pulumi-aws-kotlin/com.pulumi.aws.sagemaker.kotlin/EndpointConfiguration

EndpointConfiguration

class EndpointConfiguration : KotlinCustomResource

Provides a SageMaker AI endpoint configuration resource.

Example Usage

Basic usage:

import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";
const ec = new aws.sagemaker.EndpointConfiguration("ec", {
    name: "my-endpoint-config",
    productionVariants: [{
        variantName: "variant-1",
        modelName: m.name,
        initialInstanceCount: 1,
        instanceType: "ml.t2.medium",
    }],
    tags: {
        Name: "foo",
    },
});

import pulumi
import pulumi_aws as aws
ec = aws.sagemaker.EndpointConfiguration("ec",
    name="my-endpoint-config",
    production_variants=[{
        "variant_name": "variant-1",
        "model_name": m["name"],
        "initial_instance_count": 1,
        "instance_type": "ml.t2.medium",
    }],
    tags={
        "Name": "foo",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Aws = Pulumi.Aws;
return await Deployment.RunAsync(() =>
{
    var ec = new Aws.Sagemaker.EndpointConfiguration("ec", new()
    {
        Name = "my-endpoint-config",
        ProductionVariants = new[]
        {
            new Aws.Sagemaker.Inputs.EndpointConfigurationProductionVariantArgs
            {
                VariantName = "variant-1",
                ModelName = m.Name,
                InitialInstanceCount = 1,
                InstanceType = "ml.t2.medium",
            },
        },
        Tags =
        {
            { "Name", "foo" },
        },
    });
});

package main
import (
	"github.com/pulumi/pulumi-aws/sdk/v6/go/aws/sagemaker"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := sagemaker.NewEndpointConfiguration(ctx, "ec", &sagemaker.EndpointConfigurationArgs{
			Name: pulumi.String("my-endpoint-config"),
			ProductionVariants: sagemaker.EndpointConfigurationProductionVariantArray{
				&sagemaker.EndpointConfigurationProductionVariantArgs{
					VariantName:          pulumi.String("variant-1"),
					ModelName:            pulumi.Any(m.Name),
					InitialInstanceCount: pulumi.Int(1),
					InstanceType:         pulumi.String("ml.t2.medium"),
				},
			},
			Tags: pulumi.StringMap{
				"Name": pulumi.String("foo"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.aws.sagemaker.EndpointConfiguration;
import com.pulumi.aws.sagemaker.EndpointConfigurationArgs;
import com.pulumi.aws.sagemaker.inputs.EndpointConfigurationProductionVariantArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        var ec = new EndpointConfiguration("ec", EndpointConfigurationArgs.builder()
            .name("my-endpoint-config")
            .productionVariants(EndpointConfigurationProductionVariantArgs.builder()
                .variantName("variant-1")
                .modelName(m.name())
                .initialInstanceCount(1)
                .instanceType("ml.t2.medium")
                .build())
            .tags(Map.of("Name", "foo"))
            .build());
    }
}

resources:
  ec:
    type: aws:sagemaker:EndpointConfiguration
    properties:
      name: my-endpoint-config
      productionVariants:
        - variantName: variant-1
          modelName: ${m.name}
          initialInstanceCount: 1
          instanceType: ml.t2.medium
      tags:
        Name: foo

Import

Using pulumi import, import endpoint configurations using the name. For example:

$ pulumi import aws:sagemaker/endpointConfiguration:EndpointConfiguration test_endpoint_config endpoint-config-foo

Properties

arn

val arn: Output<String>

The Amazon Resource Name (ARN) assigned by AWS to this endpoint configuration.

asyncInferenceConfig

val asyncInferenceConfig: Output<EndpointConfigurationAsyncInferenceConfig>?

Specifies configuration for how an endpoint performs asynchronous inference.

dataCaptureConfig

val dataCaptureConfig: Output<EndpointConfigurationDataCaptureConfig>?

Specifies the parameters to capture input/output of SageMaker AI models endpoints. Fields are documented below.

val id: Output<String>

kmsKeyArn

val kmsKeyArn: Output<String>?

Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker AI uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

name

val name: Output<String>

The name of the endpoint configuration. If omitted, this provider will assign a random, unique name. Conflicts with name_prefix.

namePrefix

val namePrefix: Output<String>

Creates a unique endpoint configuration name beginning with the specified prefix. Conflicts with name.

productionVariants

val productionVariants: Output<List<EndpointConfigurationProductionVariant>>

An list of ProductionVariant objects, one for each model that you want to host at this endpoint. Fields are documented below.

pulumiChildResources

val pulumiChildResources: Set<KotlinResource>

pulumiResourceName

val pulumiResourceName: String

pulumiResourceType

val pulumiResourceType: String

shadowProductionVariants

val shadowProductionVariants: Output<List<EndpointConfigurationShadowProductionVariant>>?

Array of ProductionVariant objects. There is one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants. If you use this field, you can only specify one variant for ProductionVariants and one variant for ShadowProductionVariants. Fields are documented below.