Auto-Scaling ML Workloads Based on Computational Needs with PagerDuty.

Question

Pulumi · Accepted Answer

To auto-scale machine learning (ML) workloads based on computational needs with PagerDuty integration, you will typically need two core components:

Infrastructure to run ML workloads: This is usually a cluster setup with Kubernetes or a cloud service like AWS ECS or AWS EKS, which supports auto-scaling.
Integration with PagerDuty: This to notify or automate incident response when certain computational thresholds are met or exceeded (i.e., high CPU/memory usage).

The Pulumi program below is a high-level example of how you might set up infrastructure with AWS and integrate it with PagerDuty:

We'll use AWS to host our ML workloads.
We'll define an ECS cluster along with an Auto Scaling Group that manages the computational resources required for our ML tasks.
We'll then integrate PagerDuty to get alerts when we need to scale up our infrastructure due to high demand or if any incidents occur that need attention.

Below is the structure of the program with placeholders for the AWS and PagerDuty configurations:

Define the ECS cluster.
Define the Auto Scaling Group and set up auto-scaling based on desired metrics (e.g., CPU utilization).
Use the PagerDuty provider to create integration points, such as services and possibly escalation policies for incident management.

Please note that to keep this example focused, I haven't included the ML workload specifics. You might have a container definition with a pre-built ML model ready to deploy.

import pulumi
import pulumi_aws as aws
import pulumi_pagerduty as pagerduty

# Define an ECS cluster where your ML workloads will run
ecs_cluster = aws.ecs.Cluster("ecsCluster")

# Define an Auto Scaling Group for the ECS cluster based on desired metrics, such as CPU utilization
asg = aws.autoscaling.Group("asg",
    desired_capacity=1,
    max_size=3,
    min_size=1,
    health_check_grace_period=300,
    health_check_type="EC2",
    force_delete=True,
    vpc_zone_identifiers=["<subnet-id>"],
    launch_configuration=pulumi_aws.autoscaling.LaunchConfigurationArgs(
        image_id="<ami-id>",
        instance_type="t2.medium",
        # ... additional configuration ...
    ))

# Create the PagerDuty service which will be used to manage incidents
pagerduty_service = pagerduty.Service("pager_duty_service",
    name="ml-workload-scaling",
    escalation_policy="<escalation-policy-id>",
    teams=["<team-id>"],
    # Use 'incident_urgency_rule' to define how urgent the incident is based on severity
    incident_urgency_rule=pagerduty.ServiceIncidentUrgencyRuleArgs(
        type="use_support_hours",
        during_support_hours=pagerduty.ServiceIncidentUrgencyRuleDuringSupportHoursArgs(
            type="constant",
            urgency="high",
        ),
        outside_support_hours=pagerduty.ServiceIncidentUrgencyRuleOutsideSupportHoursArgs(
            type="constant",
            urgency="low",
        ),
    ),
    # ... additional configurations ...
)

# Export relevant URLs or IDs as output for ease of access or further integrations
pulumi.export("cluster_id", ecs_cluster.id)
pulumi.export("asg_name", asg.name)
pulumi.export("pager_duty_service_id", pagerduty_service.id)

In the program above, replace placeholders such as "<subnet-id>", "<ami-id>", "<escalation-policy-id>", and "<team-id>" with actual values relevant to your AWS environment and PagerDuty setup.

This program sets up the initial building blocks, including an ECS cluster for running ML workloads and PagerDuty services for incident management. You would also need appropriate cloud monitoring and alerting setup, which when triggered, could be directed to PagerDuty for the actual scaling response. This can be through AWS CloudWatch metrics, alarms, and notification linkage to PagerDuty services.

To further customize the auto-scaling behavior, you can modify the autoscaling policies based on the computational needs of your ML workloads, such as adjusting the scaling metrics and thresholds.

Once you have the Pulumi CLI installed and configured, you can apply this program by running pulumi up in the terminal from the directory where this program is saved.

Remember, this is just a starting point, and you'll need to tailor your Pulumi program to fit the precise operational and scaling requirements of your ML workloads.