type AlgorithmSortBy string

const (
	AlgorithmSortByName          AlgorithmSortBy = "Name"
	AlgorithmSortByCreation_time AlgorithmSortBy = "CreationTime"
)

type AlgorithmSpecification struct {

	// The input mode that the algorithm supports. For the input modes that Amazon
	// SageMaker algorithms support, see Algorithms
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html). If an algorithm
	// supports the File input mode, Amazon SageMaker downloads the training data from
	// S3 to the provisioned ML storage Volume, and mounts the directory to docker
	// volume for training container. If an algorithm supports the Pipe input mode,
	// Amazon SageMaker streams data directly from S3 to the container. In File mode,
	// make sure you provision ML storage volume with sufficient capacity to
	// accommodate the data download from S3. In addition to the training data, the ML
	// storage volume also stores the output model. The algorithm container use ML
	// storage volume to also store intermediate information, if any. For distributed
	// algorithms using File mode, training data is distributed uniformly, and your
	// training duration is predictable if the input data objects size is approximately
	// same. Amazon SageMaker does not split the files any further for model training.
	// If the object sizes are skewed, training won't be optimal as the data
	// distribution is also skewed where one host in a training cluster is overloaded,
	// thus becoming bottleneck in training.
	//
	// This member is required.
	TrainingInputMode TrainingInputMode

	// The name of the algorithm resource to use for the training job. This must be an
	// algorithm resource that you created or subscribe to on AWS Marketplace. If you
	// specify a value for this parameter, you can't specify a value for TrainingImage.
	AlgorithmName *string

	// To generate and save time-series metrics during training, set to true. The
	// default is false and time-series metrics aren't generated except in the
	// following cases:
	//
	//     * You use one of the Amazon SageMaker built-in
	// algorithms
	//
	//     * You use one of the following Prebuilt Amazon SageMaker Docker
	// Images
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/pre-built-containers-frameworks-deep-learning.html):
	//
	//
	// * Tensorflow (version >= 1.15)
	//
	//         * MXNet (version >= 1.6)
	//
	//         *
	// PyTorch (version >= 1.3)
	//
	//     * You specify at least one MetricDefinition
	EnableSageMakerMetricsTimeSeries *bool

	// A list of metric definition objects. Each object specifies the metric name and
	// regular expressions used to parse algorithm logs. Amazon SageMaker publishes
	// each metric to Amazon CloudWatch.
	MetricDefinitions []*MetricDefinition

	// The registry path of the Docker image that contains the training algorithm. For
	// information about docker registry paths for built-in algorithms, see Algorithms
	// Provided by Amazon SageMaker: Common Parameters
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html).
	// Amazon SageMaker supports both registry/repository[:tag] and
	// registry/repository[@digest] image path formats. For more information, see Using
	// Your Own Algorithms with Amazon SageMaker
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html).
	TrainingImage *string
}

type AlgorithmStatus string

const (
	AlgorithmStatusPending     AlgorithmStatus = "Pending"
	AlgorithmStatusIn_progress AlgorithmStatus = "InProgress"
	AlgorithmStatusCompleted   AlgorithmStatus = "Completed"
	AlgorithmStatusFailed      AlgorithmStatus = "Failed"
	AlgorithmStatusDeleting    AlgorithmStatus = "Deleting"
)

type AlgorithmStatusDetails struct {

	// The status of the scan of the algorithm's Docker image container.
	ImageScanStatuses []*AlgorithmStatusItem

	// The status of algorithm validation.
	ValidationStatuses []*AlgorithmStatusItem
}

type AlgorithmStatusItem struct {

	// The name of the algorithm for which the overall status is being reported.
	//
	// This member is required.
	Name *string

	// The current status.
	//
	// This member is required.
	Status DetailedAlgorithmStatus

	// if the overall status is Failed, the reason for the failure.
	FailureReason *string
}

type AlgorithmSummary struct {

	// The Amazon Resource Name (ARN) of the algorithm.
	//
	// This member is required.
	AlgorithmArn *string

	// The name of the algorithm that is described by the summary.
	//
	// This member is required.
	AlgorithmName *string

	// The overall status of the algorithm.
	//
	// This member is required.
	AlgorithmStatus AlgorithmStatus

	// A timestamp that shows when the algorithm was created.
	//
	// This member is required.
	CreationTime *time.Time

	// A brief description of the algorithm.
	AlgorithmDescription *string
}

type AlgorithmValidationProfile struct {

	// The name of the profile for the algorithm. The name must have 1 to 63
	// characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
	//
	// This member is required.
	ProfileName *string

	// The TrainingJobDefinition object that describes the training job that Amazon
	// SageMaker runs to validate your algorithm.
	//
	// This member is required.
	TrainingJobDefinition *TrainingJobDefinition

	// The TransformJobDefinition object that describes the transform job that Amazon
	// SageMaker runs to validate your algorithm.
	TransformJobDefinition *TransformJobDefinition
}

type AlgorithmValidationSpecification struct {

	// An array of AlgorithmValidationProfile objects, each of which specifies a
	// training job and batch transform job that Amazon SageMaker runs to validate your
	// algorithm.
	//
	// This member is required.
	ValidationProfiles []*AlgorithmValidationProfile

	// The IAM roles that Amazon SageMaker uses to run the training jobs.
	//
	// This member is required.
	ValidationRole *string
}

type AnnotationConsolidationConfig struct {

	// The Amazon Resource Name (ARN) of a Lambda function implements the logic for
	// annotation consolidation
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-annotation-consolidation.html)
	// and to process output data. This parameter is required for all labeling jobs.
	// For built-in task types
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html), use one
	// of the following Amazon SageMaker Ground Truth Lambda function ARNs for
	// AnnotationConsolidationLambdaArn. For custom labeling workflows, see
	// Post-annotation Lambda
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates-step3.html#sms-custom-templates-step3-postlambda).
	// Bounding box - Finds the most similar boxes from different workers based on the
	// Jaccard index of the boxes.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBoxarn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBoxarn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBoxarn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBoxarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBoxarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBoxarn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBoxarn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBoxarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBoxarn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBoxarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBoxarn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox
	//
	// Image
	// classification - Uses a variant of the Expectation Maximization approach to
	// estimate the true class of an image based on annotations from individual
	// workers.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassarn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassarn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassarn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassarn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassarn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassarn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassarn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass
	//
	// Multi-label
	// image classification - Uses a variant of the Expectation Maximization approach
	// to estimate the true classes of an image based on annotations from individual
	// workers.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabelarn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel
	//
	// Semantic
	// segmentation - Treats each pixel in an image as a multi-class classification and
	// treats pixel annotations from workers as "votes" for the correct label.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation
	//
	// Text
	// classification - Uses a variant of the Expectation Maximization approach to
	// estimate the true class of text based on annotations from individual workers.
	//
	//
	// *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassarn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassarn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassarn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassarn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassarn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassarn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassarn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass
	//
	// Multi-label
	// text classification - Uses a variant of the Expectation Maximization approach to
	// estimate the true classes of text based on annotations from individual
	// workers.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabelarn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel
	//
	// Named
	// entity recognition - Groups similar selections and calculates aggregate
	// boundaries, resolving to most-assigned label.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognitionarn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognitionarn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognitionarn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognitionarn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognitionarn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognitionarn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition
	//
	// Named
	// entity recognition - Groups similar selections and calculates aggregate
	// boundaries, resolving to most-assigned label.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognitionarn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognitionarn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognitionarn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognitionarn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognitionarn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognitionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognitionarn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition
	//
	// Video
	// Classification - Use this task type when you need workers to classify videos
	// using predefined labels that you specify. Workers are shown videos and are asked
	// to choose one label for each video.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClassarn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClassarn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClassarn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClassarn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClassarn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClassarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClassarn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClassarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClassarn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClass
	//
	// Video
	// Frame Object Detection - Use this task type to have workers identify and locate
	// objects in a sequence of video frames (images extracted from a video) using
	// bounding boxes. For example, you can use this task to ask workers to identify
	// and localize various objects in a series of video frames, such as cars, bikes,
	// and pedestrians.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetection
	//
	// Video
	// Frame Object Tracking - Use this task type to have workers track the movement of
	// objects in a sequence of video frames (images extracted from a video) using
	// bounding boxes. For example, you can use this task to ask workers to track the
	// movement of objects, such as cars, bikes, and pedestrians.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking
	//
	// 3D
	// point cloud object detection - Use this task type when you want workers to
	// classify objects in a 3D point cloud by drawing 3D cuboids around objects. For
	// example, you can use this task type to ask workers to identify different types
	// of objects in a point cloud, such as cars, bikes, and pedestrians.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection
	//
	// 3D
	// point cloud object tracking - Use this task type when you want workers to draw
	// 3D cuboids around objects that appear in a sequence of 3D point cloud frames.
	// For example, you can use this task type to ask workers to track the movement of
	// vehicles across multiple point cloud frames.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking
	//
	// 3D
	// point cloud semantic segmentation - Use this task type when you want workers to
	// create a point-level semantic segmentation masks by painting objects in a 3D
	// point cloud using different colors where each color is assigned to one of the
	// classes you specify.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation
	//
	// Use
	// the following ARNs for Label Verification and Adjustment Jobs Use label
	// verification and adjustment jobs to review and adjust labels. To learn more, see
	// Verify and Adjust Labels
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html).
	// Semantic segmentation adjustment - Treats each pixel in an image as a
	// multi-class classification and treats pixel adjusted annotations from workers as
	// "votes" for the correct label.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation
	//
	// Semantic
	// segmentation verification - Uses a variant of the Expectation Maximization
	// approach to estimate the true class of verification judgment for semantic
	// segmentation labels based on annotations from individual workers.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation
	//
	// Bounding
	// box verification - Uses a variant of the Expectation Maximization approach to
	// estimate the true class of verification judgement for bounding box labels based
	// on annotations from individual workers.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBoxarn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBoxarn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBoxarn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBoxarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBoxarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBoxarn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBoxarn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBoxarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBoxarn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBoxarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBoxarn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox
	//
	// Bounding
	// box adjustment - Finds the most similar boxes from different workers based on
	// the Jaccard index of the adjusted annotations.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBoxarn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox
	//
	// Video
	// Frame Object Detection Adjustment - Use this task type when you want workers to
	// adjust bounding boxes that workers have added to video frames to classify and
	// localize objects in a sequence of video frames.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectDetection
	//
	// Video
	// Frame Object Tracking Adjustment - Use this task type when you want workers to
	// adjust bounding boxes that workers have added to video frames to track object
	// movement across a sequence of video frames.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectTracking
	//
	// 3D
	// point cloud object detection adjustment - Use this task type when you want
	// workers to adjust 3D cuboids around objects in a 3D point cloud.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetectionarn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection
	//
	// 3D
	// point cloud object tracking adjustment - Use this task type when you want
	// workers to adjust 3D cuboids around objects that appear in a sequence of 3D
	// point cloud frames.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTrackingarn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking
	//
	// 3D
	// point cloud semantic segmentation adjustment - Use this task type when you want
	// workers to adjust a point-level semantic segmentation masks using a paint
	// tool.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentationarn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//
	// This member is required.
	AnnotationConsolidationLambdaArn *string
}

type AppDetails struct {

	// The name of the app.
	AppName *string

	// The type of app.
	AppType AppType

	// The creation time.
	CreationTime *time.Time

	// The domain ID.
	DomainId *string

	// The status.
	Status AppStatus

	// The user profile name.
	UserProfileName *string
}

type AppInstanceType string

const (
	AppInstanceTypeSystem           AppInstanceType = "system"
	AppInstanceTypeMl_t3_micro      AppInstanceType = "ml.t3.micro"
	AppInstanceTypeMl_t3_small      AppInstanceType = "ml.t3.small"
	AppInstanceTypeMl_t3_medium     AppInstanceType = "ml.t3.medium"
	AppInstanceTypeMl_t3_large      AppInstanceType = "ml.t3.large"
	AppInstanceTypeMl_t3_xlarge     AppInstanceType = "ml.t3.xlarge"
	AppInstanceTypeMl_t3_2xlarge    AppInstanceType = "ml.t3.2xlarge"
	AppInstanceTypeMl_m5_large      AppInstanceType = "ml.m5.large"
	AppInstanceTypeMl_m5_xlarge     AppInstanceType = "ml.m5.xlarge"
	AppInstanceTypeMl_m5_2xlarge    AppInstanceType = "ml.m5.2xlarge"
	AppInstanceTypeMl_m5_4xlarge    AppInstanceType = "ml.m5.4xlarge"
	AppInstanceTypeMl_m5_8xlarge    AppInstanceType = "ml.m5.8xlarge"
	AppInstanceTypeMl_m5_12xlarge   AppInstanceType = "ml.m5.12xlarge"
	AppInstanceTypeMl_m5_16xlarge   AppInstanceType = "ml.m5.16xlarge"
	AppInstanceTypeMl_m5_24xlarge   AppInstanceType = "ml.m5.24xlarge"
	AppInstanceTypeMl_c5_large      AppInstanceType = "ml.c5.large"
	AppInstanceTypeMl_c5_xlarge     AppInstanceType = "ml.c5.xlarge"
	AppInstanceTypeMl_c5_2xlarge    AppInstanceType = "ml.c5.2xlarge"
	AppInstanceTypeMl_c5_4xlarge    AppInstanceType = "ml.c5.4xlarge"
	AppInstanceTypeMl_c5_9xlarge    AppInstanceType = "ml.c5.9xlarge"
	AppInstanceTypeMl_c5_12xlarge   AppInstanceType = "ml.c5.12xlarge"
	AppInstanceTypeMl_c5_18xlarge   AppInstanceType = "ml.c5.18xlarge"
	AppInstanceTypeMl_c5_24xlarge   AppInstanceType = "ml.c5.24xlarge"
	AppInstanceTypeMl_p3_2xlarge    AppInstanceType = "ml.p3.2xlarge"
	AppInstanceTypeMl_p3_8xlarge    AppInstanceType = "ml.p3.8xlarge"
	AppInstanceTypeMl_p3_16xlarge   AppInstanceType = "ml.p3.16xlarge"
	AppInstanceTypeMl_g4dn_xlarge   AppInstanceType = "ml.g4dn.xlarge"
	AppInstanceTypeMl_g4dn_2xlarge  AppInstanceType = "ml.g4dn.2xlarge"
	AppInstanceTypeMl_g4dn_4xlarge  AppInstanceType = "ml.g4dn.4xlarge"
	AppInstanceTypeMl_g4dn_8xlarge  AppInstanceType = "ml.g4dn.8xlarge"
	AppInstanceTypeMl_g4dn_12xlarge AppInstanceType = "ml.g4dn.12xlarge"
	AppInstanceTypeMl_g4dn_16xlarge AppInstanceType = "ml.g4dn.16xlarge"
)

type AppSortKey string

const (
	AppSortKeyCreationtime AppSortKey = "CreationTime"
)

type AppSpecification struct {

	// The container image to be run by the processing job.
	//
	// This member is required.
	ImageUri *string

	// The arguments for a container used to run a processing job.
	ContainerArguments []*string

	// The entrypoint for a container used to run a processing job.
	ContainerEntrypoint []*string
}

type AppStatus string

const (
	AppStatusDeleted   AppStatus = "Deleted"
	AppStatusDeleting  AppStatus = "Deleting"
	AppStatusFailed    AppStatus = "Failed"
	AppStatusInservice AppStatus = "InService"
	AppStatusPending   AppStatus = "Pending"
)

type AppType string

const (
	AppTypeJupyterserver AppType = "JupyterServer"
	AppTypeKernelgateway AppType = "KernelGateway"
	AppTypeTensorboard   AppType = "TensorBoard"
)

type AssemblyType string

const (
	AssemblyTypeNone AssemblyType = "None"
	AssemblyTypeLine AssemblyType = "Line"
)

type AuthMode string

const (
	AuthModeSso AuthMode = "SSO"
	AuthModeIam AuthMode = "IAM"
)

type AutoMLCandidate struct {

	// The candidate name.
	//
	// This member is required.
	CandidateName *string

	// The candidate's status.
	//
	// This member is required.
	CandidateStatus CandidateStatus

	// The candidate's steps.
	//
	// This member is required.
	CandidateSteps []*AutoMLCandidateStep

	// The creation time.
	//
	// This member is required.
	CreationTime *time.Time

	// The last modified time.
	//
	// This member is required.
	LastModifiedTime *time.Time

	// The objective status.
	//
	// This member is required.
	ObjectiveStatus ObjectiveStatus

	// The end time.
	EndTime *time.Time

	// The failure reason.
	FailureReason *string

	// The candidate result from a job.
	FinalAutoMLJobObjectiveMetric *FinalAutoMLJobObjectiveMetric

	// The inference containers.
	InferenceContainers []*AutoMLContainerDefinition
}

type AutoMLCandidateStep struct {

	// The ARN for the Candidate's step.
	//
	// This member is required.
	CandidateStepArn *string

	// The name for the Candidate's step.
	//
	// This member is required.
	CandidateStepName *string

	// Whether the Candidate is at the transform, training, or processing step.
	//
	// This member is required.
	CandidateStepType CandidateStepType
}

type AutoMLChannel struct {

	// The data source.
	//
	// This member is required.
	DataSource *AutoMLDataSource

	// The name of the target variable in supervised learning, a.k.a. 'y'.
	//
	// This member is required.
	TargetAttributeName *string

	// You can use Gzip or None. The default value is None.
	CompressionType CompressionType
}

type AutoMLContainerDefinition struct {

	// The ECR path of the container. Refer to ContainerDefinition for more details.
	//
	// This member is required.
	Image *string

	// The location of the model artifacts. Refer to ContainerDefinition for more
	// details.
	//
	// This member is required.
	ModelDataUrl *string

	// Environment variables to set in the container. Refer to ContainerDefinition for
	// more details.
	Environment map[string]*string
}

type AutoMLDataSource struct {

	// The Amazon S3 location of the input data. The input data must be in CSV format
	// and contain at least 1000 rows.
	//
	// This member is required.
	S3DataSource *AutoMLS3DataSource
}

type AutoMLJobArtifacts struct {

	// The URL to the notebook location.
	CandidateDefinitionNotebookLocation *string

	// The URL to the notebook location.
	DataExplorationNotebookLocation *string
}

type AutoMLJobCompletionCriteria struct {

	// The maximum time, in seconds, an AutoML job is allowed to wait for a trial to
	// complete. It must be equal to or greater than MaxRuntimePerTrainingJobInSeconds.
	MaxAutoMLJobRuntimeInSeconds *int32

	// The maximum number of times a training job is allowed to run.
	MaxCandidates *int32

	// The maximum time, in seconds, a job is allowed to run.
	MaxRuntimePerTrainingJobInSeconds *int32
}

type AutoMLJobConfig struct {

	// How long a job is allowed to run, or how many candidates a job is allowed to
	// generate.
	CompletionCriteria *AutoMLJobCompletionCriteria

	// Security configuration for traffic encryption or Amazon VPC settings.
	SecurityConfig *AutoMLSecurityConfig
}

type AutoMLJobObjective struct {

	// The name of the metric.
	//
	// This member is required.
	MetricName AutoMLMetricEnum
}

type AutoMLJobObjectiveType string

const (
	AutoMLJobObjectiveTypeMaximize AutoMLJobObjectiveType = "Maximize"
	AutoMLJobObjectiveTypeMinimize AutoMLJobObjectiveType = "Minimize"
)

type AutoMLJobSecondaryStatus string

const (
	AutoMLJobSecondaryStatusStarting                        AutoMLJobSecondaryStatus = "Starting"
	AutoMLJobSecondaryStatusAnalyzing_data                  AutoMLJobSecondaryStatus = "AnalyzingData"
	AutoMLJobSecondaryStatusFeature_engineering             AutoMLJobSecondaryStatus = "FeatureEngineering"
	AutoMLJobSecondaryStatusModel_tuning                    AutoMLJobSecondaryStatus = "ModelTuning"
	AutoMLJobSecondaryStatusMax_candidates_reached          AutoMLJobSecondaryStatus = "MaxCandidatesReached"
	AutoMLJobSecondaryStatusFailed                          AutoMLJobSecondaryStatus = "Failed"
	AutoMLJobSecondaryStatusStopped                         AutoMLJobSecondaryStatus = "Stopped"
	AutoMLJobSecondaryStatusMax_auto_ml_job_runtime_reached AutoMLJobSecondaryStatus = "MaxAutoMLJobRuntimeReached"
	AutoMLJobSecondaryStatusStopping                        AutoMLJobSecondaryStatus = "Stopping"
	AutoMLJobSecondaryStatusCandidate_definitions_generated AutoMLJobSecondaryStatus = "CandidateDefinitionsGenerated"
)

type AutoMLJobStatus string

const (
	AutoMLJobStatusCompleted   AutoMLJobStatus = "Completed"
	AutoMLJobStatusIn_progress AutoMLJobStatus = "InProgress"
	AutoMLJobStatusFailed      AutoMLJobStatus = "Failed"
	AutoMLJobStatusStopped     AutoMLJobStatus = "Stopped"
	AutoMLJobStatusStopping    AutoMLJobStatus = "Stopping"
)

type AutoMLJobSummary struct {

	// The ARN of the job.
	//
	// This member is required.
	AutoMLJobArn *string

	// The name of the object you are requesting.
	//
	// This member is required.
	AutoMLJobName *string

	// The job's secondary status.
	//
	// This member is required.
	AutoMLJobSecondaryStatus AutoMLJobSecondaryStatus

	// The job's status.
	//
	// This member is required.
	AutoMLJobStatus AutoMLJobStatus

	// When the job was created.
	//
	// This member is required.
	CreationTime *time.Time

	// When the job was last modified.
	//
	// This member is required.
	LastModifiedTime *time.Time

	// The end time.
	EndTime *time.Time

	// The failure reason.
	FailureReason *string
}

type AutoMLMetricEnum string

const (
	AutoMLMetricEnumAccuracy AutoMLMetricEnum = "Accuracy"
	AutoMLMetricEnumMse      AutoMLMetricEnum = "MSE"
	AutoMLMetricEnumF1       AutoMLMetricEnum = "F1"
	AutoMLMetricEnumF1_macro AutoMLMetricEnum = "F1macro"
)

type AutoMLOutputDataConfig struct {

	// The Amazon S3 output path. Must be 128 characters or less.
	//
	// This member is required.
	S3OutputPath *string

	// The AWS KMS encryption key ID.
	KmsKeyId *string
}

type AutoMLS3DataSource struct {

	// The data type.
	//
	// This member is required.
	S3DataType AutoMLS3DataType

	// The URL to the Amazon S3 data source.
	//
	// This member is required.
	S3Uri *string
}

type AutoMLS3DataType string

const (
	AutoMLS3DataTypeManifest_file AutoMLS3DataType = "ManifestFile"
	AutoMLS3DataTypeS3_prefix     AutoMLS3DataType = "S3Prefix"
)

type AutoMLSecurityConfig struct {

	// Whether to use traffic encryption between the container layers.
	EnableInterContainerTrafficEncryption *bool

	// The key used to encrypt stored data.
	VolumeKmsKeyId *string

	// VPC configuration.
	VpcConfig *VpcConfig
}

type AutoMLSortBy string

const (
	AutoMLSortByName          AutoMLSortBy = "Name"
	AutoMLSortByCreation_time AutoMLSortBy = "CreationTime"
	AutoMLSortByStatus        AutoMLSortBy = "Status"
)

type AutoMLSortOrder string

const (
	AutoMLSortOrderAscending  AutoMLSortOrder = "Ascending"
	AutoMLSortOrderDescending AutoMLSortOrder = "Descending"
)

type AwsManagedHumanLoopRequestSource string

const (
	AwsManagedHumanLoopRequestSourceRekognition_detect_moderation_labels_image_v3 AwsManagedHumanLoopRequestSource = "AWS/Rekognition/DetectModerationLabels/Image/V3"
	AwsManagedHumanLoopRequestSourceTextract_analyze_document_forms_v1            AwsManagedHumanLoopRequestSource = "AWS/Textract/AnalyzeDocument/Forms/V1"
)

type BatchStrategy string

const (
	BatchStrategyMulti_record  BatchStrategy = "MultiRecord"
	BatchStrategySingle_record BatchStrategy = "SingleRecord"
)

type BooleanOperator string

const (
	BooleanOperatorAnd BooleanOperator = "And"
	BooleanOperatorOr  BooleanOperator = "Or"
)

type CandidateSortBy string

const (
	CandidateSortByCreationtime              CandidateSortBy = "CreationTime"
	CandidateSortByStatus                    CandidateSortBy = "Status"
	CandidateSortByFinalobjectivemetricvalue CandidateSortBy = "FinalObjectiveMetricValue"
)

type CandidateStatus string

const (
	CandidateStatusCompleted   CandidateStatus = "Completed"
	CandidateStatusIn_progress CandidateStatus = "InProgress"
	CandidateStatusFailed      CandidateStatus = "Failed"
	CandidateStatusStopped     CandidateStatus = "Stopped"
	CandidateStatusStopping    CandidateStatus = "Stopping"
)

type CandidateStepType string

const (
	CandidateStepTypeTraining   CandidateStepType = "AWS::SageMaker::TrainingJob"
	CandidateStepTypeTransform  CandidateStepType = "AWS::SageMaker::TransformJob"
	CandidateStepTypeProcessing CandidateStepType = "AWS::SageMaker::ProcessingJob"
)

type CaptureContentTypeHeader struct {

	//
	CsvContentTypes []*string

	//
	JsonContentTypes []*string
}

type CaptureMode string

const (
	CaptureModeInput  CaptureMode = "Input"
	CaptureModeOutput CaptureMode = "Output"
)

type CaptureOption struct {

	//
	//
	// This member is required.
	CaptureMode CaptureMode
}

type CaptureStatus string

const (
	CaptureStatusStarted CaptureStatus = "Started"
	CaptureStatusStopped CaptureStatus = "Stopped"
)

type CategoricalParameterRange struct {

	// The name of the categorical hyperparameter to tune.
	//
	// This member is required.
	Name *string

	// A list of the categories for the hyperparameter.
	//
	// This member is required.
	Values []*string
}

type CategoricalParameterRangeSpecification struct {

	// The allowed categories for the hyperparameter.
	//
	// This member is required.
	Values []*string
}

type Channel struct {

	// The name of the channel.
	//
	// This member is required.
	ChannelName *string

