EndpointConfig
The EndpointConfig resource lets you manage AWS SageMaker EndpointConfigs for deploying machine learning models at scale.
Minimal Example
Section titled “Minimal Example”Create a basic EndpointConfig with required properties and one optional property.
import AWS from "alchemy/aws/control";
const basicEndpointConfig = await AWS.SageMaker.EndpointConfig("basic-endpoint-config", { ProductionVariants: [{ VariantName: "AllTraffic", ModelName: "my-model", InitialInstanceCount: 1, InstanceType: "ml.m5.large" }], DataCaptureConfig: { CaptureOptions: [{ CaptureMode: "Input" }], DestinationS3Uri: "s3://my-data-capture-bucket/", CaptureContentTypeHeader: { CsvContentTypes: ["text/csv"], JsonContentTypes: ["application/json"] } }});
Advanced Configuration
Section titled “Advanced Configuration”Configure an EndpointConfig with shadow production variants and network isolation.
const advancedEndpointConfig = await AWS.SageMaker.EndpointConfig("advanced-endpoint-config", { ProductionVariants: [{ VariantName: "MainTraffic", ModelName: "my-main-model", InitialInstanceCount: 2, InstanceType: "ml.m5.large" }], ShadowProductionVariants: [{ VariantName: "ShadowTraffic", ModelName: "my-shadow-model", InitialInstanceCount: 1, InstanceType: "ml.m5.large" }], EnableNetworkIsolation: true, KmsKeyId: "arn:aws:kms:us-east-1:123456789012:key/example-key-id"});
Custom VPC Configuration
Section titled “Custom VPC Configuration”Create an EndpointConfig that specifies a VPC configuration for enhanced security.
const vpcEndpointConfig = await AWS.SageMaker.EndpointConfig("vpc-endpoint-config", { ProductionVariants: [{ VariantName: "VPCConfiguredTraffic", ModelName: "my-vpc-model", InitialInstanceCount: 1, InstanceType: "ml.m5.large" }], VpcConfig: { SecurityGroupIds: ["sg-0123456789abcdef0"], Subnets: ["subnet-0123456789abcdef0", "subnet-0fedcba9876543210"] }});
Async Inference Configuration
Section titled “Async Inference Configuration”Set up an EndpointConfig with asynchronous inference capabilities.
const asyncInferenceEndpointConfig = await AWS.SageMaker.EndpointConfig("async-inference-endpoint-config", { ProductionVariants: [{ VariantName: "AsyncInferenceTraffic", ModelName: "my-async-model", InitialInstanceCount: 1, InstanceType: "ml.m5.large" }], AsyncInferenceConfig: { OutputConfig: { S3OutputPath: "s3://my-async-output-bucket/", KmsKeyId: "arn:aws:kms:us-east-1:123456789012:key/example-key-id" }, ClientId: "my-client-id" }});