import * as pulumi from "@pulumi/pulumi";
import * as inputs from "../types/input";
import * as outputs from "../types/output";
/**
 * Resource type definition for AWS::SageMaker::Model
 */
export declare class Model extends pulumi.CustomResource {
    /**
     * Get an existing Model resource's state with the given name, ID, and optional extra
     * properties used to qualify the lookup.
     *
     * @param name The _unique_ name of the resulting resource.
     * @param id The _unique_ provider ID of the resource to lookup.
     * @param opts Optional settings to control the behavior of the CustomResource.
     */
    static get(name: string, id: pulumi.Input<pulumi.ID>, opts?: pulumi.CustomResourceOptions): Model;
    /**
     * Returns true if the given object is an instance of Model.  This is designed to work even
     * when multiple copies of the Pulumi SDK have been loaded into the same process.
     */
    static isInstance(obj: any): obj is Model;
    /**
     * Specifies the containers in the inference pipeline.
     */
    readonly containers: pulumi.Output<outputs.sagemaker.ModelContainerDefinition[] | undefined>;
    /**
     * Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
     */
    readonly enableNetworkIsolation: pulumi.Output<boolean | undefined>;
    /**
     * The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
     */
    readonly executionRoleArn: pulumi.Output<string | undefined>;
    /**
     * Specifies details of how containers in a multi-container endpoint are called.
     */
    readonly inferenceExecutionConfig: pulumi.Output<outputs.sagemaker.ModelInferenceExecutionConfig | undefined>;
    /**
     * The Amazon Resource Name (ARN) of the model.
     */
    readonly modelArn: pulumi.Output<string>;
    /**
     * The name of the new model.
     */
    readonly modelName: pulumi.Output<string | undefined>;
    /**
     * The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
     */
    readonly primaryContainer: pulumi.Output<outputs.sagemaker.ModelContainerDefinition | undefined>;
    /**
     * An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging AWS Resources](https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).
     */
    readonly tags: pulumi.Output<outputs.Tag[] | undefined>;
    /**
     * A [VpcConfig](https://docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html) object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. `VpcConfig` is used in hosting services and in batch transform. For more information, see [Protect Endpoints by Using an Amazon Virtual Private Cloud](https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html) and [Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-vpc.html) .
     */
    readonly vpcConfig: pulumi.Output<outputs.sagemaker.ModelVpcConfig | undefined>;
    /**
     * Create a Model resource with the given unique name, arguments, and options.
     *
     * @param name The _unique_ name of the resource.
     * @param args The arguments to use to populate this resource's properties.
     * @param opts A bag of options that control this resource's behavior.
     */
    constructor(name: string, args?: ModelArgs, opts?: pulumi.CustomResourceOptions);
}
/**
 * The set of arguments for constructing a Model resource.
 */
export interface ModelArgs {
    /**
     * Specifies the containers in the inference pipeline.
     */
    containers?: pulumi.Input<pulumi.Input<inputs.sagemaker.ModelContainerDefinitionArgs>[]>;
    /**
     * Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
     */
    enableNetworkIsolation?: pulumi.Input<boolean>;
    /**
     * The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
     */
    executionRoleArn?: pulumi.Input<string>;
    /**
     * Specifies details of how containers in a multi-container endpoint are called.
     */
    inferenceExecutionConfig?: pulumi.Input<inputs.sagemaker.ModelInferenceExecutionConfigArgs>;
    /**
     * The name of the new model.
     */
    modelName?: pulumi.Input<string>;
    /**
     * The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
     */
    primaryContainer?: pulumi.Input<inputs.sagemaker.ModelContainerDefinitionArgs>;
    /**
     * An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging AWS Resources](https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).
     */
    tags?: pulumi.Input<pulumi.Input<inputs.TagArgs>[]>;
    /**
     * A [VpcConfig](https://docs.aws.amazon.com/sagemaker/latest/dg/API_VpcConfig.html) object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. `VpcConfig` is used in hosting services and in batch transform. For more information, see [Protect Endpoints by Using an Amazon Virtual Private Cloud](https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html) and [Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-vpc.html) .
     */
    vpcConfig?: pulumi.Input<inputs.sagemaker.ModelVpcConfigArgs>;
}