	// The location of the channel data.
	//
	// This member is required.
	DataSource *DataSource

	// If training data is compressed, the compression type. The default value is None.
	// CompressionType is used only in Pipe input mode. In File mode, leave this field
	// unset or set it to None.
	CompressionType CompressionType

	// The MIME type of the data.
	ContentType *string

	// (Optional) The input mode to use for the data channel in a training job. If you
	// don't set a value for InputMode, Amazon SageMaker uses the value set for
	// TrainingInputMode. Use this parameter to override the TrainingInputMode setting
	// in a AlgorithmSpecification request when you have a channel that needs a
	// different input mode from the training job's general setting. To download the
	// data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML
	// storage volume, and mount the directory to a Docker volume, use File input mode.
	// To stream data directly from Amazon S3 to the container, choose Pipe input mode.
	// To use a model for incremental training, choose File input model.
	InputMode TrainingInputMode

	// Specify RecordIO as the value when input data is in raw format but the training
	// algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps
	// each individual S3 object in a RecordIO record. If the input data is already in
	// RecordIO format, you don't need to set this attribute. For more information, see
	// Create a Dataset Using RecordIO
	// (https://mxnet.apache.org/api/architecture/note_data_loading#data-format). In
	// File mode, leave this field unset or set it to None.
	RecordWrapperType RecordWrapper

	// A configuration for a shuffle option for input data in a channel. If you use
	// S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches.
	// If you use ManifestFile, the order of the S3 object references in the
	// ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the
	// JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is
	// determined using the Seed value. For Pipe input mode, shuffling is done at the
	// start of every epoch. With large datasets this ensures that the order of the
	// training data is different for each epoch, it helps reduce bias and possible
	// overfitting. In a multi-node training job when ShuffleConfig is combined with
	// S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so
	// that the content sent to a particular node on the first epoch might be sent to a
	// different node on the second epoch.
	ShuffleConfig *ShuffleConfig
}

type ChannelSpecification struct {

	// The name of the channel.
	//
	// This member is required.
	Name *string

	// The supported MIME types for the data.
	//
	// This member is required.
	SupportedContentTypes []*string

	// The allowed input mode, either FILE or PIPE. In FILE mode, Amazon SageMaker
	// copies the data from the input source onto the local Amazon Elastic Block Store
	// (Amazon EBS) volumes before starting your training algorithm. This is the most
	// commonly used input mode. In PIPE mode, Amazon SageMaker streams input data from
	// the source directly to your algorithm without using the EBS volume.
	//
	// This member is required.
	SupportedInputModes []TrainingInputMode

	// A brief description of the channel.
	Description *string

	// Indicates whether the channel is required by the algorithm.
	IsRequired *bool

	// The allowed compression types, if data compression is used.
	SupportedCompressionTypes []CompressionType
}

type CheckpointConfig struct {

	// Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For
	// example, s3://bucket-name/key-name-prefix.
	//
	// This member is required.
	S3Uri *string

	// (Optional) The local directory where checkpoints are written. The default
	// directory is /opt/ml/checkpoints/.
	LocalPath *string
}

type CodeRepositorySortBy string

const (
	CodeRepositorySortByName               CodeRepositorySortBy = "Name"
	CodeRepositorySortByCreation_time      CodeRepositorySortBy = "CreationTime"
	CodeRepositorySortByLast_modified_time CodeRepositorySortBy = "LastModifiedTime"
)

type CodeRepositorySortOrder string

const (
	CodeRepositorySortOrderAscending  CodeRepositorySortOrder = "Ascending"
	CodeRepositorySortOrderDescending CodeRepositorySortOrder = "Descending"
)

type CodeRepositorySummary struct {

	// The Amazon Resource Name (ARN) of the Git repository.
	//
	// This member is required.
	CodeRepositoryArn *string

	// The name of the Git repository.
	//
	// This member is required.
	CodeRepositoryName *string

	// The date and time that the Git repository was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The date and time that the Git repository was last modified.
	//
	// This member is required.
	LastModifiedTime *time.Time

	// Configuration details for the Git repository, including the URL where it is
	// located and the ARN of the AWS Secrets Manager secret that contains the
	// credentials used to access the repository.
	GitConfig *GitConfig
}

type CognitoConfig struct {

	// The client ID for your Amazon Cognito user pool.
	//
	// This member is required.
	ClientId *string

	// A  user pool
	// (https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html)
	// is a user directory in Amazon Cognito. With a user pool, your users can sign in
	// to your web or mobile app through Amazon Cognito. Your users can also sign in
	// through social identity providers like Google, Facebook, Amazon, or Apple, and
	// through SAML identity providers.
	//
	// This member is required.
	UserPool *string
}

type CognitoMemberDefinition struct {

	// An identifier for an application client. You must create the app client ID using
	// Amazon Cognito.
	//
	// This member is required.
	ClientId *string

	// An identifier for a user group.
	//
	// This member is required.
	UserGroup *string

	// An identifier for a user pool. The user pool must be in the same region as the
	// service that you are calling.
	//
	// This member is required.
	UserPool *string
}

type CollectionConfiguration struct {

	// The name of the tensor collection. The name must be unique relative to other
	// rule configuration names.
	CollectionName *string

	// Parameter values for the tensor collection. The allowed parameters are "name",
	// "include_regex", "reduction_config", "save_config", "tensor_names", and
	// "save_histogram".
	CollectionParameters map[string]*string
}

type CompilationJobStatus string

const (
	CompilationJobStatusInprogress CompilationJobStatus = "INPROGRESS"
	CompilationJobStatusCompleted  CompilationJobStatus = "COMPLETED"
	CompilationJobStatusFailed     CompilationJobStatus = "FAILED"
	CompilationJobStatusStarting   CompilationJobStatus = "STARTING"
	CompilationJobStatusStopping   CompilationJobStatus = "STOPPING"
	CompilationJobStatusStopped    CompilationJobStatus = "STOPPED"
)

type CompilationJobSummary struct {

	// The Amazon Resource Name (ARN) of the model compilation job.
	//
	// This member is required.
	CompilationJobArn *string

	// The name of the model compilation job that you want a summary for.
	//
	// This member is required.
	CompilationJobName *string

	// The status of the model compilation job.
	//
	// This member is required.
	CompilationJobStatus CompilationJobStatus

	// The time when the model compilation job was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The time when the model compilation job completed.
	CompilationEndTime *time.Time

	// The time when the model compilation job started.
	CompilationStartTime *time.Time

	// The type of device that the model will run on after the compilation job has
	// completed.
	CompilationTargetDevice TargetDevice

	// The type of accelerator that the model will run on after the compilation job has
	// completed.
	CompilationTargetPlatformAccelerator TargetPlatformAccelerator

	// The type of architecture that the model will run on after the compilation job
	// has completed.
	CompilationTargetPlatformArch TargetPlatformArch

	// The type of OS that the model will run on after the compilation job has
	// completed.
	CompilationTargetPlatformOs TargetPlatformOs

	// The time when the model compilation job was last modified.
	LastModifiedTime *time.Time
}

type CompressionType string

const (
	CompressionTypeNone CompressionType = "None"
	CompressionTypeGzip CompressionType = "Gzip"
)

type ConflictException struct {
	Message *string
}

type ContainerDefinition struct {

	// This parameter is ignored for models that contain only a PrimaryContainer. When
	// a ContainerDefinition is part of an inference pipeline, the value of the
	// parameter uniquely identifies the container for the purposes of logging and
	// metrics. For information, see Use Logs and Metrics to Monitor an Inference
	// Pipeline
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-logs-metrics.html).
	// If you don't specify a value for this parameter for a ContainerDefinition that
	// is part of an inference pipeline, a unique name is automatically assigned based
	// on the position of the ContainerDefinition in the pipeline. If you specify a
	// value for the ContainerHostName for any ContainerDefinition that is part of an
	// inference pipeline, you must specify a value for the ContainerHostName parameter
	// of every ContainerDefinition in that pipeline.
	ContainerHostname *string

	// The environment variables to set in the Docker container. Each key and value in
	// the Environment string to string map can have length of up to 1024. We support
	// up to 16 entries in the map.
	Environment map[string]*string

	// The Amazon EC2 Container Registry (Amazon ECR) path where inference code is
	// stored. If you are using your own custom algorithm instead of an algorithm
	// provided by Amazon SageMaker, the inference code must meet Amazon SageMaker
	// requirements. Amazon SageMaker supports both registry/repository[:tag] and
	// registry/repository[@digest] image path formats. For more information, see Using
	// Your Own Algorithms with Amazon SageMaker
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html)
	Image *string

	// Whether the container hosts a single model or multiple models.
	Mode ContainerMode

	// The S3 path where the model artifacts, which result from model training, are
	// stored. This path must point to a single gzip compressed tar archive (.tar.gz
	// suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but
	// not if you use your own algorithms. For more information on built-in algorithms,
	// see Common Parameters
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html).
	// If you provide a value for this parameter, Amazon SageMaker uses AWS Security
	// Token Service to download model artifacts from the S3 path you provide. AWS STS
	// is activated in your IAM user account by default. If you previously deactivated
	// AWS STS for a region, you need to reactivate AWS STS for that region. For more
	// information, see Activating and Deactivating AWS STS in an AWS Region
	// (https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html)
	// in the AWS Identity and Access Management User Guide. If you use a built-in
	// algorithm to create a model, Amazon SageMaker requires that you provide a S3
	// path to the model artifacts in ModelDataUrl.
	ModelDataUrl *string

	// The name or Amazon Resource Name (ARN) of the model package to use to create the
	// model.
	ModelPackageName *string
}

type ContainerMode string

const (
	ContainerModeSingle_model ContainerMode = "SingleModel"
	ContainerModeMulti_model  ContainerMode = "MultiModel"
)

type ContentClassifier string

const (
	ContentClassifierFree_of_personally_identifiable_information ContentClassifier = "FreeOfPersonallyIdentifiableInformation"
	ContentClassifierFree_of_adult_content                       ContentClassifier = "FreeOfAdultContent"
)

type ContinuousParameterRange struct {

	// The maximum value for the hyperparameter. The tuning job uses floating-point
	// values between MinValue value and this value for tuning.
	//
	// This member is required.
	MaxValue *string

	// The minimum value for the hyperparameter. The tuning job uses floating-point
	// values between this value and MaxValuefor tuning.
	//
	// This member is required.
	MinValue *string

	// The name of the continuous hyperparameter to tune.
	//
	// This member is required.
	Name *string

	// The scale that hyperparameter tuning uses to search the hyperparameter range.
	// For information about choosing a hyperparameter scale, see Hyperparameter
	// Scaling
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type).
	// One of the following values: Auto Amazon SageMaker hyperparameter tuning chooses
	// the best scale for the hyperparameter. Linear Hyperparameter tuning searches the
	// values in the hyperparameter range by using a linear scale. Logarithmic
	// Hyperparameter tuning searches the values in the hyperparameter range by using a
	// logarithmic scale. Logarithmic scaling works only for ranges that have only
	// values greater than 0. ReverseLogarithmic Hyperparameter tuning searches the
	// values in the hyperparameter range by using a reverse logarithmic scale. Reverse
	// logarithmic scaling works only for ranges that are entirely within the range
	// 0<=x<1.0.
	ScalingType HyperParameterScalingType
}

type ContinuousParameterRangeSpecification struct {

	// The maximum floating-point value allowed.
	//
	// This member is required.
	MaxValue *string

	// The minimum floating-point value allowed.
	//
	// This member is required.
	MinValue *string
}

type DataCaptureConfig struct {

	//
	//
	// This member is required.
	CaptureOptions []*CaptureOption

	//
	//
	// This member is required.
	DestinationS3Uri *string

	//
	//
	// This member is required.
	InitialSamplingPercentage *int32

	//
	CaptureContentTypeHeader *CaptureContentTypeHeader

	//
	EnableCapture *bool

	//
	KmsKeyId *string
}

type DataCaptureConfigSummary struct {

	//
	//
	// This member is required.
	CaptureStatus CaptureStatus

	//
	//
	// This member is required.
	CurrentSamplingPercentage *int32

	//
	//
	// This member is required.
	DestinationS3Uri *string

	//
	//
	// This member is required.
	EnableCapture *bool

	//
	//
	// This member is required.
	KmsKeyId *string
}

type DataProcessing struct {

	// A JSONPath
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#data-processing-operators)
	// expression used to select a portion of the input data to pass to the algorithm.
	// Use the InputFilter parameter to exclude fields, such as an ID column, from the
	// input. If you want Amazon SageMaker to pass the entire input dataset to the
	// algorithm, accept the default value $. Examples: "$", "$[1:]", "$.features"
	InputFilter *string

	// Specifies the source of the data to join with the transformed data. The valid
	// values are None and Input. The default value is None, which specifies not to
	// join the input with the transformed data. If you want the batch transform job to
	// join the original input data with the transformed data, set JoinSource to Input.
	// For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the
	// transformed data to the input JSON object in an attribute called
	// SageMakerOutput. The joined result for JSON must be a key-value pair object. If
	// the input is not a key-value pair object, Amazon SageMaker creates a new JSON
	// file. In the new JSON file, and the input data is stored under the
	// SageMakerInput key and the results are stored in SageMakerOutput. For CSV files,
	// Amazon SageMaker combines the transformed data with the input data at the end of
	// the input data and stores it in the output file. The joined data has the joined
	// input data followed by the transformed data and the output is a CSV file.
	JoinSource JoinSource

	// A JSONPath
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#data-processing-operators)
	// expression used to select a portion of the joined dataset to save in the output
	// file for a batch transform job. If you want Amazon SageMaker to store the entire
	// input dataset in the output file, leave the default value, $. If you specify
	// indexes that aren't within the dimension size of the joined dataset, you get an
	// error. Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
	OutputFilter *string
}

type DataSource struct {

	// The file system that is associated with a channel.
	FileSystemDataSource *FileSystemDataSource

	// The S3 location of the data source that is associated with a channel.
	S3DataSource *S3DataSource
}

type DebugHookConfig struct {

	// Path to Amazon S3 storage location for tensors.
	//
	// This member is required.
	S3OutputPath *string

	// Configuration information for tensor collections.
	CollectionConfigurations []*CollectionConfiguration

	// Configuration information for the debug hook parameters.
	HookParameters map[string]*string

	// Path to local storage location for tensors. Defaults to /opt/ml/output/tensors/.
	LocalPath *string
}

type DebugRuleConfiguration struct {

	// The name of the rule configuration. It must be unique relative to other rule
	// configuration names.
	//
	// This member is required.
	RuleConfigurationName *string

	// The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
	//
	// This member is required.
	RuleEvaluatorImage *string

	// The instance type to deploy for a training job.
	InstanceType ProcessingInstanceType

	// Path to local storage location for output of rules. Defaults to
	// /opt/ml/processing/output/rule/.
	LocalPath *string

	// Runtime configuration for rule container.
	RuleParameters map[string]*string

	// Path to Amazon S3 storage location for rules.
	S3OutputPath *string

	// The size, in GB, of the ML storage volume attached to the processing instance.
	VolumeSizeInGB *int32
}

type DebugRuleEvaluationStatus struct {

	// Timestamp when the rule evaluation status was last modified.
	LastModifiedTime *time.Time

	// The name of the rule configuration
	RuleConfigurationName *string

	// The Amazon Resource Name (ARN) of the rule evaluation job.
	RuleEvaluationJobArn *string

	// Status of the rule evaluation.
	RuleEvaluationStatus RuleEvaluationStatus

	// Details from the rule evaluation.
	StatusDetails *string
}

type DeployedImage struct {

	// The date and time when the image path for the model resolved to the
	// ResolvedImage
	ResolutionTime *time.Time

	// The specific digest path of the image hosted in this ProductionVariant.
	ResolvedImage *string

	// The image path you specified when you created the model.
	SpecifiedImage *string
}

type DesiredWeightAndCapacity struct {

	// The name of the variant to update.
	//
	// This member is required.
	VariantName *string

	// The variant's capacity.
	DesiredInstanceCount *int32

	// The variant's weight.
	DesiredWeight *float32
}

type DetailedAlgorithmStatus string

const (
	DetailedAlgorithmStatusNot_started DetailedAlgorithmStatus = "NotStarted"
	DetailedAlgorithmStatusIn_progress DetailedAlgorithmStatus = "InProgress"
	DetailedAlgorithmStatusCompleted   DetailedAlgorithmStatus = "Completed"
	DetailedAlgorithmStatusFailed      DetailedAlgorithmStatus = "Failed"
)

type DetailedModelPackageStatus string

const (
	DetailedModelPackageStatusNot_started DetailedModelPackageStatus = "NotStarted"
	DetailedModelPackageStatusIn_progress DetailedModelPackageStatus = "InProgress"
	DetailedModelPackageStatusCompleted   DetailedModelPackageStatus = "Completed"
	DetailedModelPackageStatusFailed      DetailedModelPackageStatus = "Failed"
)

type DirectInternetAccess string

const (
	DirectInternetAccessEnabled  DirectInternetAccess = "Enabled"
	DirectInternetAccessDisabled DirectInternetAccess = "Disabled"
)

type DomainDetails struct {

	// The creation time.
	CreationTime *time.Time

	// The domain's Amazon Resource Name (ARN).
	DomainArn *string

	// The domain ID.
	DomainId *string

	// The domain name.
	DomainName *string

	// The last modified time.
	LastModifiedTime *time.Time

	// The status.
	Status DomainStatus

	// The domain's URL.
	Url *string
}

type DomainStatus string

const (
	DomainStatusDeleting  DomainStatus = "Deleting"
	DomainStatusFailed    DomainStatus = "Failed"
	DomainStatusInservice DomainStatus = "InService"
	DomainStatusPending   DomainStatus = "Pending"
)

type EndpointConfigSortKey string

const (
	EndpointConfigSortKeyName         EndpointConfigSortKey = "Name"
	EndpointConfigSortKeyCreationtime EndpointConfigSortKey = "CreationTime"
)

type EndpointConfigSummary struct {

	// A timestamp that shows when the endpoint configuration was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the endpoint configuration.
	//
	// This member is required.
	EndpointConfigArn *string

	// The name of the endpoint configuration.
	//
	// This member is required.
	EndpointConfigName *string
}

type EndpointInput struct {

	// An endpoint in customer's account which has enabled DataCaptureConfig enabled.
	//
	// This member is required.
	EndpointName *string

	// Path to the filesystem where the endpoint data is available to the container.
	//
	// This member is required.
	LocalPath *string

	// Whether input data distributed in Amazon S3 is fully replicated or sharded by an
	// S3 key. Defauts to FullyReplicated
	S3DataDistributionType ProcessingS3DataDistributionType

	// Whether the Pipe or File is used as the input mode for transfering data for the
	// monitoring job. Pipe mode is recommended for large datasets. File mode is useful
	// for small files that fit in memory. Defaults to File.
	S3InputMode ProcessingS3InputMode
}

type EndpointSortKey string

const (
	EndpointSortKeyName         EndpointSortKey = "Name"
	EndpointSortKeyCreationtime EndpointSortKey = "CreationTime"
	EndpointSortKeyStatus       EndpointSortKey = "Status"
)

type EndpointStatus string

const (
	EndpointStatusOut_of_service  EndpointStatus = "OutOfService"
	EndpointStatusCreating        EndpointStatus = "Creating"
	EndpointStatusUpdating        EndpointStatus = "Updating"
	EndpointStatusSystem_updating EndpointStatus = "SystemUpdating"
	EndpointStatusRolling_back    EndpointStatus = "RollingBack"
	EndpointStatusIn_service      EndpointStatus = "InService"
	EndpointStatusDeleting        EndpointStatus = "Deleting"
	EndpointStatusFailed          EndpointStatus = "Failed"
)

type EndpointSummary struct {

	// A timestamp that shows when the endpoint was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the endpoint.
	//
	// This member is required.
	EndpointArn *string

	// The name of the endpoint.
	//
	// This member is required.
	EndpointName *string

	// The status of the endpoint.
	//
	//     * OutOfService: Endpoint is not available to
	// take incoming requests.
	//
	//     * Creating: CreateEndpoint is executing.
	//
	//     *
	// Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.
	//
	//
	// * SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or
	// deleted or re-scaled until it has completed. This maintenance operation does not
	// change any customer-specified values such as VPC config, KMS encryption, model,
	// instance type, or instance count.
	//
	//     * RollingBack: Endpoint fails to scale up
	// or down or change its variant weight and is in the process of rolling back to
	// its previous configuration. Once the rollback completes, endpoint returns to an
	// InService status. This transitional status only applies to an endpoint that has
	// autoscaling enabled and is undergoing variant weight or capacity changes as part
	// of an UpdateEndpointWeightsAndCapacities call or when the
	// UpdateEndpointWeightsAndCapacities operation is called explicitly.
	//
	//     *
	// InService: Endpoint is available to process incoming requests.
	//
	//     * Deleting:
	// DeleteEndpoint is executing.
	//
	//     * Failed: Endpoint could not be created,
	// updated, or re-scaled. Use DescribeEndpointOutput$FailureReason for information
	// about the failure. DeleteEndpoint is the only operation that can be performed on
	// a failed endpoint.
	//
	// To get a list of endpoints with a specified status, use the
	// ListEndpointsInput$StatusEquals filter.
	//
	// This member is required.
	EndpointStatus EndpointStatus

	// A timestamp that shows when the endpoint was last modified.
	//
	// This member is required.
	LastModifiedTime *time.Time
}

type ExecutionStatus string

const (
	ExecutionStatusPending                   ExecutionStatus = "Pending"
	ExecutionStatusCompleted                 ExecutionStatus = "Completed"
	ExecutionStatusCompleted_with_violations ExecutionStatus = "CompletedWithViolations"
	ExecutionStatusIn_progress               ExecutionStatus = "InProgress"
	ExecutionStatusFailed                    ExecutionStatus = "Failed"
	ExecutionStatusStopping                  ExecutionStatus = "Stopping"
	ExecutionStatusStopped                   ExecutionStatus = "Stopped"
)

type Experiment struct {

	// Information about the user who created or modified an experiment, trial, or
	// trial component.
	CreatedBy *UserContext

	// When the experiment was created.
	CreationTime *time.Time

	// The description of the experiment.
	Description *string

	// The name of the experiment as displayed. If DisplayName isn't specified,
	// ExperimentName is displayed.
	DisplayName *string

	// The Amazon Resource Name (ARN) of the experiment.
	ExperimentArn *string

	// The name of the experiment.
	ExperimentName *string

	// Information about the user who created or modified an experiment, trial, or
	// trial component.
	LastModifiedBy *UserContext

	// When the experiment was last modified.
	LastModifiedTime *time.Time

	// The source of the experiment.
	Source *ExperimentSource

	// The list of tags that are associated with the experiment. You can use Search API
	// to search on the tags.
	Tags []*Tag
}

type ExperimentConfig struct {

	// The name of an existing experiment to associate the trial component with.
	ExperimentName *string

	// The display name for the trial component. If this key isn't specified, the
	// display name is the trial component name.
	TrialComponentDisplayName *string

	// The name of an existing trial to associate the trial component with. If not
	// specified, a new trial is created.
	TrialName *string
}

type ExperimentSource struct {

	// The Amazon Resource Name (ARN) of the source.
	//
	// This member is required.
	SourceArn *string

	// The source type.
	SourceType *string
}

type ExperimentSummary struct {

	// When the experiment was created.
	CreationTime *time.Time

	// The name of the experiment as displayed. If DisplayName isn't specified,
	// ExperimentName is displayed.
	DisplayName *string

	// The Amazon Resource Name (ARN) of the experiment.
	ExperimentArn *string

	// The name of the experiment.
	ExperimentName *string

	// The source of the experiment.
	ExperimentSource *ExperimentSource

	// When the experiment was last modified.
	LastModifiedTime *time.Time
}

type FileSystemAccessMode string

const (
	FileSystemAccessModeRw FileSystemAccessMode = "rw"
	FileSystemAccessModeRo FileSystemAccessMode = "ro"
)

type FileSystemDataSource struct {

	// The full path to the directory to associate with the channel.
	//
	// This member is required.
	DirectoryPath *string

	// The access mode of the mount of the directory associated with the channel. A
	// directory can be mounted either in ro (read-only) or rw (read-write) mode.
	//
	// This member is required.
	FileSystemAccessMode FileSystemAccessMode

	// The file system id.
	//
	// This member is required.
	FileSystemId *string

	// The file system type.
	//
	// This member is required.
	FileSystemType FileSystemType
}

type FileSystemType string

const (
	FileSystemTypeEfs       FileSystemType = "EFS"
	FileSystemTypeFsxlustre FileSystemType = "FSxLustre"
)

type Filter struct {

	// A resource property name. For example, TrainingJobName. For valid property
	// names, see SearchRecord. You must specify a valid property for the resource.
	//
	// This member is required.
	Name *string

	// A Boolean binary operator that is used to evaluate the filter. The operator
	// field contains one of the following values: Equals The value of Name equals
	// Value. NotEquals The value of Name doesn't equal Value. Exists The Name property
	// exists. NotExists The Name property does not exist. GreaterThan The value of
	// Name is greater than Value. Not supported for text properties.
	// GreaterThanOrEqualTo The value of Name is greater than or equal to Value. Not
	// supported for text properties. LessThan The value of Name is less than Value.
	// Not supported for text properties. LessThanOrEqualTo The value of Name is less
	// than or equal to Value. Not supported for text properties. In The value of Name
	// is one of the comma delimited strings in Value. Only supported for text
	// properties. Contains The value of Name contains the string Value. Only supported
	// for text properties. A SearchExpression can include the Contains operator
	// multiple times when the value of Name is one of the following:
	//
	//     *
	// Experiment.DisplayName
	//
	//     * Experiment.ExperimentName
	//
	//     * Experiment.Tags
	//
	//
	// * Trial.DisplayName
	//
	//     * Trial.TrialName
	//
	//     * Trial.Tags
	//
	//     *
	// TrialComponent.DisplayName
	//
	//     * TrialComponent.TrialComponentName
	//
	//     *
	// TrialComponent.Tags
	//
	//     * TrialComponent.InputArtifacts
	//
	//     *
	// TrialComponent.OutputArtifacts
	//
	// A SearchExpression can include only one Contains
	// operator for all other values of Name. In these cases, if you include multiple
	// Contains operators in the SearchExpression, the result is the following error
	// message: "'CONTAINS' operator usage limit of 1 exceeded."
	Operator Operator

