1. Auto-scaling ML Model Serving Workloads with App Mesh


    Auto-scaling is a critical feature for machine learning (ML) workloads, allowing you to handle variable load efficiently. When implementing auto-scaling for ML model serving with AWS, you can utilize multiple services to set up a robust and responsive environment. AWS App Mesh is particularly useful as it allows you to control and monitor microservices without requiring changes to the application code.

    AWS App Mesh is a service mesh that provides application-level networking, making it easy to manage microservice architectures. It standardizes how your microservices communicate, giving you end-to-end visibility and ensuring high-availability.

    To set up auto-scaling for ML workloads, you can use Kubernetes along with AWS App Mesh. Kubernetes provides native support for auto-scaling through the Horizontal Pod Autoscaler (HPA), which automatically scales the number of pods in a deployment based on observed CPU utilization or custom metrics.

    In this program, we'll define the infrastructure needed for auto-scaling ML model serving workloads using Pulumi with AWS App Mesh and Kubernetes.

    Here are the steps we will follow in the program:

    1. Set up an AWS App Mesh environment, including a mesh, virtual nodes, virtual services, and a virtual router.
    2. Deploy a Kubernetes cluster and configure the Horizontal Pod Autoscaler (HPA).
    3. Configure the HPA to scale based on custom metrics appropriate for ML workloads (e.g., request latency, queue depth, etc.).
    import pulumi import pulumi_aws as aws import pulumi_kubernetes as k8s from pulumi_kubernetes.apps.v1 import Deployment from pulumi_kubernetes.autoscaling.v2beta1 import HorizontalPodAutoscaler # Initialize AWS provider configuration. aws_provider = aws.Provider('aws', region='us-west-2') # Create an AWS App Mesh Mesh resource. This acts as the logical boundary for the network traffic # between the services that reside within it. app_mesh = aws.appmesh.Mesh('appMesh', opts=pulumi.ResourceOptions(provider=aws_provider)) # Define the Virtual Node(s) for the ML workloads. A Virtual Node acts as a logical pointer to a # particular service. virtual_node = aws.appmesh.VirtualNode( 'virtualNode', mesh_name=app_mesh.name, spec=aws.appmesh.VirtualNodeSpecArgs( # Definition for the service discovery mechanism for the virtual node. service_discovery=aws.appmesh.VirtualNodeSpecServiceDiscoveryArgs( dns=aws.appmesh.VirtualNodeSpecServiceDiscoveryDnsArgs( hostname='your-ml-service.local' ) ), ), opts=pulumi.ResourceOptions(provider=aws_provider) ) # Define a Virtual Service, which is an abstraction of a real service provided by a virtual node. virtual_service = aws.appmesh.VirtualService( 'virtualService', mesh_name=app_mesh.name, spec=aws.appmesh.VirtualServiceSpecArgs( provider=aws.appmesh.VirtualServiceSpecProviderArgs( virtual_node=aws.appmesh.VirtualServiceSpecProviderVirtualNodeArgs( virtual_node_name=virtual_node.name ) ) ), opts=pulumi.ResourceOptions(provider=aws_provider) ) # Deploy a Kubernetes cluster. For demonstration purposes, we are going to use a mocked cluster. # In a real-world scenario, you would configure your Kubernetes cluster here. k8s_cluster = [...] # Your Kubernetes cluster configuration. # Deploy the ML service as a Kubernetes Deployment. ml_deployment = Deployment( 'mlDeployment', spec={ 'selector': {'matchLabels': {'app': 'ml-service'}}, 'replicas': 1, 'template': { 'metadata': {'labels': {'app': 'ml-service'}}, 'spec': { 'containers': [{ 'name': 'ml-container', 'image': 'your-ml-model-image:latest', # Replace with your ML model serving image. 'ports': [{'containerPort': 8080}] }] } } } ) # Create a Horizontal Pod Autoscaler to automatically scale the number of pods # in the ML service deployment based on observed CPU utilization. hpa = HorizontalPodAutoscaler( 'mlHpa', spec={ 'scaleTargetRef': { 'apiVersion': 'apps/v1', 'kind': 'Deployment', 'name': ml_deployment.metadata['name'] }, # Add custom metrics for your ML model serving here. 'minReplicas': 1, 'maxReplicas': 10, 'metrics': [{ 'type': 'Resource', 'resource': { 'name': 'cpu', 'target': { 'type': 'Utilization', 'averageUtilization': 80 } } }] } ) # Export the App Mesh Virtual Node name and endpoint for the ML service. pulumi.export('virtual_node_name', virtual_node.name) pulumi.export('ml_service_endpoint', f"http://{virtual_node.spec.service_discovery.dns.hostname}")

    In this program, you're defining:

    • An App Mesh mesh as a network boundary.
    • A virtual node which represents a backend service.
    • A virtual service that abstracts the actual backend service behind a virtual node.
    • A Kubernetes deployment for deploying your ML model as a microservice.
    • A Horizontal Pod Autoscaler to scale your service based on CPU utilization or custom metrics.

    Remember to replace the values for mesh_name, hostname, and image with your specific details.

    Please make sure AWS CLI and Pulumi are properly configured to interact with your AWS account and the Kubernetes cluster. Additionally, replace the placeholder [...] with your actual Kubernetes cluster configuration depending on your environment setup.