Unlocking Enhanced Metrics for Amazon SageMaker AI Endpoints

Introduction to Enhanced Metrics

What’s New in Enhanced Metrics

Instance-Level Metrics: Access for All Endpoints

Resource Utilization Metrics

Invocation Metrics

Container-Level Metrics: Visibility for Inference Components

Resource Utilization Metrics for Containers

Invocation Metrics for Containers

Configuring Enhanced Metrics

Choosing Your Publishing Frequency

Example Use Cases for Enhanced Metrics

Real-Time GPU Utilization Tracking

Per-Model Cost Attribution in Multi-Model Deployments

Cluster-Wide Resource Monitoring

Creating Operational Dashboards

Best Practices for Utilizing Enhanced Metrics

Conclusion: Transforming ML Workload Monitoring

About the Authors

Enhanced Metrics for Amazon SageMaker AI: Your Guide to Effective Monitoring in Production

Running machine learning (ML) models in production isn’t just about robust infrastructure or the ability to scale; it’s about maintaining an eagle-eyed view of performance and resource utilization. As latency spikes, invocations fail, or resources dwindle, having immediate insights is crucial to diagnosing and resolving issues before they ripple outwards and impact customer experience.

The Limitations of Traditional Metrics

Historically, Amazon SageMaker AI provided valuable insights through Amazon CloudWatch metrics, but these were primarily high-level aggregate metrics across all instances and containers. While they served as a useful tool for overall health monitoring, they often masked critical details of individual instances and containers. This lack of granularity complicated troubleshooting and hindered optimization efforts, making it tough to uncover performance bottlenecks.

Unlocking Granular Visibility with Enhanced Metrics

The introduction of enhanced metrics for Amazon SageMaker AI endpoints marks a significant advancement. It allows for configurable publishing frequency and provides the granular visibility necessary for monitoring, troubleshooting, and enhancing production workloads. With enhanced metrics, you can now delve into container-level and instance-level specifics, offering capabilities such as:

• Per Model Copy Metrics: When deploying multiple model copies across an endpoint, tracking metrics per model copy (e.g., concurrent requests, GPU and CPU utilization) becomes invaluable for issue diagnosis.

• Model Cost Analysis: In shared infrastructure environments, understanding the true cost per model is challenging. Enhanced metrics facilitate this by monitoring GPU allocation at the inference component level.

What’s New?

Enhanced metrics introduce two categories, each with multiple levels of granularity:

1. EC2 Resource Utilization Metrics: These metrics focus on tracking CPU, GPU, and memory consumption at both instance and container levels.

2. Invocation Metrics: These allow for a detailed examination of request patterns, errors, latency, and concurrency with precise dimensions, enabling deeper analyses based on your endpoint configuration.

Instance-level Metrics: A Standard for All Endpoints

Every SageMaker AI endpoint now comes with instance-level metrics, providing insights into the activities occurring on every Amazon Elastic Compute Cloud (EC2) instance linked to the endpoint.

Resource Utilization

Utilizing the CloudWatch namespace /aws/sagemaker/Endpoints, users can track metrics such as CPU utilization, memory consumption, and GPU metrics per host. This immediate identification of issues helps streamline resolution efforts.

Invocation Metrics

Invoke the CloudWatch namespace AWS/SageMaker to monitor patterns related to requests, errors, and latency at the instance level. By pinpointing which instance faced issues, users can correlate performance problems with specific resources, making troubleshooting more efficient.

Container-level Metrics: For Inference Components

Using Inference Components to host multiple models? Enhanced metrics provide visibility at the container level.

Resource Utilization per Container

Monitor resource consumption for each container, including GPU and CPU utilization, which aids in identifying performance issues and managing resource allocation effectively across multi-tenant environments.

Configuring Enhanced Metrics

Enabling enhanced metrics requires a single parameter in your endpoint configuration:

response = sagemaker_client.create_endpoint_config(
    EndpointConfigName="my-config",
    ProductionVariants=[{
        'VariantName': 'AllTraffic',
        'ModelName': 'my-model',
        'InstanceType': 'ml.g6.12xlarge',
        'InitialInstanceCount': 2
    }],
    MetricsConfig={
        'EnableEnhancedMetrics': True,
        'MetricsPublishFrequencyInSeconds': 10,  # Default 60s
    }
)

Optimal Publishing Frequency

After activating enhanced metrics, users can configure the publishing frequency based on their monitoring needs:

• Standard Resolution (60 seconds): Ideal for most workloads, balancing detail with cost management.

• High Resolution (10 or 30 seconds): Essential for critical applications, allowing near real-time monitoring and aggressive auto-scaling.

Example Use Cases

Enhanced metrics can deliver tangible business value across various scenarios, including:

Real-time GPU Utilization Tracking

For applications running multiple models, tracking GPU allocation and use is vital for refining costs and ensuring performance.

Per-model Cost Attribution

Enhanced metrics enable accurate cost calculations for models sharing infrastructure, making chargeback models more manageable.

Comprehensive Cluster Resource Monitoring

By aggregating metrics across all inference components on an endpoint, you can efficiently plan capacity for existing or new models.

Creating Operational Dashboards

Use the accompanying notebook to programmatically create CloudWatch dashboards that consolidate visibility across metrics, from resource utilization to per-model cost tracking.

Best Practices

1. Start with a 60-second resolution for manageable costs and sufficient detail.
2. Enable 10-second resolution only for critical endpoints or during troubleshooting.
3. Leverage strategic dimensions to transition from cluster-wide views to specific containers.
4. Employ cost allocation dashboards to track cumulative costs accurately.
5. Monitor unused GPU capacity to maintain buffer resources for scaling.
6. Correlate metrics to connect resource utilization with request patterns for deeper insights.

Conclusion

Enhanced Metrics for Amazon SageMaker AI Endpoints revolutionize how we monitor, manage, and enhance production ML workloads. Achieving granular visibility, adjustable publishing frequency, and robust dimensions equips your team with the operational intelligence necessary to run AI effectively at scale.

Take the first step today by enabling enhanced metrics on your SageMaker AI endpoints and exploring the provided notebook for comprehensive implementation examples and practical functions.

About the Authors

Dan Ferguson

Dan Ferguson is a Solutions Architect at AWS, residing in New York, USA. He specializes in machine learning services, assisting customers in efficiently integrating ML workflows.

Marc Karp

Marc Karp is an ML Architect with the Amazon SageMaker Service team. He focuses on enabling customers to design, deploy, and manage scalable ML workloads. When not working, he enjoys traveling and discovering new destinations.