	// A value used with Name and Operator to determine which resources satisfy the
	// filter's condition. For numerical properties, Value must be an integer or
	// floating-point decimal. For timestamp properties, Value must be an ISO 8601
	// date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS.
	Value *string
}

type FinalAutoMLJobObjectiveMetric struct {

	// The name of the metric.
	//
	// This member is required.
	MetricName AutoMLMetricEnum

	// The value of the metric.
	//
	// This member is required.
	Value *float32

	// The metric type used.
	Type AutoMLJobObjectiveType
}

type FinalHyperParameterTuningJobObjectiveMetric struct {

	// The name of the objective metric.
	//
	// This member is required.
	MetricName *string

	// The value of the objective metric.
	//
	// This member is required.
	Value *float32

	// Whether to minimize or maximize the objective metric. Valid values are Minimize
	// and Maximize.
	Type HyperParameterTuningJobObjectiveType
}

type FlowDefinitionOutputConfig struct {

	// The Amazon S3 path where the object containing human output will be made
	// available.
	//
	// This member is required.
	S3OutputPath *string

	// The Amazon Key Management Service (KMS) key ID for server-side encryption.
	KmsKeyId *string
}

type FlowDefinitionStatus string

const (
	FlowDefinitionStatusInitializing FlowDefinitionStatus = "Initializing"
	FlowDefinitionStatusActive       FlowDefinitionStatus = "Active"
	FlowDefinitionStatusFailed       FlowDefinitionStatus = "Failed"
	FlowDefinitionStatusDeleting     FlowDefinitionStatus = "Deleting"
)

type FlowDefinitionSummary struct {

	// The timestamp when SageMaker created the flow definition.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the flow definition.
	//
	// This member is required.
	FlowDefinitionArn *string

	// The name of the flow definition.
	//
	// This member is required.
	FlowDefinitionName *string

	// The status of the flow definition. Valid values:
	//
	// This member is required.
	FlowDefinitionStatus FlowDefinitionStatus

	// The reason why the flow definition creation failed. A failure reason is returned
	// only when the flow definition status is Failed.
	FailureReason *string
}

type Framework string

const (
	FrameworkTensorflow Framework = "TENSORFLOW"
	FrameworkKeras      Framework = "KERAS"
	FrameworkMxnet      Framework = "MXNET"
	FrameworkOnnx       Framework = "ONNX"
	FrameworkPytorch    Framework = "PYTORCH"
	FrameworkXgboost    Framework = "XGBOOST"
	FrameworkTflite     Framework = "TFLITE"
)

type GitConfig struct {

	// The URL where the Git repository is located.
	//
	// This member is required.
	RepositoryUrl *string

	// The default branch for the Git repository.
	Branch *string

	// The Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains
	// the credentials used to access the git repository. The secret must have a
	// staging label of AWSCURRENT and must be in the following format: {"username":
	// UserName, "password": Password}
	SecretArn *string
}

type GitConfigForUpdate struct {

	// The Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains
	// the credentials used to access the git repository. The secret must have a
	// staging label of AWSCURRENT and must be in the following format: {"username":
	// UserName, "password": Password}
	SecretArn *string
}

type HumanLoopActivationConditionsConfig struct {

	// JSON expressing use-case specific conditions declaratively. If any condition is
	// matched, atomic tasks are created against the configured work team. The set of
	// conditions is different for Rekognition and Textract. For more information about
	// how to structure the JSON, see JSON Schema for Human Loop Activation Conditions
	// in Amazon Augmented AI
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/a2i-human-fallback-conditions-json-schema.html)
	// in the Amazon SageMaker Developer Guide.
	// This value conforms to the media type: application/json
	//
	// This member is required.
	HumanLoopActivationConditions *string
}

type HumanLoopActivationConfig struct {

	// Container structure for defining under what conditions SageMaker creates a human
	// loop.
	//
	// This member is required.
	HumanLoopActivationConditionsConfig *HumanLoopActivationConditionsConfig
}

type HumanLoopConfig struct {

	// The Amazon Resource Name (ARN) of the human task user interface.
	//
	// This member is required.
	HumanTaskUiArn *string

	// The number of distinct workers who will perform the same task on each object.
	// For example, if TaskCount is set to 3 for an image classification labeling job,
	// three workers will classify each input image. Increasing TaskCount can improve
	// label accuracy.
	//
	// This member is required.
	TaskCount *int32

	// A description for the human worker task.
	//
	// This member is required.
	TaskDescription *string

	// A title for the human worker task.
	//
	// This member is required.
	TaskTitle *string

	// Amazon Resource Name (ARN) of a team of workers.
	//
	// This member is required.
	WorkteamArn *string

	// Defines the amount of money paid to an Amazon Mechanical Turk worker for each
	// task performed. Use one of the following prices for bounding box tasks. Prices
	// are in US dollars and should be based on the complexity of the task; the longer
	// it takes in your initial testing, the more you should offer.
	//
	//     * 0.036
	//
	//     *
	// 0.048
	//
	//     * 0.060
	//
	//     * 0.072
	//
	//     * 0.120
	//
	//     * 0.240
	//
	//     * 0.360
	//
	//     *
	// 0.480
	//
	//     * 0.600
	//
	//     * 0.720
	//
	//     * 0.840
	//
	//     * 0.960
	//
	//     * 1.080
	//
	//     *
	// 1.200
	//
	// Use one of the following prices for image classification, text
	// classification, and custom tasks. Prices are in US dollars.
	//
	//     * 0.012
	//
	//     *
	// 0.024
	//
	//     * 0.036
	//
	//     * 0.048
	//
	//     * 0.060
	//
	//     * 0.072
	//
	//     * 0.120
	//
	//     *
	// 0.240
	//
	//     * 0.360
	//
	//     * 0.480
	//
	//     * 0.600
	//
	//     * 0.720
	//
	//     * 0.840
	//
	//     *
	// 0.960
	//
	//     * 1.080
	//
	//     * 1.200
	//
	// Use one of the following prices for semantic
	// segmentation tasks. Prices are in US dollars.
	//
	//     * 0.840
	//
	//     * 0.960
	//
	//     *
	// 1.080
	//
	//     * 1.200
	//
	// Use one of the following prices for Textract AnalyzeDocument
	// Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.
	//
	//
	// * 2.400
	//
	//     * 2.280
	//
	//     * 2.160
	//
	//     * 2.040
	//
	//     * 1.920
	//
	//     * 1.800
	//
	//     *
	// 1.680
	//
	//     * 1.560
	//
	//     * 1.440
	//
	//     * 1.320
	//
	//     * 1.200
	//
	//     * 1.080
	//
	//     *
	// 0.960
	//
	//     * 0.840
	//
	//     * 0.720
	//
	//     * 0.600
	//
	//     * 0.480
	//
	//     * 0.360
	//
	//     *
	// 0.240
	//
	//     * 0.120
	//
	//     * 0.072
	//
	//     * 0.060
	//
	//     * 0.048
	//
	//     * 0.036
	//
	//     *
	// 0.024
	//
	//     * 0.012
	//
	// Use one of the following prices for Rekognition
	// DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US
	// dollars.
	//
	//     * 1.200
	//
	//     * 1.080
	//
	//     * 0.960
	//
	//     * 0.840
	//
	//     * 0.720
	//
	//     *
	// 0.600
	//
	//     * 0.480
	//
	//     * 0.360
	//
	//     * 0.240
	//
	//     * 0.120
	//
	//     * 0.072
	//
	//     *
	// 0.060
	//
	//     * 0.048
	//
	//     * 0.036
	//
	//     * 0.024
	//
	//     * 0.012
	//
	// Use one of the
	// following prices for Amazon Augmented AI custom human review tasks. Prices are
	// in US dollars.
	//
	//     * 1.200
	//
	//     * 1.080
	//
	//     * 0.960
	//
	//     * 0.840
	//
	//     *
	// 0.720
	//
	//     * 0.600
	//
	//     * 0.480
	//
	//     * 0.360
	//
	//     * 0.240
	//
	//     * 0.120
	//
	//     *
	// 0.072
	//
	//     * 0.060
	//
	//     * 0.048
	//
	//     * 0.036
	//
	//     * 0.024
	//
	//     * 0.012
	PublicWorkforceTaskPrice *PublicWorkforceTaskPrice

	// The length of time that a task remains available for labeling by human workers.
	TaskAvailabilityLifetimeInSeconds *int32

	// Keywords used to describe the task so that workers can discover the task.
	TaskKeywords []*string

	// The amount of time that a worker has to complete a task.
	TaskTimeLimitInSeconds *int32
}

type HumanLoopRequestSource struct {

	// Specifies whether Amazon Rekognition or Amazon Textract are used as the
	// integration source. The default field settings and JSON parsing rules are
	// different based on the integration source. Valid values:
	//
	// This member is required.
	AwsManagedHumanLoopRequestSource AwsManagedHumanLoopRequestSource
}

type HumanTaskConfig struct {

	// Configures how labels are consolidated across human workers.
	//
	// This member is required.
	AnnotationConsolidationConfig *AnnotationConsolidationConfig

	// The number of human workers that will label an object.
	//
	// This member is required.
	NumberOfHumanWorkersPerDataObject *int32

	// The Amazon Resource Name (ARN) of a Lambda function that is run before a data
	// object is sent to a human worker. Use this function to provide input to a custom
	// labeling job. For built-in task types
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html), use one
	// of the following Amazon SageMaker Ground Truth Lambda function ARNs for
	// PreHumanTaskLambdaArn. For custom labeling workflows, see Pre-annotation Lambda
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates-step3.html#sms-custom-templates-step3-prelambda).
	// Bounding box - Finds the most similar boxes from different workers based on the
	// Jaccard index of the boxes.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox
	//
	//     *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox
	//
	// Image
	// classification - Uses a variant of the Expectation Maximization approach to
	// estimate the true class of an image based on annotations from individual
	// workers.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass
	//
	//     *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass
	//
	// Multi-label
	// image classification - Uses a variant of the Expectation Maximization approach
	// to estimate the true classes of an image based on annotations from individual
	// workers.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// * arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// * arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// * arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// * arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel
	//
	// Semantic
	// segmentation - Treats each pixel in an image as a multi-class classification and
	// treats pixel annotations from workers as "votes" for the correct label.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation
	//
	//     *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation
	//
	//     *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation
	//
	//     *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation
	//
	//
	// * arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation
	//
	//     *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation
	//
	//     *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation
	//
	//
	// * arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation
	//
	//
	// * arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation
	//
	//
	// * arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation
	//
	//
	// * arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation
	//
	// Text
	// classification - Uses a variant of the Expectation Maximization approach to
	// estimate the true class of text based on annotations from individual workers.
	//
	//
	// * arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass
	//
	//     *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass
	//
	// Multi-label
	// text classification - Uses a variant of the Expectation Maximization approach to
	// estimate the true classes of text based on annotations from individual
	// workers.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel
	//
	//
	// * arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel
	//
	//
	// * arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel
	//
	//
	// * arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel
	//
	//
	// * arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel
	//
	//
	// * arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel
	//
	// Named
	// entity recognition - Groups similar selections and calculates aggregate
	// boundaries, resolving to most-assigned label.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition
	//
	//     *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition
	//
	//     *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition
	//
	//     *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition
	//
	//
	// * arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition
	//
	//
	// * arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition
	//
	//
	// * arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition
	//
	//
	// * arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition
	//
	// Video
	// Classification - Use this task type when you need workers to classify videos
	// using predefined labels that you specify. Workers are shown videos and are asked
	// to choose one label for each video.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass
	//
	//     *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass
	//
	// Video
	// Frame Object Detection - Use this task type to have workers identify and locate
	// objects in a sequence of video frames (images extracted from a video) using
	// bounding boxes. For example, you can use this task to ask workers to identify
	// and localize various objects in a series of video frames, such as cars, bikes,
	// and pedestrians.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection
	//
	//     *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection
	//
	//     *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection
	//
	//     *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection
	//
	//     *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection
	//
	//
	// * arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection
	//
	//
	// * arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection
	//
	//
	// * arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection
	//
	//
	// * arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection
	//
	//
	// * arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection
	//
	//     *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection
	//
	// Video
	// Frame Object Tracking - Use this task type to have workers track the movement of
	// objects in a sequence of video frames (images extracted from a video) using
	// bounding boxes. For example, you can use this task to ask workers to track the
	// movement of objects, such as cars, bikes, and pedestrians.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking
	//
	//     *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking
	//
	//     *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking
	//
	//     *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking
	//
	//     *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking
	//
	//
	// * arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking
	//
	//
	// * arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking
	//
	//     *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking
	//
	//     *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking
	//
	//
	// * arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking
	//
	//     *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking
	//
	//
	// * arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking
	//
	// 3D
	// Point Cloud Modalities Use the following pre-annotation lambdas for 3D point
	// cloud labeling modality tasks. See 3D Point Cloud Task types
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-task-types.html)
	// to learn more. 3D Point Cloud Object Detection - Use this task type when you
	// want workers to classify objects in a 3D point cloud by drawing 3D cuboids
	// around objects. For example, you can use this task type to ask workers to
	// identify different types of objects in a point cloud, such as cars, bikes, and
	// pedestrians.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection
	//
	// 3D
	// Point Cloud Object Tracking - Use this task type when you want workers to draw
	// 3D cuboids around objects that appear in a sequence of 3D point cloud frames.
	// For example, you can use this task type to ask workers to track the movement of
	// vehicles across multiple point cloud frames.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking
	//
	// 3D
	// Point Cloud Semantic Segmentation - Use this task type when you want workers to
	// create a point-level semantic segmentation masks by painting objects in a 3D
	// point cloud using different colors where each color is assigned to one of the
	// classes you specify.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation
	//
	// Use
	// the following ARNs for Label Verification and Adjustment Jobs Use label
	// verification and adjustment jobs to review and adjust labels. To learn more, see
	// Verify and Adjust Labels
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html).
	// Bounding box verification - Uses a variant of the Expectation Maximization
	// approach to estimate the true class of verification judgement for bounding box
	// labels based on annotations from individual workers.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	// Bounding
	// box adjustment - Finds the most similar boxes from different workers based on
	// the Jaccard index of the adjusted annotations.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox
	//
	//     *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox
	//
	//     *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox
	//
	//     *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox
	//
	//
	// * arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox
	//
	//
	// * arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox
	//
	//
	// * arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox
	//
	//
	// * arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox
	//
	// Semantic
	// segmentation verification - Uses a variant of the Expectation Maximization
	// approach to estimate the true class of verification judgment for semantic
	// segmentation labels based on annotations from individual workers.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation
	//
	// Semantic
	// segmentation adjustment - Treats each pixel in an image as a multi-class
	// classification and treats pixel adjusted annotations from workers as "votes" for
	// the correct label.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation
	//
	// Video
	// Frame Object Detection Adjustment - Use this task type when you want workers to
	// adjust bounding boxes that workers have added to video frames to classify and
	// localize objects in a sequence of video frames.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection
	//
	// Video
	// Frame Object Tracking Adjustment - Use this task type when you want workers to
	// adjust bounding boxes that workers have added to video frames to track object
	// movement across a sequence of video frames.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking
	//
	// 3D
	// point cloud object detection adjustment - Adjust 3D cuboids in a point cloud
	// frame.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection
	//
	// 3D
	// point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of
	// point cloud frames.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking
	//
	// 3D
	// point cloud semantic segmentation adjustment - Adjust semantic segmentation
	// masks in a 3D point cloud.
	//
	//     *
	// arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	//
	// *
	// arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation
	//
	// This member is required.
	PreHumanTaskLambdaArn *string

	// A description of the task for your human workers.
	//
	// This member is required.
	TaskDescription *string

	// The amount of time that a worker has to complete a task.
	//
	// This member is required.
	TaskTimeLimitInSeconds *int32

	// A title for the task for your human workers.
	//
	// This member is required.
	TaskTitle *string

	// Information about the user interface that workers use to complete the labeling
	// task.
	//
	// This member is required.
	UiConfig *UiConfig

	// The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
	//
	// This member is required.
	WorkteamArn *string

	// Defines the maximum number of data objects that can be labeled by human workers
	// at the same time. Also referred to as batch size. Each object may have more than
	// one worker at one time. The default value is 1000 objects.
	MaxConcurrentTaskCount *int32

	// The price that you pay for each task performed by an Amazon Mechanical Turk
	// worker.
	PublicWorkforceTaskPrice *PublicWorkforceTaskPrice

	// The length of time that a task remains available for labeling by human workers.
	// If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours
	// (43200). The default value is 864000 seconds (10 days). For private and vendor
	// workforces, the maximum is as listed.
	TaskAvailabilityLifetimeInSeconds *int32

	// Keywords used to describe the task so that workers on Amazon Mechanical Turk can
	// discover the task.
	TaskKeywords []*string
}

type HumanTaskUiStatus string

const (
	HumanTaskUiStatusActive   HumanTaskUiStatus = "Active"
	HumanTaskUiStatusDeleting HumanTaskUiStatus = "Deleting"
)

type HumanTaskUiSummary struct {

	// A timestamp when SageMaker created the human task user interface.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the human task user interface.
	//
	// This member is required.
	HumanTaskUiArn *string

	// The name of the human task user interface.
	//
	// This member is required.
	HumanTaskUiName *string
}

type HyperParameterAlgorithmSpecification struct {

	// The input mode that the algorithm supports: File or Pipe. In File input mode,
	// Amazon SageMaker downloads the training data from Amazon S3 to the storage
	// volume that is attached to the training instance and mounts the directory to the
	// Docker volume for the training container. In Pipe input mode, Amazon SageMaker
	// streams data directly from Amazon S3 to the container. If you specify File mode,
	// make sure that you provision the storage volume that is attached to the training
	// instance with enough capacity to accommodate the training data downloaded from
	// Amazon S3, the model artifacts, and intermediate information. For more
	// information about input modes, see Algorithms
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).
	//
	// This member is required.
	TrainingInputMode TrainingInputMode

	// The name of the resource algorithm to use for the hyperparameter tuning job. If
	// you specify a value for this parameter, do not specify a value for
	// TrainingImage.
	AlgorithmName *string

	// An array of MetricDefinition objects that specify the metrics that the algorithm
	// emits.
	MetricDefinitions []*MetricDefinition

	// The registry path of the Docker image that contains the training algorithm. For
	// information about Docker registry paths for built-in algorithms, see Algorithms
	// Provided by Amazon SageMaker: Common Parameters
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html).
	// Amazon SageMaker supports both registry/repository[:tag] and
	// registry/repository[@digest] image path formats. For more information, see Using
	// Your Own Algorithms with Amazon SageMaker
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html).
	TrainingImage *string
}

type HyperParameterScalingType string

const (
	HyperParameterScalingTypeAuto                HyperParameterScalingType = "Auto"
	HyperParameterScalingTypeLinear              HyperParameterScalingType = "Linear"
	HyperParameterScalingTypeLogarithmic         HyperParameterScalingType = "Logarithmic"
	HyperParameterScalingTypeReverse_logarithmic HyperParameterScalingType = "ReverseLogarithmic"
)

type HyperParameterSpecification struct {

	// The name of this hyperparameter. The name must be unique.
	//
	// This member is required.
	Name *string

	// The type of this hyperparameter. The valid types are Integer, Continuous,
	// Categorical, and FreeText.
	//
	// This member is required.
	Type ParameterType

	// The default value for this hyperparameter. If a default value is specified, a
	// hyperparameter cannot be required.
	DefaultValue *string

	// A brief description of the hyperparameter.
	Description *string

	// Indicates whether this hyperparameter is required.
	IsRequired *bool

	// Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
	IsTunable *bool

	// The allowed range for this hyperparameter.
	Range *ParameterRange
}

type HyperParameterTrainingJobDefinition struct {

	// The HyperParameterAlgorithmSpecification object that specifies the resource
	// algorithm to use for the training jobs that the tuning job launches.
	//
	// This member is required.
	AlgorithmSpecification *HyperParameterAlgorithmSpecification

	// Specifies the path to the Amazon S3 bucket where you store model artifacts from
	// the training jobs that the tuning job launches.
	//
	// This member is required.
	OutputDataConfig *OutputDataConfig

	// The resources, including the compute instances and storage volumes, to use for
	// the training jobs that the tuning job launches. Storage volumes store model
	// artifacts and incremental states. Training algorithms might also use storage
	// volumes for scratch space. If you want Amazon SageMaker to use the storage
	// volume to store the training data, choose File as the TrainingInputMode in the
	// algorithm specification. For distributed training algorithms, specify an
	// instance count greater than 1.
	//
	// This member is required.
	ResourceConfig *ResourceConfig

	// The Amazon Resource Name (ARN) of the IAM role associated with the training jobs
	// that the tuning job launches.
	//
	// This member is required.
	RoleArn *string

	// Specifies a limit to how long a model hyperparameter training job can run. It
	// also specifies how long you are willing to wait for a managed spot training job
	// to complete. When the job reaches the a limit, Amazon SageMaker ends the
	// training job. Use this API to cap model training costs.
	//
	// This member is required.
	StoppingCondition *StoppingCondition

	// Contains information about the output location for managed spot training
	// checkpoint data.
	CheckpointConfig *CheckpointConfig

	// The job definition name.
	DefinitionName *string

	// To encrypt all communications between ML compute instances in distributed
	// training, choose True. Encryption provides greater security for distributed
	// training, but training might take longer. How long it takes depends on the
	// amount of communication between compute instances, especially if you use a deep
	// learning algorithm in distributed training.
	EnableInterContainerTrafficEncryption *bool

	// A Boolean indicating whether managed spot training is enabled (True) or not
	// (False).
	EnableManagedSpotTraining *bool

	// Isolates the training container. No inbound or outbound network calls can be
	// made, except for calls between peers within a training cluster for distributed
	// training. If network isolation is used for training jobs that are configured to
	// use a VPC, Amazon SageMaker downloads and uploads customer data and model
	// artifacts through the specified VPC, but the training container does not have
	// network access.
	EnableNetworkIsolation *bool

	// Specifies ranges of integer, continuous, and categorical hyperparameters that a
	// hyperparameter tuning job searches. The hyperparameter tuning job launches
	// training jobs with hyperparameter values within these ranges to find the
	// combination of values that result in the training job with the best performance
	// as measured by the objective metric of the hyperparameter tuning job. You can
	// specify a maximum of 20 hyperparameters that a hyperparameter tuning job can
	// search over. Every possible value of a categorical parameter range counts
	// against this limit.
	HyperParameterRanges *ParameterRanges

	// An array of Channel objects that specify the input for the training jobs that
	// the tuning job launches.
	InputDataConfig []*Channel

	// Specifies the values of hyperparameters that do not change for the tuning job.
	StaticHyperParameters map[string]*string

	// Defines the objective metric for a hyperparameter tuning job. Hyperparameter
	// tuning uses the value of this metric to evaluate the training jobs it launches,
	// and returns the training job that results in either the highest or lowest value
	// for this metric, depending on the value you specify for the Type parameter.
	TuningObjective *HyperParameterTuningJobObjective

	// The VpcConfig object that specifies the VPC that you want the training jobs that
	// this hyperparameter tuning job launches to connect to. Control access to and
	// from your training container by configuring the VPC. For more information, see
	// Protect Training Jobs by Using an Amazon Virtual Private Cloud
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html).
	VpcConfig *VpcConfig
}

type HyperParameterTrainingJobSummary struct {

	// The date and time that the training job was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the training job.
	//
	// This member is required.
	TrainingJobArn *string

	// The name of the training job.
	//
	// This member is required.
	TrainingJobName *string

	// The status of the training job.
	//
	// This member is required.
	TrainingJobStatus TrainingJobStatus

	// A list of the hyperparameters for which you specified ranges to search.
	//
	// This member is required.
	TunedHyperParameters map[string]*string

	// The reason that the training job failed.
	FailureReason *string

	// The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value
	// of the objective metric of the tuning job that launched this training job.
	FinalHyperParameterTuningJobObjectiveMetric *FinalHyperParameterTuningJobObjectiveMetric

	// The status of the objective metric for the training job:
	//
	//     * Succeeded: The
	// final objective metric for the training job was evaluated by the hyperparameter
	// tuning job and used in the hyperparameter tuning process.
	//
	//     * Pending: The
	// training job is in progress and evaluation of its final objective metric is
	// pending.
	//
	//     * Failed: The final objective metric for the training job was not
	// evaluated, and was not used in the hyperparameter tuning process. This typically
	// occurs when the training job failed or did not emit an objective metric.
	ObjectiveStatus ObjectiveStatus

	// Specifies the time when the training job ends on training instances. You are
	// billed for the time interval between the value of TrainingStartTime and this
	// time. For successful jobs and stopped jobs, this is the time after model
	// artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker
	// detects a job failure.
	TrainingEndTime *time.Time

	// The training job definition name.
	TrainingJobDefinitionName *string

	// The date and time that the training job started.
	TrainingStartTime *time.Time

	// The HyperParameter tuning job that launched the training job.
	TuningJobName *string
}

type HyperParameterTuningJobConfig struct {

	// The ResourceLimits object that specifies the maximum number of training jobs and
	// parallel training jobs for this tuning job.
	//
	// This member is required.
	ResourceLimits *ResourceLimits

	// Specifies how hyperparameter tuning chooses the combinations of hyperparameter
	// values to use for the training job it launches. To use the Bayesian search
	// strategy, set this to Bayesian. To randomly search, set it to Random. For
	// information about search strategies, see How Hyperparameter Tuning Works
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html).
	//
	// This member is required.
	Strategy HyperParameterTuningJobStrategyType

	// The HyperParameterTuningJobObjective object that specifies the objective metric
	// for this tuning job.
	HyperParameterTuningJobObjective *HyperParameterTuningJobObjective

	// The ParameterRanges object that specifies the ranges of hyperparameters that
	// this tuning job searches.
	ParameterRanges *ParameterRanges

