Auto-scaling AI Workloads Monitoring with Datadog on AWS

Question

Pulumi · Accepted Answer

To build an infrastructure setup on AWS for auto-scaling AI workloads and monitor them with Datadog, you'll need to accomplish several tasks with Pulumi:

1. Create an auto-scaling group on AWS for your AI workloads.
2. Integrate Datadog monitoring to keep track of the performance and status of your auto-scaling group and the individual instances within it.
3. Set up Datadog monitors that will alert you in case any of the metrics you're tracking fall outside the desired thresholds.

For this process, we'll define an auto-scaling group in AWS using Pulumi's AWS package. Then, we'll define two types of Datadog resources with Pulumi's Datadog package: `Monitor` for setting up alerts based on different metrics and `MetricMetadata` to describe the metrics we want to track.

Here's a Pulumi program in Python that demonstrates how to set up auto-scaling of AI workloads and monitoring with Datadog on AWS:

```python
import pulumi
import pulumi_aws as aws
import pulumi_datadog as datadog

# Replace these with your specific values
ami_id = "ami-12345678"
instance_type = "t2.medium"
key_name = "your-key-pair"
desired_capacity = 2
max_size = 5
min_size = 1
scaling_adjustment = 1
cool_down = 300

# Create an auto-scaling group on AWS for AI workloads
auto_scaling_group = aws.autoscaling.Group("ai-auto-scaling-group",
    desired_capacity=desired_capacity,
    max_size=max_size,
    min_size=min_size,
    health_check_type="EC2",
    force_delete=True,
    launch_configuration=aws.autoscaling.LaunchConfiguration("ai-launch-configuration",
        image_id=ami_id,
        instance_type=instance_type,
        key_name=key_name,
    ).name,
)

# Define a scale-up policy
scale_up_policy = aws.autoscaling.Policy("scale-up",
    scaling_adjustment=scaling_adjustment,
    adjustment_type="ChangeInCapacity",
    cooldown=cool_down,
    autoscaling_group_name=auto_scaling_group.name,
)

# Define a Datadog monitor for high CPU usage
cpu_monitor = datadog.Monitor("cpu-monitor",
    name="High CPU Usage",
    type="metric alert",
    query="avg(last_5m):avg:aws.ec2.cpuutilization{auto_scaling_group:ai-auto-scaling-group} by {instance} > 80",
    message="""{{#is_alert}}
                High CPU utilization detected for instance {{instance}} in auto scaling group ai-auto-scaling-group.
                {{/is_alert}}
                {{#is_recovery}}
                CPU utilization for instance {{instance}} has returned to normal.
                {{/is_recovery}}""",
    tags=["auto_scaling_group:ai-auto-scaling-group", "environment:production"],
    notify_no_data=True,
    new_host_delay=300,
)

# Define a monitor for auto-scaling events
scaling_monitor = datadog.Monitor("scaling-monitor",
    name="Auto-scaling event",
    type="event alert",
    query='"Scale Up" OR "Scale Down" @ai-auto-scaling-group',
    message="""{{#is_alert}}
                Auto-scaling event detected for group ai-auto-scaling-group.
                {{/is_alert}}""",
    tags=["auto_scaling_group:ai-auto-scaling-group"],
)

# The URLs can be exported using stack outputs, with pulumi.Output.all construct if needed for composed attributes
pulumi.export("auto_scaling_group_name", auto_scaling_group.name)
pulumi.export("cpu_monitor_id", cpu_monitor.id)
pulumi.export("scaling_monitor_id", scaling_monitor.id)
```

This program starts by setting up the auto-scaling group with AWS, specifying the desired capacity and limits. Next, it creates two Datadog monitors: one for high CPU usage and the other for tracking scaling events. The `query` property for the `cpu_monitor` is a Datadog-specific string that represents what we're monitoring, while the `message` property is the message that will be sent when the alert condition is met.

Make sure you have appropriate credentials set up for both AWS and Datadog, and install the required Pulumi packages for AWS and Datadog by running:

```bash
pip install pulumi_aws pulumi_datadog
```

After setting up the program above, you can deploy it using Pulumi CLI commands such as `pulumi up`. The program will provision resources on AWS and set up monitoring in Datadog, you will be able to see the exported stack outputs showing the names and IDs of your resources.

For more advanced scenarios or to tailor the monitoring to your specific needs, you can tweak the properties of the auto-scaling group and the monitor definitions. For example, you might want to adjust the scaling policies or create additional monitors for other metrics like memory usage or disk I/O. The Datadog documentation provides extensive information on the metrics and query syntax to use for monitors.