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

Model

class Model : KotlinCustomResource

Provides a SageMaker model resource.

Example Usage

Basic usage:

package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.aws.iam.IamFunctions;
import com.pulumi.aws.iam.inputs.GetPolicyDocumentArgs;
import com.pulumi.aws.iam.Role;
import com.pulumi.aws.iam.RoleArgs;
import com.pulumi.aws.sagemaker.SagemakerFunctions;
import com.pulumi.aws.sagemaker.inputs.GetPrebuiltEcrImageArgs;
import com.pulumi.aws.sagemaker.Model;
import com.pulumi.aws.sagemaker.ModelArgs;
import com.pulumi.aws.sagemaker.inputs.ModelPrimaryContainerArgs;
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) {
        final var assumeRole = IamFunctions.getPolicyDocument(GetPolicyDocumentArgs.builder()
            .statements(GetPolicyDocumentStatementArgs.builder()
                .actions("sts:AssumeRole")
                .principals(GetPolicyDocumentStatementPrincipalArgs.builder()
                    .type("Service")
                    .identifiers("sagemaker.amazonaws.com")
                    .build())
                .build())
            .build());
        var exampleRole = new Role("exampleRole", RoleArgs.builder()
            .assumeRolePolicy(assumeRole.applyValue(getPolicyDocumentResult -> getPolicyDocumentResult.json()))
            .build());
        final var test = SagemakerFunctions.getPrebuiltEcrImage(GetPrebuiltEcrImageArgs.builder()
            .repositoryName("kmeans")
            .build());
        var exampleModel = new Model("exampleModel", ModelArgs.builder()
            .executionRoleArn(exampleRole.arn())
            .primaryContainer(ModelPrimaryContainerArgs.builder()
                .image(test.applyValue(getPrebuiltEcrImageResult -> getPrebuiltEcrImageResult.registryPath()))
                .build())
            .build());
    }
}

Inference Execution Config

mode - (Required) How containers in a multi-container are run. The following values are valid Serial and Direct.

Import

Models can be imported using the name, e.g.,

$ pulumi import aws:sagemaker/model:Model test_model model-foo

Properties

arn

val arn: Output<String>

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

containers

val containers: Output<List<ModelContainer>>?

Specifies containers in the inference pipeline. If not specified, the primary_container argument is required. Fields are documented below.

enableNetworkIsolation

val enableNetworkIsolation: Output<Boolean>?

Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

executionRoleArn

val executionRoleArn: Output<String>

A role that SageMaker can assume to access model artifacts and docker images for deployment.

val id: Output<String>

inferenceExecutionConfig

val inferenceExecutionConfig: Output<ModelInferenceExecutionConfig>

Specifies details of how containers in a multi-container endpoint are called. see Inference Execution Config.

name

val name: Output<String>

The name of the model (must be unique). If omitted, this provider will assign a random, unique name.

primaryContainer

val primaryContainer: Output<ModelPrimaryContainer>?

The primary docker image containing inference code that is used when the model is deployed for predictions. If not specified, the container argument is required. Fields are documented below.

pulumiChildResources

val pulumiChildResources: Set<KotlinResource>

pulumiResourceName

val pulumiResourceName: String

pulumiResourceType

val pulumiResourceType: String