	// Specifies whether to use early stopping for training jobs launched by the
	// hyperparameter tuning job. This can be one of the following values (the default
	// value is OFF): OFF Training jobs launched by the hyperparameter tuning job do
	// not use early stopping. AUTO Amazon SageMaker stops training jobs launched by
	// the hyperparameter tuning job when they are unlikely to perform better than
	// previously completed training jobs. For more information, see Stop Training Jobs
	// Early
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html).
	TrainingJobEarlyStoppingType TrainingJobEarlyStoppingType

	// The tuning job's completion criteria.
	TuningJobCompletionCriteria *TuningJobCompletionCriteria
}

type HyperParameterTuningJobObjective struct {

	// The name of the metric to use for the objective metric.
	//
	// This member is required.
	MetricName *string

	// Whether to minimize or maximize the objective metric.
	//
	// This member is required.
	Type HyperParameterTuningJobObjectiveType
}

type HyperParameterTuningJobObjectiveType string

const (
	HyperParameterTuningJobObjectiveTypeMaximize HyperParameterTuningJobObjectiveType = "Maximize"
	HyperParameterTuningJobObjectiveTypeMinimize HyperParameterTuningJobObjectiveType = "Minimize"
)

type HyperParameterTuningJobSortByOptions string

const (
	HyperParameterTuningJobSortByOptionsName         HyperParameterTuningJobSortByOptions = "Name"
	HyperParameterTuningJobSortByOptionsStatus       HyperParameterTuningJobSortByOptions = "Status"
	HyperParameterTuningJobSortByOptionsCreationtime HyperParameterTuningJobSortByOptions = "CreationTime"
)

type HyperParameterTuningJobStatus string

const (
	HyperParameterTuningJobStatusCompleted   HyperParameterTuningJobStatus = "Completed"
	HyperParameterTuningJobStatusIn_progress HyperParameterTuningJobStatus = "InProgress"
	HyperParameterTuningJobStatusFailed      HyperParameterTuningJobStatus = "Failed"
	HyperParameterTuningJobStatusStopped     HyperParameterTuningJobStatus = "Stopped"
	HyperParameterTuningJobStatusStopping    HyperParameterTuningJobStatus = "Stopping"
)

type HyperParameterTuningJobStrategyType string

const (
	HyperParameterTuningJobStrategyTypeBayesian HyperParameterTuningJobStrategyType = "Bayesian"
	HyperParameterTuningJobStrategyTypeRandom   HyperParameterTuningJobStrategyType = "Random"
)

type HyperParameterTuningJobSummary struct {

	// The date and time that the tuning job was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the tuning job.
	//
	// This member is required.
	HyperParameterTuningJobArn *string

	// The name of the tuning job.
	//
	// This member is required.
	HyperParameterTuningJobName *string

	// The status of the tuning job.
	//
	// This member is required.
	HyperParameterTuningJobStatus HyperParameterTuningJobStatus

	// The ObjectiveStatusCounters object that specifies the numbers of training jobs,
	// categorized by objective metric status, that this tuning job launched.
	//
	// This member is required.
	ObjectiveStatusCounters *ObjectiveStatusCounters

	// Specifies the search strategy hyperparameter tuning uses to choose which
	// hyperparameters to use for each iteration. Currently, the only valid value is
	// Bayesian.
	//
	// This member is required.
	Strategy HyperParameterTuningJobStrategyType

	// The TrainingJobStatusCounters object that specifies the numbers of training
	// jobs, categorized by status, that this tuning job launched.
	//
	// This member is required.
	TrainingJobStatusCounters *TrainingJobStatusCounters

	// The date and time that the tuning job ended.
	HyperParameterTuningEndTime *time.Time

	// The date and time that the tuning job was modified.
	LastModifiedTime *time.Time

	// The ResourceLimits object that specifies the maximum number of training jobs and
	// parallel training jobs allowed for this tuning job.
	ResourceLimits *ResourceLimits
}

type HyperParameterTuningJobWarmStartConfig struct {

	// An array of hyperparameter tuning jobs that are used as the starting point for
	// the new hyperparameter tuning job. For more information about warm starting a
	// hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a
	// Starting Point
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html).
	// Hyperparameter tuning jobs created before October 1, 2018 cannot be used as
	// parent jobs for warm start tuning jobs.
	//
	// This member is required.
	ParentHyperParameterTuningJobs []*ParentHyperParameterTuningJob

	// Specifies one of the following: IDENTICAL_DATA_AND_ALGORITHM The new
	// hyperparameter tuning job uses the same input data and training image as the
	// parent tuning jobs. You can change the hyperparameter ranges to search and the
	// maximum number of training jobs that the hyperparameter tuning job launches. You
	// cannot use a new version of the training algorithm, unless the changes in the
	// new version do not affect the algorithm itself. For example, changes that
	// improve logging or adding support for a different data format are allowed. You
	// can also change hyperparameters from tunable to static, and from static to
	// tunable, but the total number of static plus tunable hyperparameters must remain
	// the same as it is in all parent jobs. The objective metric for the new tuning
	// job must be the same as for all parent jobs. TRANSFER_LEARNING The new
	// hyperparameter tuning job can include input data, hyperparameter ranges, maximum
	// number of concurrent training jobs, and maximum number of training jobs that are
	// different than those of its parent hyperparameter tuning jobs. The training
	// image can also be a different version from the version used in the parent
	// hyperparameter tuning job. You can also change hyperparameters from tunable to
	// static, and from static to tunable, but the total number of static plus tunable
	// hyperparameters must remain the same as it is in all parent jobs. The objective
	// metric for the new tuning job must be the same as for all parent jobs.
	//
	// This member is required.
	WarmStartType HyperParameterTuningJobWarmStartType
}

type HyperParameterTuningJobWarmStartType string

const (
	HyperParameterTuningJobWarmStartTypeIdentical_data_and_algorithm HyperParameterTuningJobWarmStartType = "IdenticalDataAndAlgorithm"
	HyperParameterTuningJobWarmStartTypeTransfer_learning            HyperParameterTuningJobWarmStartType = "TransferLearning"
)

type InferenceSpecification struct {

	// The Amazon ECR registry path of the Docker image that contains the inference
	// code.
	//
	// This member is required.
	Containers []*ModelPackageContainerDefinition

	// The supported MIME types for the input data.
	//
	// This member is required.
	SupportedContentTypes []*string

	// A list of the instance types that are used to generate inferences in real-time.
	//
	// This member is required.
	SupportedRealtimeInferenceInstanceTypes []ProductionVariantInstanceType

	// The supported MIME types for the output data.
	//
	// This member is required.
	SupportedResponseMIMETypes []*string

	// A list of the instance types on which a transformation job can be run or on
	// which an endpoint can be deployed.
	//
	// This member is required.
	SupportedTransformInstanceTypes []TransformInstanceType
}

type InputConfig struct {

	// Specifies the name and shape of the expected data inputs for your trained model
	// with a JSON dictionary form. The data inputs are InputConfig$Framework
	// specific.
	//
	//     * TensorFlow: You must specify the name and shape (NHWC format)
	// of the expected data inputs using a dictionary format for your trained model.
	// The dictionary formats required for the console and CLI are different.
	//
	//
	// * Examples for one input:
	//
	//             * If using the console,
	// {"input":[1,1024,1024,3]}
	//
	//             * If using the CLI,
	// {\"input\":[1,1024,1024,3]}
	//
	//         * Examples for two inputs:
	//
	//             *
	// If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
	//
	//             *
	// If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
	//
	//     * KERAS:
	// You must specify the name and shape (NCHW format) of expected data inputs using
	// a dictionary format for your trained model. Note that while Keras model
	// artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig
	// should be specified in NCHW (channel-first) format. The dictionary formats
	// required for the console and CLI are different.
	//
	//         * Examples for one
	// input:
	//
	//             * If using the console, {"input_1":[1,3,224,224]}
	//
	//
	// * If using the CLI, {\"input_1\":[1,3,224,224]}
	//
	//         * Examples for two
	// inputs:
	//
	//             * If using the console, {"input_1": [1,3,224,224],
	// "input_2":[1,3,224,224]}
	//
	//             * If using the CLI, {\"input_1\":
	// [1,3,224,224], \"input_2\":[1,3,224,224]}
	//
	//     * MXNET/ONNX: You must specify
	// the name and shape (NCHW format) of the expected data inputs in order using a
	// dictionary format for your trained model. The dictionary formats required for
	// the console and CLI are different.
	//
	//         * Examples for one input:
	//
	//
	// * If using the console, {"data":[1,3,1024,1024]}
	//
	//             * If using the
	// CLI, {\"data\":[1,3,1024,1024]}
	//
	//         * Examples for two inputs:
	//
	//
	// * If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
	//
	//             *
	// If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
	//
	//     * PyTorch:
	// You can either specify the name and shape (NCHW format) of expected data inputs
	// in order using a dictionary format for your trained model or you can specify the
	// shape only using a list format. The dictionary formats required for the console
	// and CLI are different. The list formats for the console and CLI are the same.
	//
	//
	// * Examples for one input in dictionary format:
	//
	//             * If using the
	// console, {"input0":[1,3,224,224]}
	//
	//             * If using the CLI,
	// {\"input0\":[1,3,224,224]}
	//
	//         * Example for one input in list format:
	// [[1,3,224,224]]
	//
	//         * Examples for two inputs in dictionary format:
	//
	//
	// * If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
	//
	//
	// * If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
	//
	//
	// * Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
	//
	//     *
	// XGBOOST: input data name and shape are not needed.
	//
	// This member is required.
	DataInputConfig *string

	// Identifies the framework in which the model was trained. For example:
	// TENSORFLOW.
	//
	// This member is required.
	Framework Framework

	// The S3 path where the model artifacts, which result from model training, are
	// stored. This path must point to a single gzip compressed tar archive (.tar.gz
	// suffix).
	//
	// This member is required.
	S3Uri *string
}

type InstanceType string

const (
	InstanceTypeMl_t2_medium    InstanceType = "ml.t2.medium"
	InstanceTypeMl_t2_large     InstanceType = "ml.t2.large"
	InstanceTypeMl_t2_xlarge    InstanceType = "ml.t2.xlarge"
	InstanceTypeMl_t2_2xlarge   InstanceType = "ml.t2.2xlarge"
	InstanceTypeMl_t3_medium    InstanceType = "ml.t3.medium"
	InstanceTypeMl_t3_large     InstanceType = "ml.t3.large"
	InstanceTypeMl_t3_xlarge    InstanceType = "ml.t3.xlarge"
	InstanceTypeMl_t3_2xlarge   InstanceType = "ml.t3.2xlarge"
	InstanceTypeMl_m4_xlarge    InstanceType = "ml.m4.xlarge"
	InstanceTypeMl_m4_2xlarge   InstanceType = "ml.m4.2xlarge"
	InstanceTypeMl_m4_4xlarge   InstanceType = "ml.m4.4xlarge"
	InstanceTypeMl_m4_10xlarge  InstanceType = "ml.m4.10xlarge"
	InstanceTypeMl_m4_16xlarge  InstanceType = "ml.m4.16xlarge"
	InstanceTypeMl_m5_xlarge    InstanceType = "ml.m5.xlarge"
	InstanceTypeMl_m5_2xlarge   InstanceType = "ml.m5.2xlarge"
	InstanceTypeMl_m5_4xlarge   InstanceType = "ml.m5.4xlarge"
	InstanceTypeMl_m5_12xlarge  InstanceType = "ml.m5.12xlarge"
	InstanceTypeMl_m5_24xlarge  InstanceType = "ml.m5.24xlarge"
	InstanceTypeMl_c4_xlarge    InstanceType = "ml.c4.xlarge"
	InstanceTypeMl_c4_2xlarge   InstanceType = "ml.c4.2xlarge"
	InstanceTypeMl_c4_4xlarge   InstanceType = "ml.c4.4xlarge"
	InstanceTypeMl_c4_8xlarge   InstanceType = "ml.c4.8xlarge"
	InstanceTypeMl_c5_xlarge    InstanceType = "ml.c5.xlarge"
	InstanceTypeMl_c5_2xlarge   InstanceType = "ml.c5.2xlarge"
	InstanceTypeMl_c5_4xlarge   InstanceType = "ml.c5.4xlarge"
	InstanceTypeMl_c5_9xlarge   InstanceType = "ml.c5.9xlarge"
	InstanceTypeMl_c5_18xlarge  InstanceType = "ml.c5.18xlarge"
	InstanceTypeMl_c5d_xlarge   InstanceType = "ml.c5d.xlarge"
	InstanceTypeMl_c5d_2xlarge  InstanceType = "ml.c5d.2xlarge"
	InstanceTypeMl_c5d_4xlarge  InstanceType = "ml.c5d.4xlarge"
	InstanceTypeMl_c5d_9xlarge  InstanceType = "ml.c5d.9xlarge"
	InstanceTypeMl_c5d_18xlarge InstanceType = "ml.c5d.18xlarge"
	InstanceTypeMl_p2_xlarge    InstanceType = "ml.p2.xlarge"
	InstanceTypeMl_p2_8xlarge   InstanceType = "ml.p2.8xlarge"
	InstanceTypeMl_p2_16xlarge  InstanceType = "ml.p2.16xlarge"
	InstanceTypeMl_p3_2xlarge   InstanceType = "ml.p3.2xlarge"
	InstanceTypeMl_p3_8xlarge   InstanceType = "ml.p3.8xlarge"
	InstanceTypeMl_p3_16xlarge  InstanceType = "ml.p3.16xlarge"
)

type IntegerParameterRange struct {

	// The maximum value of the hyperparameter to search.
	//
	// This member is required.
	MaxValue *string

	// The minimum value of the hyperparameter to search.
	//
	// This member is required.
	MinValue *string

	// The name of the hyperparameter to search.
	//
	// This member is required.
	Name *string

	// The scale that hyperparameter tuning uses to search the hyperparameter range.
	// For information about choosing a hyperparameter scale, see Hyperparameter
	// Scaling
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type).
	// One of the following values: Auto Amazon SageMaker hyperparameter tuning chooses
	// the best scale for the hyperparameter. Linear Hyperparameter tuning searches the
	// values in the hyperparameter range by using a linear scale. Logarithmic
	// Hyperparameter tuning searches the values in the hyperparameter range by using a
	// logarithmic scale. Logarithmic scaling works only for ranges that have only
	// values greater than 0.
	ScalingType HyperParameterScalingType
}

type IntegerParameterRangeSpecification struct {

	// The maximum integer value allowed.
	//
	// This member is required.
	MaxValue *string

	// The minimum integer value allowed.
	//
	// This member is required.
	MinValue *string
}

type JoinSource string

const (
	JoinSourceInput JoinSource = "Input"
	JoinSourceNone  JoinSource = "None"
)

type JupyterServerAppSettings struct {

	// The default instance type and the Amazon Resource Name (ARN) of the SageMaker
	// image created on the instance.
	DefaultResourceSpec *ResourceSpec
}

type KernelGatewayAppSettings struct {

	// The default instance type and the Amazon Resource Name (ARN) of the SageMaker
	// image created on the instance.
	DefaultResourceSpec *ResourceSpec
}

type LabelCounters struct {

	// The total number of objects that could not be labeled due to an error.
	FailedNonRetryableError *int32

	// The total number of objects labeled by a human worker.
	HumanLabeled *int32

	// The total number of objects labeled by automated data labeling.
	MachineLabeled *int32

	// The total number of objects labeled.
	TotalLabeled *int32

	// The total number of objects not yet labeled.
	Unlabeled *int32
}

type LabelCountersForWorkteam struct {

	// The total number of data objects labeled by a human worker.
	HumanLabeled *int32

	// The total number of data objects that need to be labeled by a human worker.
	PendingHuman *int32

	// The total number of tasks in the labeling job.
	Total *int32
}

type LabelingJobAlgorithmsConfig struct {

	// Specifies the Amazon Resource Name (ARN) of the algorithm used for
	// auto-labeling. You must select one of the following ARNs:
	//
	//     * Image
	// classification
	// arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification
	//
	//
	// * Text classification
	// arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification
	//
	//
	// * Object detection
	// arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection
	//
	//
	// * Semantic Segmentation
	// arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation
	//
	// This member is required.
	LabelingJobAlgorithmSpecificationArn *string

	// At the end of an auto-label job Amazon SageMaker Ground Truth sends the Amazon
	// Resource Nam (ARN) of the final model used for auto-labeling. You can use this
	// model as the starting point for subsequent similar jobs by providing the ARN of
	// the model here.
	InitialActiveLearningModelArn *string

	// Provides configuration information for a labeling job.
	LabelingJobResourceConfig *LabelingJobResourceConfig
}

type LabelingJobDataAttributes struct {

	// Declares that your content is free of personally identifiable information or
	// adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers
	// that can view your task based on this information.
	ContentClassifiers []ContentClassifier
}

type LabelingJobDataSource struct {

	// The Amazon S3 location of the input data objects.
	S3DataSource *LabelingJobS3DataSource
}

type LabelingJobForWorkteamSummary struct {

	// The date and time that the labeling job was created.
	//
	// This member is required.
	CreationTime *time.Time

	// A unique identifier for a labeling job. You can use this to refer to a specific
	// labeling job.
	//
	// This member is required.
	JobReferenceCode *string

	//
	//
	// This member is required.
	WorkRequesterAccountId *string

	// Provides information about the progress of a labeling job.
	LabelCounters *LabelCountersForWorkteam

	// The name of the labeling job that the work team is assigned to.
	LabelingJobName *string

	// The configured number of workers per data object.
	NumberOfHumanWorkersPerDataObject *int32
}

type LabelingJobInputConfig struct {

	// The location of the input data.
	//
	// This member is required.
	DataSource *LabelingJobDataSource

	// Attributes of the data specified by the customer.
	DataAttributes *LabelingJobDataAttributes
}

type LabelingJobOutput struct {

	// The Amazon S3 bucket location of the manifest file for labeled data.
	//
	// This member is required.
	OutputDatasetS3Uri *string

	// The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model
	// trained as part of automated data labeling.
	FinalActiveLearningModelArn *string
}

type LabelingJobOutputConfig struct {

	// The Amazon S3 location to write output data.
	//
	// This member is required.
	S3OutputPath *string

	// The AWS Key Management Service ID of the key used to encrypt the output data, if
	// any. If you use a KMS key ID or an alias of your master key, the Amazon
	// SageMaker execution role must include permissions to call kms:Encrypt. If you
	// don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon
	// S3 for your role's account. Amazon SageMaker uses server-side encryption with
	// KMS-managed keys for LabelingJobOutputConfig. If you use a bucket policy with an
	// s3:PutObject permission that only allows objects with server-side encryption,
	// set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more
	// information, see KMS-Managed Encryption Keys
	// (https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html) in the
	// Amazon Simple Storage Service Developer Guide. The KMS key policy must grant
	// permission to the IAM role that you specify in your CreateLabelingJob request.
	// For more information, see Using Key Policies in AWS KMS
	// (http://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html) in the
	// AWS Key Management Service Developer Guide.
	KmsKeyId *string
}

type LabelingJobResourceConfig struct {

	// The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to
	// encrypt data on the storage volume attached to the ML compute instance(s) that
	// run the training job. The VolumeKmsKeyId can be any of the following formats:
	//
	//
	// * // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
	//
	//     * // Amazon Resource
	// Name (ARN) of a KMS Key
	// "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
	VolumeKmsKeyId *string
}

type LabelingJobS3DataSource struct {

	// The Amazon S3 location of the manifest file that describes the input data
	// objects.
	//
	// This member is required.
	ManifestS3Uri *string
}

type LabelingJobStatus string

const (
	LabelingJobStatusInitializing LabelingJobStatus = "Initializing"
	LabelingJobStatusIn_progress  LabelingJobStatus = "InProgress"
	LabelingJobStatusCompleted    LabelingJobStatus = "Completed"
	LabelingJobStatusFailed       LabelingJobStatus = "Failed"
	LabelingJobStatusStopping     LabelingJobStatus = "Stopping"
	LabelingJobStatusStopped      LabelingJobStatus = "Stopped"
)

type LabelingJobStoppingConditions struct {

	// The maximum number of objects that can be labeled by human workers.
	MaxHumanLabeledObjectCount *int32

	// The maximum number of input data objects that should be labeled.
	MaxPercentageOfInputDatasetLabeled *int32
}

type LabelingJobSummary struct {

	// The date and time that the job was created (timestamp).
	//
	// This member is required.
	CreationTime *time.Time

	// Counts showing the progress of the labeling job.
	//
	// This member is required.
	LabelCounters *LabelCounters

	// The Amazon Resource Name (ARN) assigned to the labeling job when it was created.
	//
	// This member is required.
	LabelingJobArn *string

	// The name of the labeling job.
	//
	// This member is required.
	LabelingJobName *string

	// The current status of the labeling job.
	//
	// This member is required.
	LabelingJobStatus LabelingJobStatus

	// The date and time that the job was last modified (timestamp).
	//
	// This member is required.
	LastModifiedTime *time.Time

	// The Amazon Resource Name (ARN) of a Lambda function. The function is run before
	// each data object is sent to a worker.
	//
	// This member is required.
	PreHumanTaskLambdaArn *string

	// The Amazon Resource Name (ARN) of the work team assigned to the job.
	//
	// This member is required.
	WorkteamArn *string

	// The Amazon Resource Name (ARN) of the Lambda function used to consolidate the
	// annotations from individual workers into a label for a data object. For more
	// information, see Annotation Consolidation
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-annotation-consolidation.html).
	AnnotationConsolidationLambdaArn *string

	// If the LabelingJobStatus field is Failed, this field contains a description of
	// the error.
	FailureReason *string

	// Input configuration for the labeling job.
	InputConfig *LabelingJobInputConfig

	// The location of the output produced by the labeling job.
	LabelingJobOutput *LabelingJobOutput
}

type ListCompilationJobsSortBy string

const (
	ListCompilationJobsSortByName          ListCompilationJobsSortBy = "Name"
	ListCompilationJobsSortByCreation_time ListCompilationJobsSortBy = "CreationTime"
	ListCompilationJobsSortByStatus        ListCompilationJobsSortBy = "Status"
)

type ListLabelingJobsForWorkteamSortByOptions string

const (
	ListLabelingJobsForWorkteamSortByOptionsCreation_time ListLabelingJobsForWorkteamSortByOptions = "CreationTime"
)

type ListWorkforcesSortByOptions string

const (
	ListWorkforcesSortByOptionsName       ListWorkforcesSortByOptions = "Name"
	ListWorkforcesSortByOptionsCreatedate ListWorkforcesSortByOptions = "CreateDate"
)

type ListWorkteamsSortByOptions string

const (
	ListWorkteamsSortByOptionsName       ListWorkteamsSortByOptions = "Name"
	ListWorkteamsSortByOptionsCreatedate ListWorkteamsSortByOptions = "CreateDate"
)

type MemberDefinition struct {

	// The Amazon Cognito user group that is part of the work team.
	CognitoMemberDefinition *CognitoMemberDefinition

	// A list user groups that exist in your OIDC Identity Provider (IdP). One to ten
	// groups can be used to create a single private work team. When you add a user
	// group to the list of Groups, you can add that user group to one or more private
	// work teams. If you add a user group to a private work team, all workers in that
	// user group are added to the work team.
	OidcMemberDefinition *OidcMemberDefinition
}

type MetricData struct {

	// The name of the metric.
	MetricName *string

	// The date and time that the algorithm emitted the metric.
	Timestamp *time.Time

	// The value of the metric.
	Value *float32
}

type MetricDefinition struct {

	// The name of the metric.
	//
	// This member is required.
	Name *string

	// A regular expression that searches the output of a training job and gets the
	// value of the metric. For more information about using regular expressions to
	// define metrics, see Defining Objective Metrics
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics.html).
	//
	// This member is required.
	Regex *string
}

type ModelArtifacts struct {

	// The path of the S3 object that contains the model artifacts. For example,
	// s3://bucket-name/keynameprefix/model.tar.gz.
	//
	// This member is required.
	S3ModelArtifacts *string
}

type ModelClientConfig struct {

	// The maximum number of retries when invocation requests are failing.
	InvocationsMaxRetries *int32

	// The timeout value in seconds for an invocation request.
	InvocationsTimeoutInSeconds *int32
}

type ModelPackageContainerDefinition struct {

	// The Amazon EC2 Container Registry (Amazon ECR) path where inference code is
	// stored. If you are using your own custom algorithm instead of an algorithm
	// provided by Amazon SageMaker, the inference code must meet Amazon SageMaker
	// requirements. Amazon SageMaker supports both registry/repository[:tag] and
	// registry/repository[@digest] image path formats. For more information, see Using
	// Your Own Algorithms with Amazon SageMaker
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html).
	//
	// This member is required.
	Image *string

	// The DNS host name for the Docker container.
	ContainerHostname *string

	// An MD5 hash of the training algorithm that identifies the Docker image used for
	// training.
	ImageDigest *string

	// The Amazon S3 path where the model artifacts, which result from model training,
	// are stored. This path must point to a single gzip compressed tar archive
	// (.tar.gz suffix).
	ModelDataUrl *string

	// The AWS Marketplace product ID of the model package.
	ProductId *string
}

type ModelPackageSortBy string

const (
	ModelPackageSortByName          ModelPackageSortBy = "Name"
	ModelPackageSortByCreation_time ModelPackageSortBy = "CreationTime"
)

type ModelPackageStatus string

const (
	ModelPackageStatusPending     ModelPackageStatus = "Pending"
	ModelPackageStatusIn_progress ModelPackageStatus = "InProgress"
	ModelPackageStatusCompleted   ModelPackageStatus = "Completed"
	ModelPackageStatusFailed      ModelPackageStatus = "Failed"
	ModelPackageStatusDeleting    ModelPackageStatus = "Deleting"
)

type ModelPackageStatusDetails struct {

	// The validation status of the model package.
	//
	// This member is required.
	ValidationStatuses []*ModelPackageStatusItem

	// The status of the scan of the Docker image container for the model package.
	ImageScanStatuses []*ModelPackageStatusItem
}

type ModelPackageStatusItem struct {

	// The name of the model package for which the overall status is being reported.
	//
	// This member is required.
	Name *string

	// The current status.
	//
	// This member is required.
	Status DetailedModelPackageStatus

