1. Visualizing Auto-scaling Events of ML Environments with CloudWatch


    To visualize auto-scaling events of ML (Machine Learning) environments using AWS CloudWatch, you will typically need to create AWS CloudWatch Alarms that react to the metrics related to your ML environment scaling activities. CloudWatch Alarms can trigger notifications or actions for scaling events, such as launching or terminating instances in response to load variations.

    Additionally, you can create a CloudWatch Dashboard to provide a visual representation of the scaling activities and other metrics relevant to your ML environment. Here is a Pulumi program in Python that demonstrates how to create CloudWatch Alarms and a CloudWatch Dashboard to achieve these objectives.

    The program entails the following resources:

    1. aws_native.cloudwatch.Alarm - This is used to create an alarm that watches over a particular metric (such as CPU utilization or memory usage) and performs actions when the metric breaches a specified threshold. These actions can include sending messages to SNS Topics, which in turn can notify an operator or trigger auto-scaling events.

    2. aws.cloudwatch.Dashboard - A dashboard resource is used to create a unified graphical user interface that displays data from various CloudWatch alarms and metrics, giving you insight into the performance and health of your resources.

    Let's proceed with the Pulumi program:

    import pulumi import pulumi_aws as aws # Define the CloudWatch Alarms for an ML auto-scaling event # Replace "AutoScalingGroupName" with the name of your ML environment's Auto Scaling group. cpu_alarm_high = aws.cloudwatch.Alarm("cpuAlarmHigh", comparison_operator="GreaterThanThreshold", evaluation_periods=2, metric_name="CPUUtilization", namespace="AWS/EC2", period=120, statistic="Average", threshold=80, # Set your own threshold value alarm_description="This alarm monitors EC2 CPU utilization", dimensions={"AutoScalingGroupName": "my-auto-scaling-group"}, actions_enabled=True, alarm_actions=["arn:aws:sns:us-east-1:123456789012:my-sns-topic"]) # Use your SNS topic ARN # Define a CloudWatch Dashboard JSON definition. # This JSON structure defines widgets and their layout on the dashboard. # You can add multiple widgets for different kinds of views (graphs, numbers, text) and metrics. dashboard_body = { "widgets": [ { "type": "metric", "x": 0, "y": 0, "width": 12, "height": 6, "properties": { "metrics": [ ["AWS/EC2", "CPUUtilization", "AutoScalingGroupName", "my-auto-scaling-group"] ], "period": 300, "stat": "Average", "region": "us-east-1", "title": "CPU Utilization" } }, # You can add more widgets here ] } # Create a new CloudWatch Dashboard for the ML environment ml_dashboard = aws.cloudwatch.Dashboard("mlDashboard", dashboard_name="MLAutoScalingDashboard", dashboard_body=pulumi.Output.from_input(dashboard_body).apply(pulumi.json.JsonEncoder.encode)) # Export the Dashboard URL pulumi.export('dashboard_url', pulumi.Output.concat( "https://console.aws.amazon.com/cloudwatch/home?region=", aws.config.region, "#dashboards:name=", ml_dashboard.dashboard_name))

    In the above program:

    • An alarm cpu_alarm_high watches over the ML environment's CPU utilization. You should customize the metric name, namespace, and dimensions according to your setup. The threshold should be configured to the level that indicates the environment needs scaling.
    • A dashboard ml_dashboard is created with a widget showing the CPU Utilization metric over time for the specified auto-scaling group. The JSON structure inside dashboard_body defines the layout and metrics shown on the dashboard.
    • You can extend dashboard_body to add more widgets for additional metrics if needed.

    This program results in a CloudWatch Dashboard that visualizes auto-scaling events. You can view the dashboard by navigating to the exported URL, which will lead you directly to the CloudWatch Dashboard in your AWS console.