@pulumi/aws-native
Version:
The Pulumi AWS Cloud Control Provider enables you to build, deploy, and manage [any AWS resource that's supported by the AWS Cloud Control API](https://github.com/pulumi/pulumi-aws-native/blob/master/provider/cmd/pulumi-gen-aws-native/supported-types.txt)
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TypeScript
import * as pulumi from "@pulumi/pulumi";
import * as inputs from "../types/input";
import * as outputs from "../types/output";
import * as enums from "../types/enums";
/**
* Resource Type definition for AWS::SageMaker::ModelPackage
*/
export declare class ModelPackage extends pulumi.CustomResource {
/**
* Get an existing ModelPackage 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): ModelPackage;
/**
* Returns true if the given object is an instance of ModelPackage. 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 ModelPackage;
/**
* An array of additional Inference Specification objects.
*/
readonly additionalInferenceSpecifications: pulumi.Output<outputs.sagemaker.ModelPackageAdditionalInferenceSpecificationDefinition[] | undefined>;
/**
* An array of additional Inference Specification objects to be added to the existing array. The total number of additional Inference Specification objects cannot exceed 15. Each additional Inference Specification object specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
*/
readonly additionalInferenceSpecificationsToAdd: pulumi.Output<outputs.sagemaker.ModelPackageAdditionalInferenceSpecificationDefinition[] | undefined>;
/**
* A description provided when the model approval is set.
*/
readonly approvalDescription: pulumi.Output<string | undefined>;
/**
* Whether the model package is to be certified to be listed on AWS Marketplace. For information about listing model packages on AWS Marketplace, see [List Your Algorithm or Model Package on AWS Marketplace](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-list.html) .
*/
readonly certifyForMarketplace: pulumi.Output<boolean | undefined>;
/**
* A unique token that guarantees that the call to this API is idempotent.
*/
readonly clientToken: pulumi.Output<string | undefined>;
/**
* The time that the model package was created.
*/
readonly creationTime: pulumi.Output<string>;
/**
* The metadata properties for the model package.
*/
readonly customerMetadataProperties: pulumi.Output<outputs.sagemaker.ModelPackageCustomerMetadataProperties | undefined>;
/**
* The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
*/
readonly domain: pulumi.Output<string | undefined>;
/**
* Represents the drift check baselines that can be used when the model monitor is set using the model package.
*/
readonly driftCheckBaselines: pulumi.Output<outputs.sagemaker.ModelPackageDriftCheckBaselines | undefined>;
/**
* Defines how to perform inference generation after a training job is run.
*/
readonly inferenceSpecification: pulumi.Output<outputs.sagemaker.ModelPackageInferenceSpecification | undefined>;
/**
* The last time the model package was modified.
*/
readonly lastModifiedTime: pulumi.Output<string | undefined>;
/**
* Metadata properties of the tracking entity, trial, or trial component.
*/
readonly metadataProperties: pulumi.Output<outputs.sagemaker.ModelPackageMetadataProperties | undefined>;
/**
* The approval status of the model. This can be one of the following values.
*
* - `APPROVED` - The model is approved
* - `REJECTED` - The model is rejected.
* - `PENDING_MANUAL_APPROVAL` - The model is waiting for manual approval.
*/
readonly modelApprovalStatus: pulumi.Output<enums.sagemaker.ModelPackageModelApprovalStatus | undefined>;
/**
* An Amazon SageMaker Model Card.
*/
readonly modelCard: pulumi.Output<outputs.sagemaker.ModelPackageModelCard | undefined>;
/**
* Metrics for the model.
*/
readonly modelMetrics: pulumi.Output<outputs.sagemaker.ModelPackageModelMetrics | undefined>;
/**
* The Amazon Resource Name (ARN) of the model package.
*/
readonly modelPackageArn: pulumi.Output<string>;
/**
* The description of the model package.
*/
readonly modelPackageDescription: pulumi.Output<string | undefined>;
/**
* The model group to which the model belongs.
*/
readonly modelPackageGroupName: pulumi.Output<string | undefined>;
/**
* The name of the model package. The name can be as follows:
*
* - For a versioned model, the name is automatically generated by SageMaker Model Registry and follows the format ' `ModelPackageGroupName/ModelPackageVersion` '.
* - For an unversioned model, you must provide the name.
*/
readonly modelPackageName: pulumi.Output<string | undefined>;
/**
* The status of the model package. This can be one of the following values.
*
* - `PENDING` - The model package creation is pending.
* - `IN_PROGRESS` - The model package is in the process of being created.
* - `COMPLETED` - The model package was successfully created.
* - `FAILED` - The model package creation failed.
* - `DELETING` - The model package is in the process of being deleted.
*/
readonly modelPackageStatus: pulumi.Output<enums.sagemaker.ModelPackageStatus>;
/**
* Specifies the validation and image scan statuses of the model package.
*/
readonly modelPackageStatusDetails: pulumi.Output<outputs.sagemaker.ModelPackageStatusDetails | undefined>;
/**
* The version number of a versioned model.
*/
readonly modelPackageVersion: pulumi.Output<number | undefined>;
/**
* The Amazon Simple Storage Service path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
*/
readonly samplePayloadUrl: pulumi.Output<string | undefined>;
readonly securityConfig: pulumi.Output<outputs.sagemaker.ModelPackageSecurityConfig | undefined>;
/**
* Indicates if you want to skip model validation.
*/
readonly skipModelValidation: pulumi.Output<enums.sagemaker.ModelPackageSkipModelValidation | undefined>;
/**
* A list of algorithms that were used to create a model package.