	// if the overall status is Failed, the reason for the failure.
	FailureReason *string
}

type ModelPackageSummary struct {

	// A timestamp that shows when the model package was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the model package.
	//
	// This member is required.
	ModelPackageArn *string

	// The name of the model package.
	//
	// This member is required.
	ModelPackageName *string

	// The overall status of the model package.
	//
	// This member is required.
	ModelPackageStatus ModelPackageStatus

	// A brief description of the model package.
	ModelPackageDescription *string
}

type ModelPackageValidationProfile struct {

	// The name of the profile for the model package.
	//
	// This member is required.
	ProfileName *string

	// The TransformJobDefinition object that describes the transform job used for the
	// validation of the model package.
	//
	// This member is required.
	TransformJobDefinition *TransformJobDefinition
}

type ModelPackageValidationSpecification struct {

	// An array of ModelPackageValidationProfile objects, each of which specifies a
	// batch transform job that Amazon SageMaker runs to validate your model package.
	//
	// This member is required.
	ValidationProfiles []*ModelPackageValidationProfile

	// The IAM roles to be used for the validation of the model package.
	//
	// This member is required.
	ValidationRole *string
}

type ModelSortKey string

const (
	ModelSortKeyName         ModelSortKey = "Name"
	ModelSortKeyCreationtime ModelSortKey = "CreationTime"
)

type ModelSummary struct {

	// A timestamp that indicates when the model was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the model.
	//
	// This member is required.
	ModelArn *string

	// The name of the model that you want a summary for.
	//
	// This member is required.
	ModelName *string
}

type MonitoringAppSpecification struct {

	// The container image to be run by the monitoring job.
	//
	// This member is required.
	ImageUri *string

	// An array of arguments for the container used to run the monitoring job.
	ContainerArguments []*string

	// Specifies the entrypoint for a container used to run the monitoring job.
	ContainerEntrypoint []*string

	// An Amazon S3 URI to a script that is called after analysis has been performed.
	// Applicable only for the built-in (first party) containers.
	PostAnalyticsProcessorSourceUri *string

	// An Amazon S3 URI to a script that is called per row prior to running analysis.
	// It can base64 decode the payload and convert it into a flatted json so that the
	// built-in container can use the converted data. Applicable only for the built-in
	// (first party) containers.
	RecordPreprocessorSourceUri *string
}

type MonitoringBaselineConfig struct {

	// The baseline constraint file in Amazon S3 that the current monitoring job should
	// validated against.
	ConstraintsResource *MonitoringConstraintsResource

	// The baseline statistics file in Amazon S3 that the current monitoring job should
	// be validated against.
	StatisticsResource *MonitoringStatisticsResource
}

type MonitoringClusterConfig struct {

	// The number of ML compute instances to use in the model monitoring job. For
	// distributed processing jobs, specify a value greater than 1. The default value
	// is 1.
	//
	// This member is required.
	InstanceCount *int32

	// The ML compute instance type for the processing job.
	//
	// This member is required.
	InstanceType ProcessingInstanceType

	// The size of the ML storage volume, in gigabytes, that you want to provision. You
	// must specify sufficient ML storage for your scenario.
	//
	// This member is required.
	VolumeSizeInGB *int32

	// The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to
	// encrypt data on the storage volume attached to the ML compute instance(s) that
	// run the model monitoring job.
	VolumeKmsKeyId *string
}

type MonitoringConstraintsResource struct {

	// The Amazon S3 URI for the constraints resource.
	S3Uri *string
}

type MonitoringExecutionSortKey string

const (
	MonitoringExecutionSortKeyCreation_time  MonitoringExecutionSortKey = "CreationTime"
	MonitoringExecutionSortKeyScheduled_time MonitoringExecutionSortKey = "ScheduledTime"
	MonitoringExecutionSortKeyStatus         MonitoringExecutionSortKey = "Status"
)

type MonitoringExecutionSummary struct {

	// The time at which the monitoring job was created.
	//
	// This member is required.
	CreationTime *time.Time

	// A timestamp that indicates the last time the monitoring job was modified.
	//
	// This member is required.
	LastModifiedTime *time.Time

	// The status of the monitoring job.
	//
	// This member is required.
	MonitoringExecutionStatus ExecutionStatus

	// The name of the monitoring schedule.
	//
	// This member is required.
	MonitoringScheduleName *string

	// The time the monitoring job was scheduled.
	//
	// This member is required.
	ScheduledTime *time.Time

	// The name of teh endpoint used to run the monitoring job.
	EndpointName *string

	// Contains the reason a monitoring job failed, if it failed.
	FailureReason *string

	// The Amazon Resource Name (ARN) of the monitoring job.
	ProcessingJobArn *string
}

type MonitoringInput struct {

	// The endpoint for a monitoring job.
	//
	// This member is required.
	EndpointInput *EndpointInput
}

type MonitoringJobDefinition struct {

	// Configures the monitoring job to run a specified Docker container image.
	//
	// This member is required.
	MonitoringAppSpecification *MonitoringAppSpecification

	// The array of inputs for the monitoring job. Currently we support monitoring an
	// Amazon SageMaker Endpoint.
	//
	// This member is required.
	MonitoringInputs []*MonitoringInput

	// The array of outputs from the monitoring job to be uploaded to Amazon Simple
	// Storage Service (Amazon S3).
	//
	// This member is required.
	MonitoringOutputConfig *MonitoringOutputConfig

	// Identifies the resources, ML compute instances, and ML storage volumes to deploy
	// for a monitoring job. In distributed processing, you specify more than one
	// instance.
	//
	// This member is required.
	MonitoringResources *MonitoringResources

	// The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume
	// to perform tasks on your behalf.
	//
	// This member is required.
	RoleArn *string

	// Baseline configuration used to validate that the data conforms to the specified
	// constraints and statistics
	BaselineConfig *MonitoringBaselineConfig

	// Sets the environment variables in the Docker container.
	Environment map[string]*string

	// Specifies networking options for an monitoring job.
	NetworkConfig *NetworkConfig

	// Specifies a time limit for how long the monitoring job is allowed to run.
	StoppingCondition *MonitoringStoppingCondition
}

type MonitoringOutput struct {

	// The Amazon S3 storage location where the results of a monitoring job are saved.
	//
	// This member is required.
	S3Output *MonitoringS3Output
}

type MonitoringOutputConfig struct {

	// Monitoring outputs for monitoring jobs. This is where the output of the periodic
	// monitoring jobs is uploaded.
	//
	// This member is required.
	MonitoringOutputs []*MonitoringOutput

	// The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to
	// encrypt the model artifacts at rest using Amazon S3 server-side encryption.
	KmsKeyId *string
}

type MonitoringResources struct {

	// The configuration for the cluster resources used to run the processing job.
	//
	// This member is required.
	ClusterConfig *MonitoringClusterConfig
}

type MonitoringS3Output struct {

	// The local path to the Amazon S3 storage location where Amazon SageMaker saves
	// the results of a monitoring job. LocalPath is an absolute path for the output
	// data.
	//
	// This member is required.
	LocalPath *string

	// A URI that identifies the Amazon S3 storage location where Amazon SageMaker
	// saves the results of a monitoring job.
	//
	// This member is required.
	S3Uri *string

	// Whether to upload the results of the monitoring job continuously or after the
	// job completes.
	S3UploadMode ProcessingS3UploadMode
}

type MonitoringScheduleConfig struct {

	// Defines the monitoring job.
	//
	// This member is required.
	MonitoringJobDefinition *MonitoringJobDefinition

	// Configures the monitoring schedule.
	ScheduleConfig *ScheduleConfig
}

type MonitoringScheduleSortKey string

const (
	MonitoringScheduleSortKeyName          MonitoringScheduleSortKey = "Name"
	MonitoringScheduleSortKeyCreation_time MonitoringScheduleSortKey = "CreationTime"
	MonitoringScheduleSortKeyStatus        MonitoringScheduleSortKey = "Status"
)

type MonitoringScheduleSummary struct {

	// The creation time of the monitoring schedule.
	//
	// This member is required.
	CreationTime *time.Time

	// The last time the monitoring schedule was modified.
	//
	// This member is required.
	LastModifiedTime *time.Time

	// The Amazon Resource Name (ARN) of the monitoring schedule.
	//
	// This member is required.
	MonitoringScheduleArn *string

	// The name of the monitoring schedule.
	//
	// This member is required.
	MonitoringScheduleName *string

	// The status of the monitoring schedule.
	//
	// This member is required.
	MonitoringScheduleStatus ScheduleStatus

	// The name of the endpoint using the monitoring schedule.
	EndpointName *string
}

type MonitoringStatisticsResource struct {

	// The Amazon S3 URI for the statistics resource.
	S3Uri *string
}

type MonitoringStoppingCondition struct {

	// The maximum runtime allowed in seconds.
	//
	// This member is required.
	MaxRuntimeInSeconds *int32
}

type NestedFilters struct {

	// A list of filters. Each filter acts on a property. Filters must contain at least
	// one Filters value. For example, a NestedFilters call might include a filter on
	// the PropertyName parameter of the InputDataConfig property:
	// InputDataConfig.DataSource.S3DataSource.S3Uri.
	//
	// This member is required.
	Filters []*Filter

	// The name of the property to use in the nested filters. The value must match a
	// listed property name, such as InputDataConfig.
	//
	// This member is required.
	NestedPropertyName *string
}

type NetworkConfig struct {

	// Whether to encrypt all communications between distributed processing jobs.
	// Choose True to encrypt communications. Encryption provides greater security for
	// distributed processing jobs, but the processing might take longer.
	EnableInterContainerTrafficEncryption *bool

	// Whether to allow inbound and outbound network calls to and from the containers
	// used for the processing job.
	EnableNetworkIsolation *bool

	// Specifies a VPC that your training jobs and hosted models have access to.
	// Control access to and from your training and model containers by configuring the
	// VPC. 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 Training Jobs by Using an Amazon Virtual Private Cloud
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html).
	VpcConfig *VpcConfig
}

type NotebookInstanceAcceleratorType string

const (
	NotebookInstanceAcceleratorTypeMl_eia1_medium NotebookInstanceAcceleratorType = "ml.eia1.medium"
	NotebookInstanceAcceleratorTypeMl_eia1_large  NotebookInstanceAcceleratorType = "ml.eia1.large"
	NotebookInstanceAcceleratorTypeMl_eia1_xlarge NotebookInstanceAcceleratorType = "ml.eia1.xlarge"
	NotebookInstanceAcceleratorTypeMl_eia2_medium NotebookInstanceAcceleratorType = "ml.eia2.medium"
	NotebookInstanceAcceleratorTypeMl_eia2_large  NotebookInstanceAcceleratorType = "ml.eia2.large"
	NotebookInstanceAcceleratorTypeMl_eia2_xlarge NotebookInstanceAcceleratorType = "ml.eia2.xlarge"
)

type NotebookInstanceLifecycleConfigSortKey string

const (
	NotebookInstanceLifecycleConfigSortKeyName               NotebookInstanceLifecycleConfigSortKey = "Name"
	NotebookInstanceLifecycleConfigSortKeyCreation_time      NotebookInstanceLifecycleConfigSortKey = "CreationTime"
	NotebookInstanceLifecycleConfigSortKeyLast_modified_time NotebookInstanceLifecycleConfigSortKey = "LastModifiedTime"
)

type NotebookInstanceLifecycleConfigSortOrder string

const (
	NotebookInstanceLifecycleConfigSortOrderAscending  NotebookInstanceLifecycleConfigSortOrder = "Ascending"
	NotebookInstanceLifecycleConfigSortOrderDescending NotebookInstanceLifecycleConfigSortOrder = "Descending"
)

type NotebookInstanceLifecycleConfigSummary struct {

	// The Amazon Resource Name (ARN) of the lifecycle configuration.
	//
	// This member is required.
	NotebookInstanceLifecycleConfigArn *string

	// The name of the lifecycle configuration.
	//
	// This member is required.
	NotebookInstanceLifecycleConfigName *string

	// A timestamp that tells when the lifecycle configuration was created.
	CreationTime *time.Time

	// A timestamp that tells when the lifecycle configuration was last modified.
	LastModifiedTime *time.Time
}

type NotebookInstanceLifecycleHook struct {

	// A base64-encoded string that contains a shell script for a notebook instance
	// lifecycle configuration.
	Content *string
}

type NotebookInstanceSortKey string

const (
	NotebookInstanceSortKeyName          NotebookInstanceSortKey = "Name"
	NotebookInstanceSortKeyCreation_time NotebookInstanceSortKey = "CreationTime"
	NotebookInstanceSortKeyStatus        NotebookInstanceSortKey = "Status"
)

type NotebookInstanceSortOrder string

const (
	NotebookInstanceSortOrderAscending  NotebookInstanceSortOrder = "Ascending"
	NotebookInstanceSortOrderDescending NotebookInstanceSortOrder = "Descending"
)

type NotebookInstanceStatus string

const (
	NotebookInstanceStatusPending   NotebookInstanceStatus = "Pending"
	NotebookInstanceStatusInservice NotebookInstanceStatus = "InService"
	NotebookInstanceStatusStopping  NotebookInstanceStatus = "Stopping"
	NotebookInstanceStatusStopped   NotebookInstanceStatus = "Stopped"
	NotebookInstanceStatusFailed    NotebookInstanceStatus = "Failed"
	NotebookInstanceStatusDeleting  NotebookInstanceStatus = "Deleting"
	NotebookInstanceStatusUpdating  NotebookInstanceStatus = "Updating"
)

type NotebookInstanceSummary struct {

	// The Amazon Resource Name (ARN) of the notebook instance.
	//
	// This member is required.
	NotebookInstanceArn *string

	// The name of the notebook instance that you want a summary for.
	//
	// This member is required.
	NotebookInstanceName *string

	// An array of up to three Git repositories associated with the notebook instance.
	// These can be either the names of Git repositories stored as resources in your
	// account, or the URL of Git repositories in AWS CodeCommit
	// (https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html) or in any
	// other Git repository. These repositories are cloned at the same level as the
	// default repository of your notebook instance. For more information, see
	// Associating Git Repositories with Amazon SageMaker Notebook Instances
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html).
	AdditionalCodeRepositories []*string

	// A timestamp that shows when the notebook instance was created.
	CreationTime *time.Time

	// The Git repository associated with the notebook instance as its default code
	// repository. This can be either the name of a Git repository stored as a resource
	// in your account, or the URL of a Git repository in AWS CodeCommit
	// (https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html) or in any
	// other Git repository. When you open a notebook instance, it opens in the
	// directory that contains this repository. For more information, see Associating
	// Git Repositories with Amazon SageMaker Notebook Instances
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html).
	DefaultCodeRepository *string

	// The type of ML compute instance that the notebook instance is running on.
	InstanceType InstanceType

	// A timestamp that shows when the notebook instance was last modified.
	LastModifiedTime *time.Time

	// The name of a notebook instance lifecycle configuration associated with this
	// notebook instance. For information about notebook instance lifestyle
	// configurations, see Step 2.1: (Optional) Customize a Notebook Instance
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html).
	NotebookInstanceLifecycleConfigName *string

	// The status of the notebook instance.
	NotebookInstanceStatus NotebookInstanceStatus

	// The URL that you use to connect to the Jupyter instance running in your notebook
	// instance.
	Url *string
}

type NotebookOutputOption string

const (
	NotebookOutputOptionAllowed  NotebookOutputOption = "Allowed"
	NotebookOutputOptionDisabled NotebookOutputOption = "Disabled"
)

type NotificationConfiguration struct {

	// The ARN for the SNS topic to which notifications should be published.
	NotificationTopicArn *string
}

type ObjectiveStatus string

const (
	ObjectiveStatusSucceeded ObjectiveStatus = "Succeeded"
	ObjectiveStatusPending   ObjectiveStatus = "Pending"
	ObjectiveStatusFailed    ObjectiveStatus = "Failed"
)

type ObjectiveStatusCounters struct {

	// The number of training jobs whose final objective metric was not evaluated and
	// used in the hyperparameter tuning process. This typically occurs when the
	// training job failed or did not emit an objective metric.
	Failed *int32

	// The number of training jobs that are in progress and pending evaluation of their
	// final objective metric.
	Pending *int32

	// The number of training jobs whose final objective metric was evaluated by the
	// hyperparameter tuning job and used in the hyperparameter tuning process.
	Succeeded *int32
}

type OidcConfig struct {

	// The OIDC IdP authorization endpoint used to configure your private workforce.
	//
	// This member is required.
	AuthorizationEndpoint *string

	// The OIDC IdP client ID used to configure your private workforce.
	//
	// This member is required.
	ClientId *string

	// The OIDC IdP client secret used to configure your private workforce.
	//
	// This member is required.
	ClientSecret *string

	// The OIDC IdP issuer used to configure your private workforce.
	//
	// This member is required.
	Issuer *string

	// The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private
	// workforce.
	//
	// This member is required.
	JwksUri *string

	// The OIDC IdP logout endpoint used to configure your private workforce.
	//
	// This member is required.
	LogoutEndpoint *string

	// The OIDC IdP token endpoint used to configure your private workforce.
	//
	// This member is required.
	TokenEndpoint *string

	// The OIDC IdP user information endpoint used to configure your private workforce.
	//
	// This member is required.
	UserInfoEndpoint *string
}

type OidcConfigForResponse struct {

	// The OIDC IdP authorization endpoint used to configure your private workforce.
	AuthorizationEndpoint *string

	// The OIDC IdP client ID used to configure your private workforce.
	ClientId *string

	// The OIDC IdP issuer used to configure your private workforce.
	Issuer *string

	// The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private
	// workforce.
	JwksUri *string

	// The OIDC IdP logout endpoint used to configure your private workforce.
	LogoutEndpoint *string

	// The OIDC IdP token endpoint used to configure your private workforce.
	TokenEndpoint *string

	// The OIDC IdP user information endpoint used to configure your private workforce.
	UserInfoEndpoint *string
}

type OidcMemberDefinition struct {

	// A list of comma seperated strings that identifies user groups in your OIDC IdP.
	// Each user group is made up of a group of private workers.
	//
	// This member is required.
	Groups []*string
}

type Operator string

const (
	OperatorEquals                   Operator = "Equals"
	OperatorNot_equals               Operator = "NotEquals"
	OperatorGreater_than             Operator = "GreaterThan"
	OperatorGreater_than_or_equal_to Operator = "GreaterThanOrEqualTo"
	OperatorLess_than                Operator = "LessThan"
	OperatorLess_than_or_equal_to    Operator = "LessThanOrEqualTo"
	OperatorContains                 Operator = "Contains"
	OperatorExists                   Operator = "Exists"
	OperatorNot_exists               Operator = "NotExists"
	OperatorIn                       Operator = "In"
)

type OrderKey string

const (
	OrderKeyAscending  OrderKey = "Ascending"
	OrderKeyDescending OrderKey = "Descending"
)

type OutputConfig struct {

	// Identifies the S3 bucket where you want Amazon SageMaker to store the model
	// artifacts. For example, s3://bucket-name/key-name-prefix.
	//
	// This member is required.
	S3OutputLocation *string

	// Specifies additional parameters for compiler options in JSON format. The
	// compiler options are TargetPlatform specific. It is required for NVIDIA
	// accelerators and highly recommended for CPU compliations. For any other cases,
	// it is optional to specify CompilerOptions.
	//
	//     * CPU: Compilation for CPU
	// supports the following compiler options.
	//
	//         * mcpu: CPU
	// micro-architecture. For example, {'mcpu': 'skylake-avx512'}
	//
	//         * mattr:
	// CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
	//
	//     * ARM: Details of
	// ARM CPU compilations.
	//
	//         * NEON: NEON is an implementation of the Advanced
	// SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']}
	// to the compiler options if compiling for ARM 32-bit platform with the NEON
	// support.
	//
	//     * NVIDIA: Compilation for NVIDIA GPU supports the following
	// compiler options.
	//
	//         * gpu_code: Specifies the targeted architecture.
	//
	//
	// * trt-ver: Specifies the TensorRT versions in x.y.z. format.
	//
	//         *
	// cuda-ver: Specifies the CUDA version in x.y format.
	//
	//     For example,
	// {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
	//
	//     * ANDROID:
	// Compilation for the Android OS supports the following compiler options:
	//
	//
	// * ANDROID_PLATFORM: Specifies the Android API levels. Available levels range
	// from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.
	//
	//         * mattr: Add
	// {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform
	// with NEON support.
	CompilerOptions *string

	// Identifies the target device or the machine learning instance that you want to
	// run your model on after the compilation has completed. Alternatively, you can
	// specify OS, architecture, and accelerator using TargetPlatform fields. It can be
	// used instead of TargetPlatform.
	TargetDevice TargetDevice

	// Contains information about a target platform that you want your model to run on,
	// such as OS, architecture, and accelerators. It is an alternative of
	// TargetDevice. The following examples show how to configure the TargetPlatform
	// and CompilerOptions JSON strings for popular target platforms:
	//
	//     * Raspberry
	// Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
	// "CompilerOptions": {'mattr': ['+neon']}
	//
	//     * Jetson TX2 "TargetPlatform":
	// {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}, "CompilerOptions":
	// {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
	//
	//     * EC2
	// m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64",
	// "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': 'skylake-avx512'}
	//
	//     *
	// RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator":
	// "MALI"}
	//
	//     * ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch":
	// "ARM_EABI"}, "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
	//
	//
	// * ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
	// "CompilerOptions": {'ANDROID_PLATFORM': 29}
	TargetPlatform *TargetPlatform
}

type OutputDataConfig struct {

	// Identifies the S3 path where you want Amazon SageMaker to store the model
	// artifacts. For example, s3://bucket-name/key-name-prefix.
	//
	// This member is required.
	S3OutputPath *string

	// The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to
	// encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
	// KmsKeyId can be any of the following formats:
	//
	//     * // KMS Key ID
	// "1234abcd-12ab-34cd-56ef-1234567890ab"
	//
	//     * // Amazon Resource Name (ARN) of a
	// KMS Key
	// "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
	//
	//
	// * // KMS Key Alias "alias/ExampleAlias"
	//
	//     * // Amazon Resource Name (ARN) of
	// a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
	//
	// If you
	// use a KMS key ID or an alias of your master key, the Amazon SageMaker execution
	// role must include permissions to call kms:Encrypt. If you don't provide a KMS
	// key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's
	// account. Amazon SageMaker uses server-side encryption with KMS-managed keys for
	// OutputDataConfig. If you use a bucket policy with an s3:PutObject permission
	// that only allows objects with server-side encryption, set the condition key of
	// s3:x-amz-server-side-encryption to "aws:kms". For more information, see
	// KMS-Managed Encryption Keys
	// (https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html) in the
	// Amazon Simple Storage Service Developer Guide. The KMS key policy must grant
	// permission to the IAM role that you specify in your CreateTrainingJob,
	// CreateTransformJob, or CreateHyperParameterTuningJob requests. For more
	// information, see Using Key Policies in AWS KMS
	// (http://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html) in the
	// AWS Key Management Service Developer Guide.
	KmsKeyId *string
}

type ParameterRange struct {

	// A CategoricalParameterRangeSpecification object that defines the possible values
	// for a categorical hyperparameter.
	CategoricalParameterRangeSpecification *CategoricalParameterRangeSpecification

	// A ContinuousParameterRangeSpecification object that defines the possible values
	// for a continuous hyperparameter.
	ContinuousParameterRangeSpecification *ContinuousParameterRangeSpecification

	// A IntegerParameterRangeSpecification object that defines the possible values for
	// an integer hyperparameter.
	IntegerParameterRangeSpecification *IntegerParameterRangeSpecification
}

type ParameterRanges struct {

	// The array of CategoricalParameterRange objects that specify ranges of
	// categorical hyperparameters that a hyperparameter tuning job searches.
	CategoricalParameterRanges []*CategoricalParameterRange

	// The array of ContinuousParameterRange objects that specify ranges of continuous
	// hyperparameters that a hyperparameter tuning job searches.
	ContinuousParameterRanges []*ContinuousParameterRange

	// The array of IntegerParameterRange objects that specify ranges of integer
	// hyperparameters that a hyperparameter tuning job searches.
	IntegerParameterRanges []*IntegerParameterRange
}

type ParameterType string

const (
	ParameterTypeInteger     ParameterType = "Integer"
	ParameterTypeContinuous  ParameterType = "Continuous"
	ParameterTypeCategorical ParameterType = "Categorical"
	ParameterTypeFree_text   ParameterType = "FreeText"
)

type Parent struct {

	// The name of the experiment.
	ExperimentName *string

	// The name of the trial.
	TrialName *string
}

type ParentHyperParameterTuningJob struct {

	// The name of the hyperparameter tuning job to be used as a starting point for a
	// new hyperparameter tuning job.
	HyperParameterTuningJobName *string
}

type ProblemType string

const (
	ProblemTypeBinary_classification     ProblemType = "BinaryClassification"
	ProblemTypeMulticlass_classification ProblemType = "MulticlassClassification"
	ProblemTypeRegression                ProblemType = "Regression"
)

type ProcessingClusterConfig struct {

	// The number of ML compute instances to use in the processing job. For distributed
	// processing jobs, specify a value greater than 1. The default value is 1.
	//
	// This member is required.
	InstanceCount *int32

	// The ML compute instance type for the processing job.
	//
	// This member is required.
	InstanceType ProcessingInstanceType

	// The size of the ML storage volume in gigabytes that you want to provision. You
	// must specify sufficient ML storage for your scenario.
	//
	// This member is required.
	VolumeSizeInGB *int32

	// The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to
	// encrypt data on the storage volume attached to the ML compute instance(s) that
	// run the processing job.
	VolumeKmsKeyId *string
}

type ProcessingInput struct {

	// The name of the inputs for the processing job.
	//
	// This member is required.
	InputName *string

