1. Auto-Scaling Inference Endpoints with Service Discovery


    To set up auto-scaling inference endpoints with service discovery, we'll need to do the following:

    1. Define a compute resource that will serve as our inference endpoint. Depending on the cloud provider and the requirements, this could be an Amazon SageMaker endpoint, an Azure ML inference endpoint, or a custom solution using Kubernetes or EC2 instances, for example.
    2. Set up auto-scaling rules for the compute resource to scale the number of inference endpoints in response to the demand.
    3. Implement service discovery so new instances are automatically detected and requests are distributed among them.

    For this example, we'll use AWS as our cloud provider and Amazon SageMaker for the inference endpoints. SageMaker is a fully managed service that provides the ability to build, train, and deploy machine learning models. SageMaker Endpoints are the resources that Amazon provides for deploying your models for inference.

    We'll use AWS Service Discovery to maintain an up-to-date list of endpoints available to serve inference requests. AWS's Service Discovery service can register instances and update DNS records as new instances come online or go offline, enabling clients to discover instances that are available for work.

    Below you'll find Python code using Pulumi for setting up an auto-scaling SageMaker Endpoint with Service Discovery. Note that for an actual implementation, the SageMaker model and endpoint configuration would need to be specified with details about your ML model.

    import pulumi import pulumi_aws as aws # Set up a SageMaker model. This would require a pre-existing model, Docker container, and execution role. # In a real-world scenario, you would provide all the necessary details about your model container here. model = aws.sagemaker.Model("myModel", execution_role_arn="arn:aws:iam::123456789012:role/MySageMakerRole", primary_container={ "image": "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-model-container:latest", }) # Set up the endpoint configuration with initial instance count and instance type. # This can be adjusted later or set up to auto-scale based on CloudWatch metrics. endpoint_config = aws.sagemaker.EndpointConfig("myEndpointConfig", production_variants=[{ "instanceType": "ml.m4.xlarge", "modelName": model.name, "variantName": "variant-1", "initialInstanceCount": 1, }]) # Deploy the SageMaker endpoint endpoint = aws.sagemaker.Endpoint("myEndpoint", endpoint_config_name=endpoint_config.name) # Set up Service Discovery. # First, create an HTTP namespace. http_namespace = aws.servicediscovery.HttpNamespace("myHttpNamespace", description="My HTTP namespace for service discovery") # Then, create a service within that namespace. service_discovery_service = aws.servicediscovery.Service("myServiceDiscoveryService", description="My service discovery service", http_namespace_id=http_namespace.id, health_check_custom_config={ "failureThreshold": 1, }) # Register the SageMaker endpoint instance with the Service Discovery service. # This step typically requires custom logic to integrate the endpoint with Service Discovery, # like a Lambda function that updates the service registry upon instance changes. # The "instanceId" and "serviceId" attributes would be dynamically set based on your infrastructure. sagemaker_instance = aws.servicediscovery.Instance("mySageMakerInstance", instance_id=endpoint.id, service_id=service_discovery_service.id, attributes={ "AWS_INSTANCE_IPV4": "YOUR_INSTANCE_IP", # This should be set dynamically. "AWS_INSTANCE_PORT": "YOUR_INSTANCE_PORT", # This should be set dynamically. }) # Output the endpoint name for easy access pulumi.export("endpoint_name", endpoint.endpoint_name)

    This Pulumi program describes the provisioning of a SageMaker model and endpoint, then sets up Service Discovery to track instances of the endpoint. The SageMaker Endpoint is configured with an initial instance count and type, and it is associated with a Service Discovery service to enable client applications to find and invoke the inference service.

    Note that the actual implementation will involve creating a SageMaker Model and an Endpoint Configuration with the necessary specifications for your machine learning model. Additionally, integrating the service discovery instance might require creating custom scripts or Lambda functions that will update AWS Service Discovery with the correct IP address and port of the newly provisioned or deprovisioned instances.

    Please keep in mind the following:

    • This example assumes that you have previously set up the necessary permissions and roles (MySageMakerRole), along with a Docker container image containing your model.
    • Actual IP address and port for the SageMaker instance should be dynamically provided, possibly via AWS Lambda functions or other automation tools that can react to SageMaker endpoint changes.
    • Auto-scaling settings for the SageMaker Endpoint would need to be defined separately according to your application's scaling policies, often through AWS Autoscaling APIs or SageMaker's automatic scaling features.
    • For AWS Service Discovery to work properly with SageMaker, you must write additional code or use other AWS services to handle the lifecycle events of SageMaker endpoints and update the discovery service accordingly.

    The pulumi.export at the end is used to output the DNS name assigned to the created endpoint, which would be used by client applications to send inference requests.