*/
readonly sourceAlgorithmSpecification: pulumi.Output<outputs.sagemaker.ModelPackageSourceAlgorithmSpecification | undefined>;
/**
* The URI of the source for the model package.
*/
readonly sourceUri: pulumi.Output<string | undefined>;
/**
* An array of key-value pairs to apply to this resource.
*/
readonly tags: pulumi.Output<outputs.Tag[] | undefined>;
/**
* The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.
*/
readonly task: pulumi.Output<string | undefined>;
/**
* Specifies batch transform jobs that SageMaker runs to validate your model package.
*/
readonly validationSpecification: pulumi.Output<outputs.sagemaker.ModelPackageValidationSpecification | undefined>;
/**
* Create a ModelPackage 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?: ModelPackageArgs, opts?: pulumi.CustomResourceOptions);
}
/**
* The set of arguments for constructing a ModelPackage resource.
*/
export interface ModelPackageArgs {
/**
* An array of additional Inference Specification objects.
*/
additionalInferenceSpecifications?: pulumi.Input<pulumi.Input<inputs.sagemaker.ModelPackageAdditionalInferenceSpecificationDefinitionArgs>[]>;
/**
* An array of additional Inference Specification objects to be added to the existing array. The total number of additional Inference Specification objects cannot exceed 15. Each additional Inference Specification object specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
*/
additionalInferenceSpecificationsToAdd?: pulumi.Input<pulumi.Input<inputs.sagemaker.ModelPackageAdditionalInferenceSpecificationDefinitionArgs>[]>;
/**
* A description provided when the model approval is set.
*/
approvalDescription?: pulumi.Input<string>;
/**
* Whether the model package is to be certified to be listed on AWS Marketplace. For information about listing model packages on AWS Marketplace, see [List Your Algorithm or Model Package on AWS Marketplace](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-list.html) .
*/
certifyForMarketplace?: pulumi.Input<boolean>;
/**
* A unique token that guarantees that the call to this API is idempotent.
*/
clientToken?: pulumi.Input<string>;
/**
* The metadata properties for the model package.
*/
customerMetadataProperties?: pulumi.Input<inputs.sagemaker.ModelPackageCustomerMetadataPropertiesArgs>;
/**
* The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
*/
domain?: pulumi.Input<string>;
/**
* Represents the drift check baselines that can be used when the model monitor is set using the model package.
*/
driftCheckBaselines?: pulumi.Input<inputs.sagemaker.ModelPackageDriftCheckBaselinesArgs>;
/**
* Defines how to perform inference generation after a training job is run.
*/
inferenceSpecification?: pulumi.Input<inputs.sagemaker.ModelPackageInferenceSpecificationArgs>;
/**
* The last time the model package was modified.
*/
lastModifiedTime?: pulumi.Input<string>;
/**
* Metadata properties of the tracking entity, trial, or trial component.
*/
metadataProperties?: pulumi.Input<inputs.sagemaker.ModelPackageMetadataPropertiesArgs>;
/**
* The approval status of the model. This can be one of the following values.
*
* - `APPROVED` - The model is approved
* - `REJECTED` - The model is rejected.
* - `PENDING_MANUAL_APPROVAL` - The model is waiting for manual approval.
*/
modelApprovalStatus?: pulumi.Input<enums.sagemaker.ModelPackageModelApprovalStatus>;
/**
* An Amazon SageMaker Model Card.
*/
modelCard?: pulumi.Input<inputs.sagemaker.ModelPackageModelCardArgs>;
/**
* Metrics for the model.
*/
modelMetrics?: pulumi.Input<inputs.sagemaker.ModelPackageModelMetricsArgs>;
/**
* The description of the model package.
*/
modelPackageDescription?: pulumi.Input<string>;
/**
* The model group to which the model belongs.
*/
modelPackageGroupName?: pulumi.Input<string>;
/**
* The name of the model package. The name can be as follows:
*
* - For a versioned model, the name is automatically generated by SageMaker Model Registry and follows the format ' `ModelPackageGroupName/ModelPackageVersion` '.
* - For an unversioned model, you must provide the name.
*/
modelPackageName?: pulumi.Input<string>;
/**
* Specifies the validation and image scan statuses of the model package.
*/
modelPackageStatusDetails?: pulumi.Input<inputs.sagemaker.ModelPackageStatusDetailsArgs>;
/**
* The version number of a versioned model.
*/
modelPackageVersion?: pulumi.Input<number>;
/**
* The Amazon Simple Storage Service path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
*/
samplePayloadUrl?: pulumi.Input<string>;
securityConfig?: pulumi.Input<inputs.sagemaker.ModelPackageSecurityConfigArgs>;
/**
* Indicates if you want to skip model validation.
*/
skipModelValidation?: pulumi.Input<enums.sagemaker.ModelPackageSkipModelValidation>;
/**
* A list of algorithms that were used to create a model package.
*/
sourceAlgorithmSpecification?: pulumi.Input<inputs.sagemaker.ModelPackageSourceAlgorithmSpecificationArgs>;
/**
* The URI of the source for the model package.
*/
sourceUri?: pulumi.Input<string>;
/**
* An array of key-value pairs to apply to this resource.
*/
tags?: pulumi.Input<pulumi.Input<inputs.TagArgs>[]>;
/**
* The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.
*/
task?: pulumi.Input<string>;
/**
* Specifies batch transform jobs that SageMaker runs to validate your model package.
*/
validationSpecification?: pulumi.Input<inputs.sagemaker.ModelPackageValidationSpecificationArgs>;
}