	// The S3 inputs for the processing job.
	//
	// This member is required.
	S3Input *ProcessingS3Input
}

type ProcessingInstanceType string

const (
	ProcessingInstanceTypeMl_t3_medium   ProcessingInstanceType = "ml.t3.medium"
	ProcessingInstanceTypeMl_t3_large    ProcessingInstanceType = "ml.t3.large"
	ProcessingInstanceTypeMl_t3_xlarge   ProcessingInstanceType = "ml.t3.xlarge"
	ProcessingInstanceTypeMl_t3_2xlarge  ProcessingInstanceType = "ml.t3.2xlarge"
	ProcessingInstanceTypeMl_m4_xlarge   ProcessingInstanceType = "ml.m4.xlarge"
	ProcessingInstanceTypeMl_m4_2xlarge  ProcessingInstanceType = "ml.m4.2xlarge"
	ProcessingInstanceTypeMl_m4_4xlarge  ProcessingInstanceType = "ml.m4.4xlarge"
	ProcessingInstanceTypeMl_m4_10xlarge ProcessingInstanceType = "ml.m4.10xlarge"
	ProcessingInstanceTypeMl_m4_16xlarge ProcessingInstanceType = "ml.m4.16xlarge"
	ProcessingInstanceTypeMl_c4_xlarge   ProcessingInstanceType = "ml.c4.xlarge"
	ProcessingInstanceTypeMl_c4_2xlarge  ProcessingInstanceType = "ml.c4.2xlarge"
	ProcessingInstanceTypeMl_c4_4xlarge  ProcessingInstanceType = "ml.c4.4xlarge"
	ProcessingInstanceTypeMl_c4_8xlarge  ProcessingInstanceType = "ml.c4.8xlarge"
	ProcessingInstanceTypeMl_p2_xlarge   ProcessingInstanceType = "ml.p2.xlarge"
	ProcessingInstanceTypeMl_p2_8xlarge  ProcessingInstanceType = "ml.p2.8xlarge"
	ProcessingInstanceTypeMl_p2_16xlarge ProcessingInstanceType = "ml.p2.16xlarge"
	ProcessingInstanceTypeMl_p3_2xlarge  ProcessingInstanceType = "ml.p3.2xlarge"
	ProcessingInstanceTypeMl_p3_8xlarge  ProcessingInstanceType = "ml.p3.8xlarge"
	ProcessingInstanceTypeMl_p3_16xlarge ProcessingInstanceType = "ml.p3.16xlarge"
	ProcessingInstanceTypeMl_c5_xlarge   ProcessingInstanceType = "ml.c5.xlarge"
	ProcessingInstanceTypeMl_c5_2xlarge  ProcessingInstanceType = "ml.c5.2xlarge"
	ProcessingInstanceTypeMl_c5_4xlarge  ProcessingInstanceType = "ml.c5.4xlarge"
	ProcessingInstanceTypeMl_c5_9xlarge  ProcessingInstanceType = "ml.c5.9xlarge"
	ProcessingInstanceTypeMl_c5_18xlarge ProcessingInstanceType = "ml.c5.18xlarge"
	ProcessingInstanceTypeMl_m5_large    ProcessingInstanceType = "ml.m5.large"
	ProcessingInstanceTypeMl_m5_xlarge   ProcessingInstanceType = "ml.m5.xlarge"
	ProcessingInstanceTypeMl_m5_2xlarge  ProcessingInstanceType = "ml.m5.2xlarge"
	ProcessingInstanceTypeMl_m5_4xlarge  ProcessingInstanceType = "ml.m5.4xlarge"
	ProcessingInstanceTypeMl_m5_12xlarge ProcessingInstanceType = "ml.m5.12xlarge"
	ProcessingInstanceTypeMl_m5_24xlarge ProcessingInstanceType = "ml.m5.24xlarge"
	ProcessingInstanceTypeMl_r5_large    ProcessingInstanceType = "ml.r5.large"
	ProcessingInstanceTypeMl_r5_xlarge   ProcessingInstanceType = "ml.r5.xlarge"
	ProcessingInstanceTypeMl_r5_2xlarge  ProcessingInstanceType = "ml.r5.2xlarge"
	ProcessingInstanceTypeMl_r5_4xlarge  ProcessingInstanceType = "ml.r5.4xlarge"
	ProcessingInstanceTypeMl_r5_8xlarge  ProcessingInstanceType = "ml.r5.8xlarge"
	ProcessingInstanceTypeMl_r5_12xlarge ProcessingInstanceType = "ml.r5.12xlarge"
	ProcessingInstanceTypeMl_r5_16xlarge ProcessingInstanceType = "ml.r5.16xlarge"
	ProcessingInstanceTypeMl_r5_24xlarge ProcessingInstanceType = "ml.r5.24xlarge"
)

type ProcessingJob struct {

	// Configuration to run a processing job in a specified container image.
	AppSpecification *AppSpecification

	// The Amazon Resource Name (ARN) of the AutoML job associated with this processing
	// job.
	AutoMLJobArn *string

	// The time the processing job was created.
	CreationTime *time.Time

	// Sets the environment variables in the Docker container.
	Environment map[string]*string

	// A string, up to one KB in size, that contains metadata from the processing
	// container when the processing job exits.
	ExitMessage *string

	// Associates a SageMaker job as a trial component with an experiment and trial.
	// Specified when you call the following APIs:
	//
	//     * CreateProcessingJob
	//
	//     *
	// CreateTrainingJob
	//
	//     * CreateTransformJob
	ExperimentConfig *ExperimentConfig

	// A string, up to one KB in size, that contains the reason a processing job
	// failed, if it failed.
	FailureReason *string

	// The time the processing job was last modified.
	LastModifiedTime *time.Time

	// The ARN of a monitoring schedule for an endpoint associated with this processing
	// job.
	MonitoringScheduleArn *string

	// Networking options for a job, such as network traffic encryption between
	// containers, whether to allow inbound and outbound network calls to and from
	// containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
	NetworkConfig *NetworkConfig

	// The time that the processing job ended.
	ProcessingEndTime *time.Time

	// For each input, data is downloaded from S3 into the processing container before
	// the processing job begins running if "S3InputMode" is set to File.
	ProcessingInputs []*ProcessingInput

	// The ARN of the processing job.
	ProcessingJobArn *string

	// The name of the processing job.
	ProcessingJobName *string

	// The status of the processing job.
	ProcessingJobStatus ProcessingJobStatus

	// The output configuration for the processing job.
	ProcessingOutputConfig *ProcessingOutputConfig

	// Identifies the resources, ML compute instances, and ML storage volumes to deploy
	// for a processing job. In distributed training, you specify more than one
	// instance.
	ProcessingResources *ProcessingResources

	// The time that the processing job started.
	ProcessingStartTime *time.Time

	// The ARN of the role used to create the processing job.
	RoleArn *string

	// Specifies a time limit for how long the processing job is allowed to run.
	StoppingCondition *ProcessingStoppingCondition

	// An array of key-value pairs. For more information, see Using Cost Allocation
	// Tags
	// (https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL)
	// in the AWS Billing and Cost Management User Guide.
	Tags []*Tag

	// The ARN of the training job associated with this processing job.
	TrainingJobArn *string
}

type ProcessingJobStatus string

const (
	ProcessingJobStatusIn_progress ProcessingJobStatus = "InProgress"
	ProcessingJobStatusCompleted   ProcessingJobStatus = "Completed"
	ProcessingJobStatusFailed      ProcessingJobStatus = "Failed"
	ProcessingJobStatusStopping    ProcessingJobStatus = "Stopping"
	ProcessingJobStatusStopped     ProcessingJobStatus = "Stopped"
)

type ProcessingJobSummary struct {

	// The time at which the processing job was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the processing job..
	//
	// This member is required.
	ProcessingJobArn *string

	// The name of the processing job.
	//
	// This member is required.
	ProcessingJobName *string

	// The status of the processing job.
	//
	// This member is required.
	ProcessingJobStatus ProcessingJobStatus

	// An optional string, up to one KB in size, that contains metadata from the
	// processing container when the processing job exits.
	ExitMessage *string

	// A string, up to one KB in size, that contains the reason a processing job
	// failed, if it failed.
	FailureReason *string

	// A timestamp that indicates the last time the processing job was modified.
	LastModifiedTime *time.Time

	// The time at which the processing job completed.
	ProcessingEndTime *time.Time
}

type ProcessingOutput struct {

	// The name for the processing job output.
	//
	// This member is required.
	OutputName *string

	// Configuration for processing job outputs in Amazon S3.
	//
	// This member is required.
	S3Output *ProcessingS3Output
}

type ProcessingOutputConfig struct {

	// Output configuration information for a processing job.
	//
	// This member is required.
	Outputs []*ProcessingOutput

	// The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to
	// encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a
	// KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to
	// all outputs.
	KmsKeyId *string
}

type ProcessingResources struct {

	// The configuration for the resources in a cluster used to run the processing job.
	//
	// This member is required.
	ClusterConfig *ProcessingClusterConfig
}

type ProcessingS3CompressionType string

const (
	ProcessingS3CompressionTypeNone ProcessingS3CompressionType = "None"
	ProcessingS3CompressionTypeGzip ProcessingS3CompressionType = "Gzip"
)

type ProcessingS3DataDistributionType string

const (
	ProcessingS3DataDistributionTypeFullyreplicated ProcessingS3DataDistributionType = "FullyReplicated"
	ProcessingS3DataDistributionTypeShardedbys3key  ProcessingS3DataDistributionType = "ShardedByS3Key"
)

type ProcessingS3DataType string

const (
	ProcessingS3DataTypeManifest_file ProcessingS3DataType = "ManifestFile"
	ProcessingS3DataTypeS3_prefix     ProcessingS3DataType = "S3Prefix"
)

type ProcessingS3Input struct {

	// The local path to the Amazon S3 bucket where you want Amazon SageMaker to
	// download the inputs to run a processing job. LocalPath is an absolute path to
	// the input data.
	//
	// This member is required.
	LocalPath *string

	// Whether you use an S3Prefix or a ManifestFile for the data type. If you choose
	// S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects
	// with the specified key name prefix for the processing job. If you choose
	// ManifestFile, S3Uri identifies an object that is a manifest file containing a
	// list of object keys that you want Amazon SageMaker to use for the processing
	// job.
	//
	// This member is required.
	S3DataType ProcessingS3DataType

	// Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies
	// the data from the input source onto the local Amazon Elastic Block Store (Amazon
	// EBS) volumes before starting your training algorithm. This is the most commonly
	// used input mode. In Pipe mode, Amazon SageMaker streams input data from the
	// source directly to your algorithm without using the EBS volume.
	//
	// This member is required.
	S3InputMode ProcessingS3InputMode

	// The URI for the Amazon S3 storage where you want Amazon SageMaker to download
	// the artifacts needed to run a processing job.
	//
	// This member is required.
	S3Uri *string

	// Whether to use Gzip compression for Amazon S3 storage.
	S3CompressionType ProcessingS3CompressionType

	// Whether the data stored in Amazon S3 is FullyReplicated or ShardedByS3Key.
	S3DataDistributionType ProcessingS3DataDistributionType
}

type ProcessingS3InputMode string

const (
	ProcessingS3InputModePipe ProcessingS3InputMode = "Pipe"
	ProcessingS3InputModeFile ProcessingS3InputMode = "File"
)

type ProcessingS3Output struct {

	// The local path to the Amazon S3 bucket where you want Amazon SageMaker to save
	// the results of an processing job. LocalPath is an absolute path to the input
	// data.
	//
	// This member is required.
	LocalPath *string

	// Whether to upload the results of the processing job continuously or after the
	// job completes.
	//
	// This member is required.
	S3UploadMode ProcessingS3UploadMode

	// A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to
	// save the results of a processing job.
	//
	// This member is required.
	S3Uri *string
}

type ProcessingS3UploadMode string

const (
	ProcessingS3UploadModeContinuous ProcessingS3UploadMode = "Continuous"
	ProcessingS3UploadModeEnd_of_job ProcessingS3UploadMode = "EndOfJob"
)

type ProcessingStoppingCondition struct {

	// Specifies the maximum runtime in seconds.
	//
	// This member is required.
	MaxRuntimeInSeconds *int32
}

type ProductionVariant struct {

	// Number of instances to launch initially.
	//
	// This member is required.
	InitialInstanceCount *int32

	// The ML compute instance type.
	//
	// This member is required.
	InstanceType ProductionVariantInstanceType

	// The name of the model that you want to host. This is the name that you specified
	// when creating the model.
	//
	// This member is required.
	ModelName *string

	// The name of the production variant.
	//
	// This member is required.
	VariantName *string

	// The size of the Elastic Inference (EI) instance to use for the production
	// variant. EI instances provide on-demand GPU computing for inference. For more
	// information, see Using Elastic Inference in Amazon SageMaker
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html).
	AcceleratorType ProductionVariantAcceleratorType

	// Determines initial traffic distribution among all of the models that you specify
	// in the endpoint configuration. The traffic to a production variant is determined
	// by the ratio of the VariantWeight to the sum of all VariantWeight values across
	// all ProductionVariants. If unspecified, it defaults to 1.0.
	InitialVariantWeight *float32
}

type ProductionVariantAcceleratorType string

const (
	ProductionVariantAcceleratorTypeMl_eia1_medium ProductionVariantAcceleratorType = "ml.eia1.medium"
	ProductionVariantAcceleratorTypeMl_eia1_large  ProductionVariantAcceleratorType = "ml.eia1.large"
	ProductionVariantAcceleratorTypeMl_eia1_xlarge ProductionVariantAcceleratorType = "ml.eia1.xlarge"
	ProductionVariantAcceleratorTypeMl_eia2_medium ProductionVariantAcceleratorType = "ml.eia2.medium"
	ProductionVariantAcceleratorTypeMl_eia2_large  ProductionVariantAcceleratorType = "ml.eia2.large"
	ProductionVariantAcceleratorTypeMl_eia2_xlarge ProductionVariantAcceleratorType = "ml.eia2.xlarge"
)

type ProductionVariantInstanceType string

const (
	ProductionVariantInstanceTypeMl_t2_medium     ProductionVariantInstanceType = "ml.t2.medium"
	ProductionVariantInstanceTypeMl_t2_large      ProductionVariantInstanceType = "ml.t2.large"
	ProductionVariantInstanceTypeMl_t2_xlarge     ProductionVariantInstanceType = "ml.t2.xlarge"
	ProductionVariantInstanceTypeMl_t2_2xlarge    ProductionVariantInstanceType = "ml.t2.2xlarge"
	ProductionVariantInstanceTypeMl_m4_xlarge     ProductionVariantInstanceType = "ml.m4.xlarge"
	ProductionVariantInstanceTypeMl_m4_2xlarge    ProductionVariantInstanceType = "ml.m4.2xlarge"
	ProductionVariantInstanceTypeMl_m4_4xlarge    ProductionVariantInstanceType = "ml.m4.4xlarge"
	ProductionVariantInstanceTypeMl_m4_10xlarge   ProductionVariantInstanceType = "ml.m4.10xlarge"
	ProductionVariantInstanceTypeMl_m4_16xlarge   ProductionVariantInstanceType = "ml.m4.16xlarge"
	ProductionVariantInstanceTypeMl_m5_large      ProductionVariantInstanceType = "ml.m5.large"
	ProductionVariantInstanceTypeMl_m5_xlarge     ProductionVariantInstanceType = "ml.m5.xlarge"
	ProductionVariantInstanceTypeMl_m5_2xlarge    ProductionVariantInstanceType = "ml.m5.2xlarge"
	ProductionVariantInstanceTypeMl_m5_4xlarge    ProductionVariantInstanceType = "ml.m5.4xlarge"
	ProductionVariantInstanceTypeMl_m5_12xlarge   ProductionVariantInstanceType = "ml.m5.12xlarge"
	ProductionVariantInstanceTypeMl_m5_24xlarge   ProductionVariantInstanceType = "ml.m5.24xlarge"
	ProductionVariantInstanceTypeMl_m5d_large     ProductionVariantInstanceType = "ml.m5d.large"
	ProductionVariantInstanceTypeMl_m5d_xlarge    ProductionVariantInstanceType = "ml.m5d.xlarge"
	ProductionVariantInstanceTypeMl_m5d_2xlarge   ProductionVariantInstanceType = "ml.m5d.2xlarge"
	ProductionVariantInstanceTypeMl_m5d_4xlarge   ProductionVariantInstanceType = "ml.m5d.4xlarge"
	ProductionVariantInstanceTypeMl_m5d_12xlarge  ProductionVariantInstanceType = "ml.m5d.12xlarge"
	ProductionVariantInstanceTypeMl_m5d_24xlarge  ProductionVariantInstanceType = "ml.m5d.24xlarge"
	ProductionVariantInstanceTypeMl_c4_large      ProductionVariantInstanceType = "ml.c4.large"
	ProductionVariantInstanceTypeMl_c4_xlarge     ProductionVariantInstanceType = "ml.c4.xlarge"
	ProductionVariantInstanceTypeMl_c4_2xlarge    ProductionVariantInstanceType = "ml.c4.2xlarge"
	ProductionVariantInstanceTypeMl_c4_4xlarge    ProductionVariantInstanceType = "ml.c4.4xlarge"
	ProductionVariantInstanceTypeMl_c4_8xlarge    ProductionVariantInstanceType = "ml.c4.8xlarge"
	ProductionVariantInstanceTypeMl_p2_xlarge     ProductionVariantInstanceType = "ml.p2.xlarge"
	ProductionVariantInstanceTypeMl_p2_8xlarge    ProductionVariantInstanceType = "ml.p2.8xlarge"
	ProductionVariantInstanceTypeMl_p2_16xlarge   ProductionVariantInstanceType = "ml.p2.16xlarge"
	ProductionVariantInstanceTypeMl_p3_2xlarge    ProductionVariantInstanceType = "ml.p3.2xlarge"
	ProductionVariantInstanceTypeMl_p3_8xlarge    ProductionVariantInstanceType = "ml.p3.8xlarge"
	ProductionVariantInstanceTypeMl_p3_16xlarge   ProductionVariantInstanceType = "ml.p3.16xlarge"
	ProductionVariantInstanceTypeMl_c5_large      ProductionVariantInstanceType = "ml.c5.large"
	ProductionVariantInstanceTypeMl_c5_xlarge     ProductionVariantInstanceType = "ml.c5.xlarge"
	ProductionVariantInstanceTypeMl_c5_2xlarge    ProductionVariantInstanceType = "ml.c5.2xlarge"
	ProductionVariantInstanceTypeMl_c5_4xlarge    ProductionVariantInstanceType = "ml.c5.4xlarge"
	ProductionVariantInstanceTypeMl_c5_9xlarge    ProductionVariantInstanceType = "ml.c5.9xlarge"
	ProductionVariantInstanceTypeMl_c5_18xlarge   ProductionVariantInstanceType = "ml.c5.18xlarge"
	ProductionVariantInstanceTypeMl_c5d_large     ProductionVariantInstanceType = "ml.c5d.large"
	ProductionVariantInstanceTypeMl_c5d_xlarge    ProductionVariantInstanceType = "ml.c5d.xlarge"
	ProductionVariantInstanceTypeMl_c5d_2xlarge   ProductionVariantInstanceType = "ml.c5d.2xlarge"
	ProductionVariantInstanceTypeMl_c5d_4xlarge   ProductionVariantInstanceType = "ml.c5d.4xlarge"
	ProductionVariantInstanceTypeMl_c5d_9xlarge   ProductionVariantInstanceType = "ml.c5d.9xlarge"
	ProductionVariantInstanceTypeMl_c5d_18xlarge  ProductionVariantInstanceType = "ml.c5d.18xlarge"
	ProductionVariantInstanceTypeMl_g4dn_xlarge   ProductionVariantInstanceType = "ml.g4dn.xlarge"
	ProductionVariantInstanceTypeMl_g4dn_2xlarge  ProductionVariantInstanceType = "ml.g4dn.2xlarge"
	ProductionVariantInstanceTypeMl_g4dn_4xlarge  ProductionVariantInstanceType = "ml.g4dn.4xlarge"
	ProductionVariantInstanceTypeMl_g4dn_8xlarge  ProductionVariantInstanceType = "ml.g4dn.8xlarge"
	ProductionVariantInstanceTypeMl_g4dn_12xlarge ProductionVariantInstanceType = "ml.g4dn.12xlarge"
	ProductionVariantInstanceTypeMl_g4dn_16xlarge ProductionVariantInstanceType = "ml.g4dn.16xlarge"
	ProductionVariantInstanceTypeMl_r5_large      ProductionVariantInstanceType = "ml.r5.large"
	ProductionVariantInstanceTypeMl_r5_xlarge     ProductionVariantInstanceType = "ml.r5.xlarge"
	ProductionVariantInstanceTypeMl_r5_2xlarge    ProductionVariantInstanceType = "ml.r5.2xlarge"
	ProductionVariantInstanceTypeMl_r5_4xlarge    ProductionVariantInstanceType = "ml.r5.4xlarge"
	ProductionVariantInstanceTypeMl_r5_12xlarge   ProductionVariantInstanceType = "ml.r5.12xlarge"
	ProductionVariantInstanceTypeMl_r5_24xlarge   ProductionVariantInstanceType = "ml.r5.24xlarge"
	ProductionVariantInstanceTypeMl_r5d_large     ProductionVariantInstanceType = "ml.r5d.large"
	ProductionVariantInstanceTypeMl_r5d_xlarge    ProductionVariantInstanceType = "ml.r5d.xlarge"
	ProductionVariantInstanceTypeMl_r5d_2xlarge   ProductionVariantInstanceType = "ml.r5d.2xlarge"
	ProductionVariantInstanceTypeMl_r5d_4xlarge   ProductionVariantInstanceType = "ml.r5d.4xlarge"
	ProductionVariantInstanceTypeMl_r5d_12xlarge  ProductionVariantInstanceType = "ml.r5d.12xlarge"
	ProductionVariantInstanceTypeMl_r5d_24xlarge  ProductionVariantInstanceType = "ml.r5d.24xlarge"
	ProductionVariantInstanceTypeMl_inf1_xlarge   ProductionVariantInstanceType = "ml.inf1.xlarge"
	ProductionVariantInstanceTypeMl_inf1_2xlarge  ProductionVariantInstanceType = "ml.inf1.2xlarge"
	ProductionVariantInstanceTypeMl_inf1_6xlarge  ProductionVariantInstanceType = "ml.inf1.6xlarge"
	ProductionVariantInstanceTypeMl_inf1_24xlarge ProductionVariantInstanceType = "ml.inf1.24xlarge"
)

type ProductionVariantSummary struct {

	// The name of the variant.
	//
	// This member is required.
	VariantName *string

	// The number of instances associated with the variant.
	CurrentInstanceCount *int32

	// The weight associated with the variant.
	CurrentWeight *float32

	// An array of DeployedImage objects that specify the Amazon EC2 Container Registry
	// paths of the inference images deployed on instances of this ProductionVariant.
	DeployedImages []*DeployedImage

	// The number of instances requested in the UpdateEndpointWeightsAndCapacities
	// request.
	DesiredInstanceCount *int32

	// The requested weight, as specified in the UpdateEndpointWeightsAndCapacities
	// request.
	DesiredWeight *float32
}

type PropertyNameQuery struct {

	// Text that begins a property's name.
	//
	// This member is required.
	PropertyNameHint *string
}

type PropertyNameSuggestion struct {

	// A suggested property name based on what you entered in the search textbox in the
	// Amazon SageMaker console.
	PropertyName *string
}

type PublicWorkforceTaskPrice struct {

	// Defines the amount of money paid to an Amazon Mechanical Turk worker in United
	// States dollars.
	AmountInUsd *USD
}

type RecordWrapper string

const (
	RecordWrapperNone     RecordWrapper = "None"
	RecordWrapperRecordio RecordWrapper = "RecordIO"
)

type RenderableTask struct {

	// A JSON object that contains values for the variables defined in the template. It
	// is made available to the template under the substitution variable task.input.
	// For example, if you define a variable task.input.text in your template, you can
	// supply the variable in the JSON object as "text": "sample text".
	//
	// This member is required.
	Input *string
}

type RenderingError struct {

	// A unique identifier for a specific class of errors.
	//
	// This member is required.
	Code *string

	// A human-readable message describing the error.
	//
	// This member is required.
	Message *string
}

type ResolvedAttributes struct {

	// Applies a metric to minimize or maximize for the job's objective.
	AutoMLJobObjective *AutoMLJobObjective

	// How long a job is allowed to run, or how many candidates a job is allowed to
	// generate.
	CompletionCriteria *AutoMLJobCompletionCriteria

	// The problem type.
	ProblemType ProblemType
}

type ResourceConfig struct {

	// The number of ML compute instances to use. For distributed training, provide a
	// value greater than 1.
	//
	// This member is required.
	InstanceCount *int32

	// The ML compute instance type.
	//
	// This member is required.
	InstanceType TrainingInstanceType

	// The size of the ML storage volume that you want to provision. ML storage volumes
	// store model artifacts and incremental states. Training algorithms might also use
	// the ML storage volume for scratch space. If you want to store the training data
	// in the ML storage volume, choose File as the TrainingInputMode in the algorithm
	// specification. You must specify sufficient ML storage for your scenario. Amazon
	// SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
	// Certain Nitro-based instances include local storage with a fixed total size,
	// dependent on the instance type. When using these instances for training, Amazon
	// SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage.
	// You can't request a VolumeSizeInGB greater than the total size of the local
	// instance storage. For a list of instance types that support local instance
	// storage, including the total size per instance type, see Instance Store Volumes
	// (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes).
	//
	// This member is required.
	VolumeSizeInGB *int32

	// The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume
	// attached to the ML compute instance(s) that run the training job. Certain
	// Nitro-based instances include local storage, dependent on the instance type.
	// Local storage volumes are encrypted using a hardware module on the instance. You
	// can't request a VolumeKmsKeyId when using an instance type with local storage.
	// For a list of instance types that support local instance storage, see Instance
	// Store Volumes
	// (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes).
	// For more information about local instance storage encryption, see SSD Instance
	// Store Volumes
	// (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html).
	// The VolumeKmsKeyId can be in any of the following formats:
	//
	//     * // KMS Key ID
	// "1234abcd-12ab-34cd-56ef-1234567890ab"
	//
	//     * // Amazon Resource Name (ARN) of a
	// KMS Key
	// "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
	VolumeKmsKeyId *string
}

type ResourceInUse struct {
	Message *string
}

type ResourceLimitExceeded struct {
	Message *string
}

type ResourceLimits struct {

	// The maximum number of training jobs that a hyperparameter tuning job can launch.
	//
	// This member is required.
	MaxNumberOfTrainingJobs *int32

