1. Scaling AI Training Jobs Based on Prometheus Metrics


    Scaling artificial intelligence (AI) training jobs based on Prometheus metrics can be an essential task to efficiently utilize resources while ensuring timely processing of these jobs. Prometheus is a powerful monitoring tool widely used for collecting and processing metrics.

    In a cloud infrastructure context, we could use the Prometheus metrics to scale the number of instances running our training jobs automatically. This could be achieved by setting up alarms based on Prometheus metrics which, when triggered, would initiate a scaling process.

    To begin, let's use AWS as our cloud provider since it has support for integrating with Prometheus through Amazon Managed Service for Prometheus (AMP). Additionally, we can use AWS Application Auto Scaling to adjust the number of instances used by the training jobs in response to demand.

    Here is how you could set it up with Pulumi in Python:

    1. We would create a workspace in Amazon Managed Service for Prometheus to hold our metrics data.
    2. We would define an alertmanager configuration in that workspace to specify what conditions trigger an alert.
    3. Use AWS Application Auto Scaling to adjust the number of instances: We would create a scalable target configuration, representing our machine learning training job service that we want to scale. Next, we'd set up scaling policies that define how the service should scale out (add instances) or in (remove instances) in response to the alerts from Prometheus.

    Now, let's see how this would look in code:

    import pulumi import pulumi_aws as aws # Creating an Amazon Managed Service for Prometheus Workspace amp_workspace = aws.amp.Workspace("aiTrainingAmpWorkspace") # Defining an Alert Manager configuration alertmanager_definition = aws.amp.AlertManagerDefinition( "aiTrainingAlertManager", workspace_id=amp_workspace.id, # The definition parameter should contain your alerting configuration # This configuration would set up alerts based on the metrics # that indicate when to scale your training jobs definition='''global: resolve_timeout: 5m route: receiver: 'webhook' receivers: - name: 'webhook' webhook_configs: - url: <PLACE-YOUR-WEBHOOK-URL-HERE> ''' ) # Assuming we have an ECS service to scale, let's set up auto-scaling for it app_auto_scaling_target = aws.appautoscaling.Target( "aiTrainingAppAutoScalingTarget", max_capacity=10, # The maximum number of instances to scale up to min_capacity=1, # The minimum number of instances to maintain resource_id=pulumi.Output.concat("service/", pulumi.Config().require("ecsClusterName"), "/", pulumi.Config().require("ecsServiceName")), scalable_dimension="ecs:service:DesiredCount", service_namespace="ecs" ) # Defining the scaling policy based on Prometheus metrics scaling_policy = aws.appautoscaling.Policy( "aiTrainingScalingPolicy", policy_type="TargetTrackingScaling", resource_id=app_auto_scaling_target.resource_id, scalable_dimension=app_auto_scaling_target.scalable_dimension, service_namespace=app_auto_scaling_target.service_namespace, target_tracking_scaling_policy_configuration=aws.appautoscaling.PolicyTargetTrackingScalingPolicyConfigurationArgs( target_value=50.0, # The target value for the metric predefined_metric_specification=aws.appautoscaling.PolicyPredefinedMetricSpecificationArgs( predefined_metric_type="ECSServiceAverageCPUUtilization" ), # The scale in and scale out cooldown periods are set to prevent the scaling actions from firing too frequently scale_in_cooldown=300, scale_out_cooldown=300 ) ) pulumi.export('workspaceId', amp_workspace.id) pulumi.export('scalingPolicyArn', scaling_policy.arn)

    In the above program, you'll need to replace <PLACE-YOUR-WEBHOOK-URL-HERE> with the actual webhook endpoint where you'd like the Prometheus alerts to be sent. This endpoint could be an AWS Lambda function or any other service that listens for the webhook calls and triggers the auto-scaling action via the AWS API.

    It's important to note that the definition of the Alert Manager configuration should be adjusted to your specific case, and the target value and metric used in the scaling policy should be based on the actual needs of your AI training jobs. The predefined metric "ECSServiceAverageCPUUtilization" is used here as an example. You would likely have a custom metric that better reflects your workloads' operational demands.

    Remember to configure your Pulumi application with the ECS cluster and service name by using pulumi config to set the ecsClusterName and ecsServiceName.

    Lastly, this Pulumi script will automatically export the Workspace ID and the ARN of the scaling policy, which can be useful for further integration and reference.