-hpa", spec=k8s.autoscaling.v1.HorizontalPodAutoscalerSpecArgs( scale_target_ref=k8s.autoscaling.v1.CrossVersionObjectReferenceArgs( api_version="apps/v1", kind="Deployment", name="ai-workload-deployment" # Replace this with the name of your actual deployment ), min_replicas=1, max_replicas=10, target_cpu_utilization_percentage=80 ), metadata=k8s.meta.v1.ObjectMetaArgs( namespace="default", ) ) # Placeholder for your existing Prometheus deployment, # which should be monitoring Kubernetes metrics, including those for our AI workload. # We assume the Prometheus Operator is installed and configured correctly. # Define an alert rule for Prometheus that will trigger when the AI workload # scales up or down. alert_rule = k8s.apiextensions.CustomResource( "ai-workload-scaling-alert", api_version="monitoring.coreos.com/v1", kind="PrometheusRule", metadata=k8s.meta.v1.ObjectMetaArgs( name="ai-workload-scaling-alert", namespace="monitoring", # Change this to the namespace where Prometheus is deployed. ), # You would define your rule here. This is an example that triggers # if the AI workload's CPU utilization is greater than 80% for more than 5 minutes. spec={ "groups": [{ "name": "ai-workload-scaling.rules", "rules": [{ "alert": "AIWorkloadHighCpuUtilization", "expr": "rate(container_cpu_usage_seconds_total{job='kubelet'," + "name='ai-workload-container'}[5m]) > 0.8", "for": "5m", "labels": { "severity": "warning", }, "annotations": { "summary": "High CPU utilization detected for AI Workload", "description": "The AI Workload has a CPU utilization over 80%." } }] }] } ) # Finally, let's export the name of the alert rule so we can easily refer to it outside of Pulumi. pulumi.export('alert_rule_name', alert_rule.metadata['name'])

    In the above program, we created a HorizontalPodAutoscaler (hpa) that watches over a deployment named ai-workload-deployment and scales the number of replicas based on CPU usage. We assume that you've got this deployment configured for your AI workload and it has the necessary metrics available for Prometheus to scrape.

    We then define a PrometheusRule Custom Resource (alert_rule). This resource specifies an alerting rule for when the CPU utilization goes above a certain threshold (in this case 80%), which might indicate that the AI workload might need to be scaled up.

    The PrometheusRule Custom Resource should be modified according to your exact monitoring and alerting requirements. The data within expr is an expression written in PromQL, Prometheus's query language, and would need customization specific to the metrics you're interested in.

    Remember to replace the placeholders like ai-workload-container with the actual value relevant to your environment. You would need to ensure that your Prometheus setup is configured to pick up this rule and Alertmanager is configured to send out the alerts.

    This program assumes you have the necessary access rights to create these resources in the respective Kubernetes cluster and namespace, and that Prometheus operator is already installed on your cluster. If not, you will need to adjust the security settings accordingly and potentially install the Prometheus operator.