	// The maximum number of concurrent training jobs that a hyperparameter tuning job
	// can launch.
	//
	// This member is required.
	MaxParallelTrainingJobs *int32
}

type ResourceNotFound struct {
	Message *string
}

type ResourceSpec struct {

	// The instance type.
	InstanceType AppInstanceType

	// The Amazon Resource Name (ARN) of the SageMaker image created on the instance.
	SageMakerImageArn *string
}

type ResourceType string

const (
	ResourceTypeTraining_job               ResourceType = "TrainingJob"
	ResourceTypeExperiment                 ResourceType = "Experiment"
	ResourceTypeExperiment_trial           ResourceType = "ExperimentTrial"
	ResourceTypeExperiment_trial_component ResourceType = "ExperimentTrialComponent"
)

type RetentionPolicy struct {

	// The default is Retain, which specifies to keep the data stored on the EFS
	// volume. Specify Delete to delete the data stored on the EFS volume.
	HomeEfsFileSystem RetentionType
}

type RetentionType string

const (
	RetentionTypeRetain RetentionType = "Retain"
	RetentionTypeDelete RetentionType = "Delete"
)

type RootAccess string

const (
	RootAccessEnabled  RootAccess = "Enabled"
	RootAccessDisabled RootAccess = "Disabled"
)

type RuleEvaluationStatus string

const (
	RuleEvaluationStatusIn_progress     RuleEvaluationStatus = "InProgress"
	RuleEvaluationStatusNo_issues_found RuleEvaluationStatus = "NoIssuesFound"
	RuleEvaluationStatusIssues_found    RuleEvaluationStatus = "IssuesFound"
	RuleEvaluationStatusError           RuleEvaluationStatus = "Error"
	RuleEvaluationStatusStopping        RuleEvaluationStatus = "Stopping"
	RuleEvaluationStatusStopped         RuleEvaluationStatus = "Stopped"
)

type S3DataDistribution string

const (
	S3DataDistributionFully_replicated  S3DataDistribution = "FullyReplicated"
	S3DataDistributionSharded_by_s3_key S3DataDistribution = "ShardedByS3Key"
)

type S3DataSource struct {

	// If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker
	// uses all objects that match the specified key name prefix for model training. If
	// you choose ManifestFile, S3Uri identifies an object that is a manifest file
	// containing a list of object keys that you want Amazon SageMaker to use for model
	// training. If you choose AugmentedManifestFile, S3Uri identifies an object that
	// is an augmented manifest file in JSON lines format. This file contains the data
	// you want to use for model training. AugmentedManifestFile can only be used if
	// the Channel's input mode is Pipe.
	//
	// This member is required.
	S3DataType S3DataType

	// Depending on the value specified for the S3DataType, identifies either a key
	// name prefix or a manifest. For example:
	//
	//     * A key name prefix might look like
	// this: s3://bucketname/exampleprefix
	//
	//     * A manifest might look like this:
	// s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file
	// consisting of an array of elements. The first element is a prefix which is
	// followed by one or more suffixes. SageMaker appends the suffix elements to the
	// prefix to get a full set of S3Uri. Note that the prefix must be a valid
	// non-empty S3Uri that precludes users from specifying a manifest whose individual
	// S3Uri is sourced from different S3 buckets. The following code example shows a
	// valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"},
	// "relative/path/to/custdata-1", "relative/path/custdata-2", ...
	// "relative/path/custdata-N"] This JSON is equivalent to the following S3Uri list:
	// s3://customer_bucket/some/prefix/relative/path/to/custdata-1s3://customer_bucket/some/prefix/relative/path/custdata-2...s3://customer_bucket/some/prefix/relative/path/custdata-N
	// The complete set of S3Uri in this manifest is the input data for the channel for
	// this data source. The object that each S3Uri points to must be readable by the
	// IAM role that Amazon SageMaker uses to perform tasks on your behalf.
	//
	// This member is required.
	S3Uri *string

	// A list of one or more attribute names to use that are found in a specified
	// augmented manifest file.
	AttributeNames []*string

	// If you want Amazon SageMaker to replicate the entire dataset on each ML compute
	// instance that is launched for model training, specify FullyReplicated. If you
	// want Amazon SageMaker to replicate a subset of data on each ML compute instance
	// that is launched for model training, specify ShardedByS3Key. If there are n ML
	// compute instances launched for a training job, each instance gets approximately
	// 1/n of the number of S3 objects. In this case, model training on each machine
	// uses only the subset of training data. Don't choose more ML compute instances
	// for training than available S3 objects. If you do, some nodes won't get any data
	// and you will pay for nodes that aren't getting any training data. This applies
	// in both File and Pipe modes. Keep this in mind when developing algorithms. In
	// distributed training, where you use multiple ML compute EC2 instances, you might
	// choose ShardedByS3Key. If the algorithm requires copying training data to the ML
	// storage volume (when TrainingInputMode is set to File), this copies 1/n of the
	// number of objects.
	S3DataDistributionType S3DataDistribution
}

type S3DataType string

const (
	S3DataTypeManifest_file           S3DataType = "ManifestFile"
	S3DataTypeS3_prefix               S3DataType = "S3Prefix"
	S3DataTypeAugmented_manifest_file S3DataType = "AugmentedManifestFile"
)

type ScheduleConfig struct {

	// A cron expression that describes details about the monitoring schedule.
	// Currently the only supported cron expressions are:
	//
	//     * If you want to set the
	// job to start every hour, please use the following: Hourly: cron(0 * ? * * *)
	//
	//
	// * If you want to start the job daily: cron(0 [00-23] ? * * *)
	//
	// For example, the
	// following are valid cron expressions:
	//
	//     * Daily at noon UTC: cron(0 12 ? * *
	// *)
	//
	//     * Daily at midnight UTC: cron(0 0 ? * * *)
	//
	// To support running every 6,
	// 12 hours, the following are also supported: cron(0 [00-23]/[01-24] ? * * *) For
	// example, the following are valid cron expressions:
	//
	//     * Every 12 hours,
	// starting at 5pm UTC: cron(0 17/12 ? * * *)
	//
	//     * Every two hours starting at
	// midnight: cron(0 0/2 ? * * *)
	//
	//     * Even though the cron expression is set to
	// start at 5PM UTC, note that there could be a delay of 0-20 minutes from the
	// actual requested time to run the execution.
	//
	//     * We recommend that if you
	// would like a daily schedule, you do not provide this parameter. Amazon SageMaker
	// will pick a time for running every day.
	//
	// This member is required.
	ScheduleExpression *string
}

type ScheduleStatus string

const (
	ScheduleStatusPending   ScheduleStatus = "Pending"
	ScheduleStatusFailed    ScheduleStatus = "Failed"
	ScheduleStatusScheduled ScheduleStatus = "Scheduled"
	ScheduleStatusStopped   ScheduleStatus = "Stopped"
)

type SearchExpression struct {

	// A list of filter objects.
	Filters []*Filter

	// A list of nested filter objects.
	NestedFilters []*NestedFilters

	// A Boolean operator used to evaluate the search expression. If you want every
	// conditional statement in all lists to be satisfied for the entire search
	// expression to be true, specify And. If only a single conditional statement needs
	// to be true for the entire search expression to be true, specify Or. The default
	// value is And.
	Operator BooleanOperator

	// A list of search expression objects.
	SubExpressions []*SearchExpression
}

type SearchRecord struct {

	// The properties of an experiment.
	Experiment *Experiment

	// The properties of a training job.
	TrainingJob *TrainingJob

	// The properties of a trial.
	Trial *Trial

	// The properties of a trial component.
	TrialComponent *TrialComponent
}

type SearchSortOrder string

const (
	SearchSortOrderAscending  SearchSortOrder = "Ascending"
	SearchSortOrderDescending SearchSortOrder = "Descending"
)

type SecondaryStatus string

const (
	SecondaryStatusStarting                   SecondaryStatus = "Starting"
	SecondaryStatusLaunching_ml_instances     SecondaryStatus = "LaunchingMLInstances"
	SecondaryStatusPreparing_training_stack   SecondaryStatus = "PreparingTrainingStack"
	SecondaryStatusDownloading                SecondaryStatus = "Downloading"
	SecondaryStatusDownloading_training_image SecondaryStatus = "DownloadingTrainingImage"
	SecondaryStatusTraining                   SecondaryStatus = "Training"
	SecondaryStatusUploading                  SecondaryStatus = "Uploading"
	SecondaryStatusStopping                   SecondaryStatus = "Stopping"
	SecondaryStatusStopped                    SecondaryStatus = "Stopped"
	SecondaryStatusMax_runtime_exceeded       SecondaryStatus = "MaxRuntimeExceeded"
	SecondaryStatusCompleted                  SecondaryStatus = "Completed"
	SecondaryStatusFailed                     SecondaryStatus = "Failed"
	SecondaryStatusInterrupted                SecondaryStatus = "Interrupted"
	SecondaryStatusMax_wait_time_exceeded     SecondaryStatus = "MaxWaitTimeExceeded"
)

type SecondaryStatusTransition struct {

	// A timestamp that shows when the training job transitioned to the current
	// secondary status state.
	//
	// This member is required.
	StartTime *time.Time

	// Contains a secondary status information from a training job. Status might be one
	// of the following secondary statuses: InProgress
	//
	//     * Starting - Starting the
	// training job.
	//
	//     * Downloading - An optional stage for algorithms that support
	// File training input mode. It indicates that data is being downloaded to the ML
	// storage volumes.
	//
	//     * Training - Training is in progress.
	//
	//     * Uploading -
	// Training is complete and the model artifacts are being uploaded to the S3
	// location.
	//
	// Completed
	//
	//     * Completed - The training job has
	// completed.
	//
	// Failed
	//
	//     * Failed - The training job has failed. The reason for
	// the failure is returned in the FailureReason field of
	// DescribeTrainingJobResponse.
	//
	// Stopped
	//
	//     * MaxRuntimeExceeded - The job
	// stopped because it exceeded the maximum allowed runtime.
	//
	//     * Stopped - The
	// training job has stopped.
	//
	// Stopping
	//
	//     * Stopping - Stopping the training
	// job.
	//
	// We no longer support the following secondary statuses:
	//
	//     *
	// LaunchingMLInstances
	//
	//     * PreparingTrainingStack
	//
	//     *
	// DownloadingTrainingImage
	//
	// This member is required.
	Status SecondaryStatus

	// A timestamp that shows when the training job transitioned out of this secondary
	// status state into another secondary status state or when the training job has
	// ended.
	EndTime *time.Time

	// A detailed description of the progress within a secondary status. Amazon
	// SageMaker provides secondary statuses and status messages that apply to each of
	// them: Starting
	//
	//     * Starting the training job.
	//
	//     * Launching requested ML
	// instances.
	//
	//     * Insufficient capacity error from EC2 while launching
	// instances, retrying!
	//
	//     * Launched instance was unhealthy, replacing it!
	//
	//
	// * Preparing the instances for training.
	//
	// Training
	//
	//     * Downloading the
	// training image.
	//
	//     * Training image download completed. Training in
	// progress.
	//
	// Status messages are subject to change. Therefore, we recommend not
	// including them in code that programmatically initiates actions. For examples,
	// don't use status messages in if statements. To have an overview of your training
	// job's progress, view TrainingJobStatus and SecondaryStatus in
	// DescribeTrainingJob, and StatusMessage together. For example, at the start of a
	// training job, you might see the following:
	//
	//     * TrainingJobStatus -
	// InProgress
	//
	//     * SecondaryStatus - Training
	//
	//     * StatusMessage - Downloading
	// the training image
	StatusMessage *string
}

type SharingSettings struct {

	// Whether to include the notebook cell output when sharing the notebook. The
	// default is Disabled.
	NotebookOutputOption NotebookOutputOption

	// When NotebookOutputOption is Allowed, the AWS Key Management Service (KMS)
	// encryption key ID used to encrypt the notebook cell output in the Amazon S3
	// bucket.
	S3KmsKeyId *string

	// When NotebookOutputOption is Allowed, the Amazon S3 bucket used to save the
	// notebook cell output. If S3OutputPath isn't specified, a default bucket is used.
	S3OutputPath *string
}

type ShuffleConfig struct {

	// Determines the shuffling order in ShuffleConfig value.
	//
	// This member is required.
	Seed *int64
}

type SortBy string

const (
	SortByName          SortBy = "Name"
	SortByCreation_time SortBy = "CreationTime"
	SortByStatus        SortBy = "Status"
)

type SortExperimentsBy string

const (
	SortExperimentsByName          SortExperimentsBy = "Name"
	SortExperimentsByCreation_time SortExperimentsBy = "CreationTime"
)

type SortOrder string

const (
	SortOrderAscending  SortOrder = "Ascending"
	SortOrderDescending SortOrder = "Descending"
)

type SortTrialComponentsBy string

const (
	SortTrialComponentsByName          SortTrialComponentsBy = "Name"
	SortTrialComponentsByCreation_time SortTrialComponentsBy = "CreationTime"
)

type SortTrialsBy string

const (
	SortTrialsByName          SortTrialsBy = "Name"
	SortTrialsByCreation_time SortTrialsBy = "CreationTime"
)

type SourceAlgorithm struct {

	// The name of an algorithm that was used to create the model package. The
	// algorithm must be either an algorithm resource in your Amazon SageMaker account
	// or an algorithm in AWS Marketplace that you are subscribed to.
	//
	// This member is required.
	AlgorithmName *string

	// The Amazon S3 path where the model artifacts, which result from model training,
	// are stored. This path must point to a single gzip compressed tar archive
	// (.tar.gz suffix).
	ModelDataUrl *string
}

type SourceAlgorithmSpecification struct {

	// A list of the algorithms that were used to create a model package.
	//
	// This member is required.
	SourceAlgorithms []*SourceAlgorithm
}

type SourceIpConfig struct {

	// A list of one to ten Classless Inter-Domain Routing
	// (https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html) (CIDR)
	// values. Maximum: Ten CIDR values The following Length Constraints apply to
	// individual CIDR values in the CIDR value list.
	//
	// This member is required.
	Cidrs []*string
}

type SplitType string

const (
	SplitTypeNone     SplitType = "None"
	SplitTypeLine     SplitType = "Line"
	SplitTypeRecordio SplitType = "RecordIO"
	SplitTypeTfrecord SplitType = "TFRecord"
)

type StoppingCondition struct {

	// The maximum length of time, in seconds, that the training or compilation job can
	// run. If job does not complete during this time, Amazon SageMaker ends the job.
	// If value is not specified, default value is 1 day. The maximum value is 28 days.
	MaxRuntimeInSeconds *int32

	// The maximum length of time, in seconds, how long you are willing to wait for a
	// managed spot training job to complete. It is the amount of time spent waiting
	// for Spot capacity plus the amount of time the training job runs. It must be
	// equal to or greater than MaxRuntimeInSeconds.
	MaxWaitTimeInSeconds *int32
}

type SubscribedWorkteam struct {

	// The Amazon Resource Name (ARN) of the vendor that you have subscribed.
	//
	// This member is required.
	WorkteamArn *string

	// Marketplace product listing ID.
	ListingId *string

	// The description of the vendor from the Amazon Marketplace.
	MarketplaceDescription *string

	// The title of the service provided by the vendor in the Amazon Marketplace.
	MarketplaceTitle *string

	// The name of the vendor in the Amazon Marketplace.
	SellerName *string
}

type SuggestionQuery struct {

	// Defines a property name hint. Only property names that begin with the specified
	// hint are included in the response.
	PropertyNameQuery *PropertyNameQuery
}

type Tag struct {

	// The tag key.
	//
	// This member is required.
	Key *string

	// The tag value.
	//
	// This member is required.
	Value *string
}

type TargetDevice string

const (
	TargetDeviceLambda        TargetDevice = "lambda"
	TargetDeviceMl_m4         TargetDevice = "ml_m4"
	TargetDeviceMl_m5         TargetDevice = "ml_m5"
	TargetDeviceMl_c4         TargetDevice = "ml_c4"
	TargetDeviceMl_c5         TargetDevice = "ml_c5"
	TargetDeviceMl_p2         TargetDevice = "ml_p2"
	TargetDeviceMl_p3         TargetDevice = "ml_p3"
	TargetDeviceMl_g4dn       TargetDevice = "ml_g4dn"
	TargetDeviceMl_inf1       TargetDevice = "ml_inf1"
	TargetDeviceJetson_tx1    TargetDevice = "jetson_tx1"
	TargetDeviceJetson_tx2    TargetDevice = "jetson_tx2"
	TargetDeviceJetson_nano   TargetDevice = "jetson_nano"
	TargetDeviceJetson_xavier TargetDevice = "jetson_xavier"
	TargetDeviceRasp3b        TargetDevice = "rasp3b"
	TargetDeviceImx8qm        TargetDevice = "imx8qm"
	TargetDeviceDeeplens      TargetDevice = "deeplens"
	TargetDeviceRk3399        TargetDevice = "rk3399"
	TargetDeviceRk3288        TargetDevice = "rk3288"
	TargetDeviceAisage        TargetDevice = "aisage"
	TargetDeviceSbe_c         TargetDevice = "sbe_c"
	TargetDeviceQcs605        TargetDevice = "qcs605"
	TargetDeviceQcs603        TargetDevice = "qcs603"
	TargetDeviceSitara_am57x  TargetDevice = "sitara_am57x"
	TargetDeviceAmba_cv22     TargetDevice = "amba_cv22"
	TargetDeviceX86_win32     TargetDevice = "x86_win32"
	TargetDeviceX86_win64     TargetDevice = "x86_win64"
)

type TargetPlatform struct {

	// Specifies a target platform architecture.
	//
	//     * X86_64: 64-bit version of the
	// x86 instruction set.
	//
	//     * X86: 32-bit version of the x86 instruction set.
	//
	//
	// * ARM64: ARMv8 64-bit CPU.
	//
	//     * ARM_EABIHF: ARMv7 32-bit, Hard Float.
	//
	//     *
	// ARM_EABI: ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
	//
	// This member is required.
	Arch TargetPlatformArch

	// Specifies a target platform OS.
	//
	//     * LINUX: Linux-based operating systems.
	//
	//
	// * ANDROID: Android operating systems. Android API level can be specified using
	// the ANDROID_PLATFORM compiler option. For example, "CompilerOptions":
	// {'ANDROID_PLATFORM': 28}
	//
	// This member is required.
	Os TargetPlatformOs

	// Specifies a target platform accelerator (optional).
	//
	//     * NVIDIA: Nvidia
	// graphics processing unit. It also requires gpu-code, trt-ver, cuda-ver compiler
	// options
	//
	//     * MALI: ARM Mali graphics processor
	//
	//     * INTEL_GRAPHICS:
	// Integrated Intel graphics
	Accelerator TargetPlatformAccelerator
}

type TargetPlatformAccelerator string

const (
	TargetPlatformAcceleratorIntel_graphics TargetPlatformAccelerator = "INTEL_GRAPHICS"
	TargetPlatformAcceleratorMali           TargetPlatformAccelerator = "MALI"
	TargetPlatformAcceleratorNvidia         TargetPlatformAccelerator = "NVIDIA"
)

type TargetPlatformArch string

const (
	TargetPlatformArchX86_64     TargetPlatformArch = "X86_64"
	TargetPlatformArchX86        TargetPlatformArch = "X86"
	TargetPlatformArchArm64      TargetPlatformArch = "ARM64"
	TargetPlatformArchArm_eabi   TargetPlatformArch = "ARM_EABI"
	TargetPlatformArchArm_eabihf TargetPlatformArch = "ARM_EABIHF"
)

type TargetPlatformOs string

const (
	TargetPlatformOsAndroid TargetPlatformOs = "ANDROID"
	TargetPlatformOsLinux   TargetPlatformOs = "LINUX"
)

type TensorBoardAppSettings struct {

	// The default instance type and the Amazon Resource Name (ARN) of the SageMaker
	// image created on the instance.
	DefaultResourceSpec *ResourceSpec
}

type TensorBoardOutputConfig struct {

	// Path to Amazon S3 storage location for TensorBoard output.
	//
	// This member is required.
	S3OutputPath *string

	// Path to local storage location for tensorBoard output. Defaults to
	// /opt/ml/output/tensorboard.
	LocalPath *string
}

type TrainingInputMode string

const (
	TrainingInputModePipe TrainingInputMode = "Pipe"
	TrainingInputModeFile TrainingInputMode = "File"
)

type TrainingInstanceType string

const (
	TrainingInstanceTypeMl_m4_xlarge     TrainingInstanceType = "ml.m4.xlarge"
	TrainingInstanceTypeMl_m4_2xlarge    TrainingInstanceType = "ml.m4.2xlarge"
	TrainingInstanceTypeMl_m4_4xlarge    TrainingInstanceType = "ml.m4.4xlarge"
	TrainingInstanceTypeMl_m4_10xlarge   TrainingInstanceType = "ml.m4.10xlarge"
	TrainingInstanceTypeMl_m4_16xlarge   TrainingInstanceType = "ml.m4.16xlarge"
	TrainingInstanceTypeMl_g4dn_xlarge   TrainingInstanceType = "ml.g4dn.xlarge"
	TrainingInstanceTypeMl_g4dn_2xlarge  TrainingInstanceType = "ml.g4dn.2xlarge"
	TrainingInstanceTypeMl_g4dn_4xlarge  TrainingInstanceType = "ml.g4dn.4xlarge"
	TrainingInstanceTypeMl_g4dn_8xlarge  TrainingInstanceType = "ml.g4dn.8xlarge"
	TrainingInstanceTypeMl_g4dn_12xlarge TrainingInstanceType = "ml.g4dn.12xlarge"
	TrainingInstanceTypeMl_g4dn_16xlarge TrainingInstanceType = "ml.g4dn.16xlarge"
	TrainingInstanceTypeMl_m5_large      TrainingInstanceType = "ml.m5.large"
	TrainingInstanceTypeMl_m5_xlarge     TrainingInstanceType = "ml.m5.xlarge"
	TrainingInstanceTypeMl_m5_2xlarge    TrainingInstanceType = "ml.m5.2xlarge"
	TrainingInstanceTypeMl_m5_4xlarge    TrainingInstanceType = "ml.m5.4xlarge"
	TrainingInstanceTypeMl_m5_12xlarge   TrainingInstanceType = "ml.m5.12xlarge"
	TrainingInstanceTypeMl_m5_24xlarge   TrainingInstanceType = "ml.m5.24xlarge"
	TrainingInstanceTypeMl_c4_xlarge     TrainingInstanceType = "ml.c4.xlarge"
	TrainingInstanceTypeMl_c4_2xlarge    TrainingInstanceType = "ml.c4.2xlarge"
	TrainingInstanceTypeMl_c4_4xlarge    TrainingInstanceType = "ml.c4.4xlarge"
	TrainingInstanceTypeMl_c4_8xlarge    TrainingInstanceType = "ml.c4.8xlarge"
	TrainingInstanceTypeMl_p2_xlarge     TrainingInstanceType = "ml.p2.xlarge"
	TrainingInstanceTypeMl_p2_8xlarge    TrainingInstanceType = "ml.p2.8xlarge"
	TrainingInstanceTypeMl_p2_16xlarge   TrainingInstanceType = "ml.p2.16xlarge"
	TrainingInstanceTypeMl_p3_2xlarge    TrainingInstanceType = "ml.p3.2xlarge"
	TrainingInstanceTypeMl_p3_8xlarge    TrainingInstanceType = "ml.p3.8xlarge"
	TrainingInstanceTypeMl_p3_16xlarge   TrainingInstanceType = "ml.p3.16xlarge"
	TrainingInstanceTypeMl_p3dn_24xlarge TrainingInstanceType = "ml.p3dn.24xlarge"
	TrainingInstanceTypeMl_c5_xlarge     TrainingInstanceType = "ml.c5.xlarge"
	TrainingInstanceTypeMl_c5_2xlarge    TrainingInstanceType = "ml.c5.2xlarge"
	TrainingInstanceTypeMl_c5_4xlarge    TrainingInstanceType = "ml.c5.4xlarge"
	TrainingInstanceTypeMl_c5_9xlarge    TrainingInstanceType = "ml.c5.9xlarge"
	TrainingInstanceTypeMl_c5_18xlarge   TrainingInstanceType = "ml.c5.18xlarge"
	TrainingInstanceTypeMl_c5n_xlarge    TrainingInstanceType = "ml.c5n.xlarge"
	TrainingInstanceTypeMl_c5n_2xlarge   TrainingInstanceType = "ml.c5n.2xlarge"
	TrainingInstanceTypeMl_c5n_4xlarge   TrainingInstanceType = "ml.c5n.4xlarge"
	TrainingInstanceTypeMl_c5n_9xlarge   TrainingInstanceType = "ml.c5n.9xlarge"
	TrainingInstanceTypeMl_c5n_18xlarge  TrainingInstanceType = "ml.c5n.18xlarge"
)

type TrainingJob struct {

	// Information about the algorithm used for training, and algorithm metadata.
	AlgorithmSpecification *AlgorithmSpecification

	// The Amazon Resource Name (ARN) of the job.
	AutoMLJobArn *string

	// The billable time in seconds.
	BillableTimeInSeconds *int32

	// Contains information about the output location for managed spot training
	// checkpoint data.
	CheckpointConfig *CheckpointConfig

	// A timestamp that indicates when the training job was created.
	CreationTime *time.Time

	// Configuration information for the debug hook parameters, collection
	// configuration, and storage paths.
	DebugHookConfig *DebugHookConfig

	// Information about the debug rule configuration.
	DebugRuleConfigurations []*DebugRuleConfiguration

	// Information about the evaluation status of the rules for the training job.
	DebugRuleEvaluationStatuses []*DebugRuleEvaluationStatus

	// To encrypt all communications between ML compute instances in distributed
	// training, choose True. Encryption provides greater security for distributed
	// training, but training might take longer. How long it takes depends on the
	// amount of communication between compute instances, especially if you use a deep
	// learning algorithm in distributed training.
	EnableInterContainerTrafficEncryption *bool

	// When true, enables managed spot training using Amazon EC2 Spot instances to run
	// training jobs instead of on-demand instances. For more information, see Managed
	// Spot Training
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html).
	EnableManagedSpotTraining *bool

	// If the TrainingJob was created with network isolation, the value is set to true.
	// If network isolation is enabled, nodes can't communicate beyond the VPC they run
	// in.
	EnableNetworkIsolation *bool

	// Associates a SageMaker job as a trial component with an experiment and trial.
	// Specified when you call the following APIs:
	//
	//     * CreateProcessingJob
	//
	//     *
	// CreateTrainingJob
	//
	//     * CreateTransformJob
	ExperimentConfig *ExperimentConfig

	// If the training job failed, the reason it failed.
	FailureReason *string

	// A list of final metric values that are set when the training job completes. Used
	// only if the training job was configured to use metrics.
	FinalMetricDataList []*MetricData

	// Algorithm-specific parameters.
	HyperParameters map[string]*string

	// An array of Channel objects that describes each data input channel.
	InputDataConfig []*Channel

	// The Amazon Resource Name (ARN) of the labeling job.
	LabelingJobArn *string

	// A timestamp that indicates when the status of the training job was last
	// modified.
	LastModifiedTime *time.Time

	// Information about the Amazon S3 location that is configured for storing model
	// artifacts.
	ModelArtifacts *ModelArtifacts

	// The S3 path where model artifacts that you configured when creating the job are
	// stored. Amazon SageMaker creates subfolders for model artifacts.
	OutputDataConfig *OutputDataConfig

	// Resources, including ML compute instances and ML storage volumes, that are
	// configured for model training.
	ResourceConfig *ResourceConfig

	// The AWS Identity and Access Management (IAM) role configured for the training
	// job.
	RoleArn *string

	// Provides detailed information about the state of the training job. For detailed
	// information about the secondary status of the training job, see StatusMessage
	// under SecondaryStatusTransition. Amazon SageMaker provides primary statuses and
	// secondary statuses that apply to each of them: InProgress
	//
	//     * Starting -
	// Starting the training job.
	//
	//     * Downloading - An optional stage for algorithms
	// that support File training input mode. It indicates that data is being
	// downloaded to the ML storage volumes.
	//
	//     * Training - Training is in
	// progress.
	//
	//     * Uploading - Training is complete and the model artifacts are
	// being uploaded to the S3 location.
	//
	// Completed
	//
	//     * Completed - The training
	// job has completed.
	//
	// Failed
	//
	//     * Failed - The training job has failed. The
	// reason for the failure is returned in the FailureReason field of
	// DescribeTrainingJobResponse.
	//
	// Stopped
	//
	//     * MaxRuntimeExceeded - The job
	// stopped because it exceeded the maximum allowed runtime.
	//
	//     * Stopped - The
	// training job has stopped.
	//
	// Stopping
	//
	//     * Stopping - Stopping the training
	// job.
	//
	// Valid values for SecondaryStatus are subject to change. We no longer
	// support the following secondary statuses:
	//
	//     * LaunchingMLInstances
	//
	//     *
	// PreparingTrainingStack
	//
	//     * DownloadingTrainingImage
	SecondaryStatus SecondaryStatus

	// A history of all of the secondary statuses that the training job has
	// transitioned through.
	SecondaryStatusTransitions []*SecondaryStatusTransition

	// Specifies a limit to how long a model training job can run. When the job reaches
	// the time limit, Amazon SageMaker ends the training job. Use this API to cap
	// model training costs. To stop a job, Amazon SageMaker sends the algorithm the
	// SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use
	// this 120-second window to save the model artifacts, so the results of training
	// are not lost.
	StoppingCondition *StoppingCondition

	// An array of key-value pairs. For more information, see Using Cost Allocation
	// Tags
	// (https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what)
	// in the AWS Billing and Cost Management User Guide.
	Tags []*Tag

	// Configuration of storage locations for TensorBoard output.
	TensorBoardOutputConfig *TensorBoardOutputConfig

	// Indicates the time when the training job ends on training instances. You are
	// billed for the time interval between the value of TrainingStartTime and this
	// time. For successful jobs and stopped jobs, this is the time after model
	// artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker
	// detects a job failure.
	TrainingEndTime *time.Time

	// The Amazon Resource Name (ARN) of the training job.
	TrainingJobArn *string

	// The name of the training job.
	TrainingJobName *string

	// The status of the training job. Training job statuses are:
	//
	//     * InProgress -
	// The training is in progress.
	//
	//     * Completed - The training job has
	// completed.
	//
	//     * Failed - The training job has failed. To see the reason for
	// the failure, see the FailureReason field in the response to a
	// DescribeTrainingJobResponse call.
	//
	//     * Stopping - The training job is
	// stopping.
	//
	//     * Stopped - The training job has stopped.
	//
	// For more detailed
	// information, see SecondaryStatus.
	TrainingJobStatus TrainingJobStatus

	// Indicates the time when the training job starts on training instances. You are
	// billed for the time interval between this time and the value of TrainingEndTime.
	// The start time in CloudWatch Logs might be later than this time. The difference
	// is due to the time it takes to download the training data and to the size of the
	// training container.
	TrainingStartTime *time.Time

	// The training time in seconds.
	TrainingTimeInSeconds *int32

	// The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if
	// the training job was launched by a hyperparameter tuning job.
	TuningJobArn *string

	// A VpcConfig object that specifies the VPC that this training job has access to.
	// For more information, see Protect Training Jobs by Using an Amazon Virtual
	// Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html).
	VpcConfig *VpcConfig
}

type TrainingJobDefinition struct {

	// An array of Channel objects, each of which specifies an input source.
	//
	// This member is required.
	InputDataConfig []*Channel

	// the path to the S3 bucket where you want to store model artifacts. Amazon
	// SageMaker creates subfolders for the artifacts.
	//
	// This member is required.
	OutputDataConfig *OutputDataConfig

	// The resources, including the ML compute instances and ML storage volumes, to use
	// for model training.
	//
	// This member is required.
	ResourceConfig *ResourceConfig

	// Specifies a limit to how long a model training job can run. When the job reaches
	// the time limit, Amazon SageMaker ends the training job. Use this API to cap
	// model training costs. To stop a job, Amazon SageMaker sends the algorithm the
	// SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use
	// this 120-second window to save the model artifacts.
	//
	// This member is required.
	StoppingCondition *StoppingCondition

	// The input mode used by the algorithm for the training job. For the input modes
	// that Amazon SageMaker algorithms support, see Algorithms
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html). If an algorithm
	// supports the File input mode, Amazon SageMaker downloads the training data from
	// S3 to the provisioned ML storage Volume, and mounts the directory to docker
	// volume for training container. If an algorithm supports the Pipe input mode,
	// Amazon SageMaker streams data directly from S3 to the container.
	//
	// This member is required.
	TrainingInputMode TrainingInputMode

	// The hyperparameters used for the training job.
	HyperParameters map[string]*string
}

type TrainingJobEarlyStoppingType string

const (
	TrainingJobEarlyStoppingTypeOff  TrainingJobEarlyStoppingType = "Off"
	TrainingJobEarlyStoppingTypeAuto TrainingJobEarlyStoppingType = "Auto"
)

type TrainingJobSortByOptions string

const (
	TrainingJobSortByOptionsName                      TrainingJobSortByOptions = "Name"
	TrainingJobSortByOptionsCreationtime              TrainingJobSortByOptions = "CreationTime"
	TrainingJobSortByOptionsStatus                    TrainingJobSortByOptions = "Status"
	TrainingJobSortByOptionsFinalobjectivemetricvalue TrainingJobSortByOptions = "FinalObjectiveMetricValue"
)

type TrainingJobStatus string

const (
	TrainingJobStatusIn_progress TrainingJobStatus = "InProgress"
	TrainingJobStatusCompleted   TrainingJobStatus = "Completed"
	TrainingJobStatusFailed      TrainingJobStatus = "Failed"
	TrainingJobStatusStopping    TrainingJobStatus = "Stopping"
	TrainingJobStatusStopped     TrainingJobStatus = "Stopped"
)

type TrainingJobStatusCounters struct {

	// The number of completed training jobs launched by the hyperparameter tuning job.
	Completed *int32

	// The number of in-progress training jobs launched by a hyperparameter tuning job.
	InProgress *int32

	// The number of training jobs that failed and can't be retried. A failed training
	// job can't be retried if it failed because a client error occurred.
	NonRetryableError *int32

	// The number of training jobs that failed, but can be retried. A failed training
	// job can be retried only if it failed because an internal service error occurred.
	RetryableError *int32

	// The number of training jobs launched by a hyperparameter tuning job that were
	// manually stopped.
	Stopped *int32
}

type TrainingJobSummary struct {

	// A timestamp that shows when the training job was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the training job.
	//
	// This member is required.
	TrainingJobArn *string

	// The name of the training job that you want a summary for.
	//
	// This member is required.
	TrainingJobName *string

	// The status of the training job.
	//
	// This member is required.
	TrainingJobStatus TrainingJobStatus

	// Timestamp when the training job was last modified.
	LastModifiedTime *time.Time

	// A timestamp that shows when the training job ended. This field is set only if
	// the training job has one of the terminal statuses (Completed, Failed, or
	// Stopped).
	TrainingEndTime *time.Time
}

type TrainingSpecification struct {

	// A list of the instance types that this algorithm can use for training.
	//
	// This member is required.
	SupportedTrainingInstanceTypes []TrainingInstanceType

	// A list of ChannelSpecification objects, which specify the input sources to be
	// used by the algorithm.
	//
	// This member is required.
	TrainingChannels []*ChannelSpecification

	// The Amazon ECR registry path of the Docker image that contains the training
	// algorithm.
	//
	// This member is required.
	TrainingImage *string

	// A list of MetricDefinition objects, which are used for parsing metrics generated
	// by the algorithm.
	MetricDefinitions []*MetricDefinition

	// A list of the HyperParameterSpecification objects, that define the supported
	// hyperparameters. This is required if the algorithm supports automatic model
	// tuning.>
	SupportedHyperParameters []*HyperParameterSpecification

	// A list of the metrics that the algorithm emits that can be used as the objective
	// metric in a hyperparameter tuning job.
	SupportedTuningJobObjectiveMetrics []*HyperParameterTuningJobObjective

	// Indicates whether the algorithm supports distributed training. If set to false,
	// buyers can't request more than one instance during training.
	SupportsDistributedTraining *bool

	// An MD5 hash of the training algorithm that identifies the Docker image used for
	// training.
	TrainingImageDigest *string
}

type TransformDataSource struct {

	// The S3 location of the data source that is associated with a channel.
	//
	// This member is required.
	S3DataSource *TransformS3DataSource
}

type TransformInput struct {

	// Describes the location of the channel data, which is, the S3 location of the
	// input data that the model can consume.
	//
	// This member is required.
	DataSource *TransformDataSource

	// If your transform data is compressed, specify the compression type. Amazon
	// SageMaker automatically decompresses the data for the transform job accordingly.
	// The default value is None.
	CompressionType CompressionType

	// The multipurpose internet mail extension (MIME) type of the data. Amazon
	// SageMaker uses the MIME type with each http call to transfer data to the
	// transform job.
	ContentType *string

	// The method to use to split the transform job's data files into smaller batches.
	// Splitting is necessary when the total size of each object is too large to fit in
	// a single request. You can also use data splitting to improve performance by
	// processing multiple concurrent mini-batches. The default value for SplitType is
	// None, which indicates that input data files are not split, and request payloads
	// contain the entire contents of an input object. Set the value of this parameter
	// to Line to split records on a newline character boundary. SplitType also
	// supports a number of record-oriented binary data formats. When splitting is
	// enabled, the size of a mini-batch depends on the values of the BatchStrategy and
	// MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord,
	// Amazon SageMaker sends the maximum number of records in each request, up to the
	// MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon
	// SageMaker sends individual records in each request. Some data formats represent
	// a record as a binary payload wrapped with extra padding bytes. When splitting is
	// applied to a binary data format, padding is removed if the value of
	// BatchStrategy is set to SingleRecord. Padding is not removed if the value of
	// BatchStrategy is set to MultiRecord. For more information about RecordIO, see
	// Create a Dataset Using RecordIO (https://mxnet.apache.org/api/faq/recordio) in
	// the MXNet documentation. For more information about TFRecord, see Consuming
	// TFRecord data
	// (https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data) in the
	// TensorFlow documentation.
	SplitType SplitType
}

type TransformInstanceType string

const (
	TransformInstanceTypeMl_m4_xlarge   TransformInstanceType = "ml.m4.xlarge"
	TransformInstanceTypeMl_m4_2xlarge  TransformInstanceType = "ml.m4.2xlarge"
	TransformInstanceTypeMl_m4_4xlarge  TransformInstanceType = "ml.m4.4xlarge"
	TransformInstanceTypeMl_m4_10xlarge TransformInstanceType = "ml.m4.10xlarge"
	TransformInstanceTypeMl_m4_16xlarge TransformInstanceType = "ml.m4.16xlarge"
	TransformInstanceTypeMl_c4_xlarge   TransformInstanceType = "ml.c4.xlarge"
	TransformInstanceTypeMl_c4_2xlarge  TransformInstanceType = "ml.c4.2xlarge"
	TransformInstanceTypeMl_c4_4xlarge  TransformInstanceType = "ml.c4.4xlarge"
	TransformInstanceTypeMl_c4_8xlarge  TransformInstanceType = "ml.c4.8xlarge"
	TransformInstanceTypeMl_p2_xlarge   TransformInstanceType = "ml.p2.xlarge"
	TransformInstanceTypeMl_p2_8xlarge  TransformInstanceType = "ml.p2.8xlarge"
	TransformInstanceTypeMl_p2_16xlarge TransformInstanceType = "ml.p2.16xlarge"
	TransformInstanceTypeMl_p3_2xlarge  TransformInstanceType = "ml.p3.2xlarge"
	TransformInstanceTypeMl_p3_8xlarge  TransformInstanceType = "ml.p3.8xlarge"
	TransformInstanceTypeMl_p3_16xlarge TransformInstanceType = "ml.p3.16xlarge"
	TransformInstanceTypeMl_c5_xlarge   TransformInstanceType = "ml.c5.xlarge"
	TransformInstanceTypeMl_c5_2xlarge  TransformInstanceType = "ml.c5.2xlarge"
	TransformInstanceTypeMl_c5_4xlarge  TransformInstanceType = "ml.c5.4xlarge"
	TransformInstanceTypeMl_c5_9xlarge  TransformInstanceType = "ml.c5.9xlarge"
	TransformInstanceTypeMl_c5_18xlarge TransformInstanceType = "ml.c5.18xlarge"
	TransformInstanceTypeMl_m5_large    TransformInstanceType = "ml.m5.large"
	TransformInstanceTypeMl_m5_xlarge   TransformInstanceType = "ml.m5.xlarge"
	TransformInstanceTypeMl_m5_2xlarge  TransformInstanceType = "ml.m5.2xlarge"
	TransformInstanceTypeMl_m5_4xlarge  TransformInstanceType = "ml.m5.4xlarge"
	TransformInstanceTypeMl_m5_12xlarge TransformInstanceType = "ml.m5.12xlarge"
	TransformInstanceTypeMl_m5_24xlarge TransformInstanceType = "ml.m5.24xlarge"
)

type TransformJob struct {

	// The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
	AutoMLJobArn *string

	// Specifies the number of records to include in a mini-batch for an HTTP inference
	// request. A record is a single unit of input data that inference can be made on.
	// For example, a single line in a CSV file is a record.
	BatchStrategy BatchStrategy

	// A timestamp that shows when the transform Job was created.
	CreationTime *time.Time

	// The data structure used to specify the data to be used for inference in a batch
	// transform job and to associate the data that is relevant to the prediction
	// results in the output. The input filter provided allows you to exclude input
	// data that is not needed for inference in a batch transform job. The output
	// filter provided allows you to include input data relevant to interpreting the
	// predictions in the output from the job. For more information, see Associate
	// Prediction Results with their Corresponding Input Records
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html).
	DataProcessing *DataProcessing

	// The environment variables to set in the Docker container. We support up to 16
	// key and values entries in the map.
	Environment map[string]*string

	// Associates a SageMaker job as a trial component with an experiment and trial.
	// Specified when you call the following APIs:
	//
	//     * CreateProcessingJob
	//
	//     *
	// CreateTrainingJob
	//
	//     * CreateTransformJob
	ExperimentConfig *ExperimentConfig

	// If the transform job failed, the reason it failed.
	FailureReason *string

	// The Amazon Resource Name (ARN) of the labeling job that created the transform
	// job.
	LabelingJobArn *string

	// The maximum number of parallel requests that can be sent to each instance in a
	// transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker
	// checks the optional execution-parameters to determine the settings for your
	// chosen algorithm. If the execution-parameters endpoint is not enabled, the
	// default value is 1. For built-in algorithms, you don't need to set a value for
	// MaxConcurrentTransforms.
	MaxConcurrentTransforms *int32

	// The maximum allowed size of the payload, in MB. A payload is the data portion of
	// a record (without metadata). The value in MaxPayloadInMB must be greater than,
	// or equal to, the size of a single record. To estimate the size of a record in
	// MB, divide the size of your dataset by the number of records. To ensure that the
	// records fit within the maximum payload size, we recommend using a slightly
	// larger value. The default value is 6 MB. For cases where the payload might be
	// arbitrarily large and is transmitted using HTTP chunked encoding, set the value
	// to 0. This feature works only in supported algorithms. Currently, SageMaker
	// built-in algorithms do not support HTTP chunked encoding.
	MaxPayloadInMB *int32

	// Configures the timeout and maximum number of retries for processing a transform
	// job invocation.
	ModelClientConfig *ModelClientConfig

	// The name of the model associated with the transform job.
	ModelName *string

	// A list of tags associated with the transform job.
	Tags []*Tag

	// Indicates when the transform job has been completed, or has stopped or failed.
	// You are billed for the time interval between this time and the value of
	// TransformStartTime.
	TransformEndTime *time.Time

	// Describes the input source of a transform job and the way the transform job
	// consumes it.
	TransformInput *TransformInput

	// The Amazon Resource Name (ARN) of the transform job.
	TransformJobArn *string

	// The name of the transform job.
	TransformJobName *string

	// The status of the transform job. Transform job statuses are:
	//
	//     * InProgress -
	// The job is in progress.
	//
	//     * Completed - The job has completed.
	//
	//     * Failed
	// - The transform job has failed. To see the reason for the failure, see the
	// FailureReason field in the response to a DescribeTransformJob call.
	//
	//     *
	// Stopping - The transform job is stopping.
	//
	//     * Stopped - The transform job has
	// stopped.
	TransformJobStatus TransformJobStatus

	// Describes the results of a transform job.
	TransformOutput *TransformOutput

	// Describes the resources, including ML instance types and ML instance count, to
	// use for transform job.
	TransformResources *TransformResources

	// Indicates when the transform job starts on ML instances. You are billed for the
	// time interval between this time and the value of TransformEndTime.
	TransformStartTime *time.Time
}

type TransformJobDefinition struct {

	// A description of the input source and the way the transform job consumes it.
	//
	// This member is required.
	TransformInput *TransformInput

	// Identifies the Amazon S3 location where you want Amazon SageMaker to save the
	// results from the transform job.
	//
	// This member is required.
	TransformOutput *TransformOutput

	// Identifies the ML compute instances for the transform job.
	//
	// This member is required.
	TransformResources *TransformResources

	// A string that determines the number of records included in a single mini-batch.
	// SingleRecord means only one record is used per mini-batch. MultiRecord means a
	// mini-batch is set to contain as many records that can fit within the
	// MaxPayloadInMB limit.
	BatchStrategy BatchStrategy

	// The environment variables to set in the Docker container. We support up to 16
	// key and values entries in the map.
	Environment map[string]*string

	// The maximum number of parallel requests that can be sent to each instance in a
	// transform job. The default value is 1.
	MaxConcurrentTransforms *int32

	// The maximum payload size allowed, in MB. A payload is the data portion of a
	// record (without metadata).
	MaxPayloadInMB *int32
}

type TransformJobStatus string

const (
	TransformJobStatusIn_progress TransformJobStatus = "InProgress"
	TransformJobStatusCompleted   TransformJobStatus = "Completed"
	TransformJobStatusFailed      TransformJobStatus = "Failed"
	TransformJobStatusStopping    TransformJobStatus = "Stopping"
	TransformJobStatusStopped     TransformJobStatus = "Stopped"
)

type TransformJobSummary struct {

	// A timestamp that shows when the transform Job was created.
	//
	// This member is required.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the transform job.
	//
	// This member is required.
	TransformJobArn *string

	// The name of the transform job.
	//
	// This member is required.
	TransformJobName *string

	// The status of the transform job.
	//
	// This member is required.
	TransformJobStatus TransformJobStatus

	// If the transform job failed, the reason it failed.
	FailureReason *string

	// Indicates when the transform job was last modified.
	LastModifiedTime *time.Time

	// Indicates when the transform job ends on compute instances. For successful jobs
	// and stopped jobs, this is the exact time recorded after the results are
	// uploaded. For failed jobs, this is when Amazon SageMaker detected that the job
	// failed.
	TransformEndTime *time.Time
}

type TransformOutput struct {

	// The Amazon S3 path where you want Amazon SageMaker to store the results of the
	// transform job. For example, s3://bucket-name/key-name-prefix. For every S3
	// object used as input for the transform job, batch transform stores the
	// transformed data with an .out suffix in a corresponding subfolder in the
	// location in the output prefix. For example, for the input data stored at
	// s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform stores
	// the transformed data at
	// s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out. Batch
	// transform doesn't upload partially processed objects. For an input S3 object
	// that contains multiple records, it creates an .out file only if the transform
	// job succeeds on the entire file. When the input contains multiple S3 objects,
	// the batch transform job processes the listed S3 objects and uploads only the
	// output for successfully processed objects. If any object fails in the transform
	// job batch transform marks the job as failed to prompt investigation.
	//
	// This member is required.
	S3OutputPath *string

	// The MIME type used to specify the output data. Amazon SageMaker uses the MIME
	// type with each http call to transfer data from the transform job.
	Accept *string

	// Defines how to assemble the results of the transform job as a single S3 object.
	// Choose a format that is most convenient to you. To concatenate the results in
	// binary format, specify None. To add a newline character at the end of every
	// transformed record, specify Line.
	AssembleWith AssemblyType

	// The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to
	// encrypt the model artifacts at rest using Amazon S3 server-side encryption. The
	// KmsKeyId can be any of the following formats:
	//
	//     * Key ID:
	// 1234abcd-12ab-34cd-56ef-1234567890ab
	//
	//     * Key ARN:
	// arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
	//
	//
	// * Alias name: alias/ExampleAlias
	//
	//     * Alias name ARN:
	// arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
	//
	// If you don't provide a
	// KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your
	// role's account. For more information, see KMS-Managed Encryption Keys
	// (https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html) in the
	// Amazon Simple Storage Service Developer Guide. The KMS key policy must grant
	// permission to the IAM role that you specify in your CreateModel request. For
	// more information, see Using Key Policies in AWS KMS
	// (http://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html) in the
	// AWS Key Management Service Developer Guide.
	KmsKeyId *string
}

type TransformResources struct {

	// The number of ML compute instances to use in the transform job. For distributed
	// transform jobs, specify a value greater than 1. The default value is 1.
	//
	// This member is required.
	InstanceCount *int32

	// The ML compute instance type for the transform job. If you are using built-in
	// algorithms to transform moderately sized datasets, we recommend using
	// ml.m4.xlarge or ml.m5.large instance types.
	//
	// This member is required.
	InstanceType TransformInstanceType

	// The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to
	// encrypt model data on the storage volume attached to the ML compute instance(s)
	// that run the batch transform job. The VolumeKmsKeyId can be any of the following
	// formats:
	//
	//     * Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
	//
	//     * Key ARN:
	// arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
	//
	//
	// * Alias name: alias/ExampleAlias
	//
	//     * Alias name ARN:
	// arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
	VolumeKmsKeyId *string
}

type TransformS3DataSource struct {

	// If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker
	// uses all objects with the specified key name prefix for batch transform. If you
	// choose ManifestFile, S3Uri identifies an object that is a manifest file
	// containing a list of object keys that you want Amazon SageMaker to use for batch
	// transform. The following values are compatible: ManifestFile, S3Prefix The
	// following value is not compatible: AugmentedManifestFile
	//
	// This member is required.
	S3DataType S3DataType

	// Depending on the value specified for the S3DataType, identifies either a key
	// name prefix or a manifest. For example:
	//
	//     * A key name prefix might look like
	// this: s3://bucketname/exampleprefix.
	//
	//     * A manifest might look like this:
	// s3://bucketname/example.manifest The manifest is an S3 object which is a JSON
	// file with the following format: [ {"prefix":
	// "s3://customer_bucket/some/prefix/"},"relative/path/to/custdata-1","relative/path/custdata-2",..."relative/path/custdata-N"]
	// The preceding JSON matches the following S3Uris:
	// s3://customer_bucket/some/prefix/relative/path/to/custdata-1s3://customer_bucket/some/prefix/relative/path/custdata-2...s3://customer_bucket/some/prefix/relative/path/custdata-N
	// The complete set of S3Uris in this manifest constitutes the input data for the
	// channel for this datasource. The object that each S3Uris points to must be
	// readable by the IAM role that Amazon SageMaker uses to perform tasks on your
	// behalf.
	//
	// This member is required.
	S3Uri *string
}

type Trial struct {

	// Information about the user who created or modified an experiment, trial, or
	// trial component.
	CreatedBy *UserContext

	// When the trial was created.
	CreationTime *time.Time

	// The name of the trial as displayed. If DisplayName isn't specified, TrialName is
	// displayed.
	DisplayName *string

	// The name of the experiment the trial is part of.
	ExperimentName *string

	// Information about the user who created or modified an experiment, trial, or
	// trial component.
	LastModifiedBy *UserContext

	// Who last modified the trial.
	LastModifiedTime *time.Time

	// The source of the trial.
	Source *TrialSource

	// The list of tags that are associated with the trial. You can use Search API to
	// search on the tags.
	Tags []*Tag

	// The Amazon Resource Name (ARN) of the trial.
	TrialArn *string

	// A list of the components associated with the trial. For each component, a
	// summary of the component's properties is included.
	TrialComponentSummaries []*TrialComponentSimpleSummary

	// The name of the trial.
	TrialName *string
}

type TrialComponent struct {

	// Information about the user who created or modified an experiment, trial, or
	// trial component.
	CreatedBy *UserContext

	// When the component was created.
	CreationTime *time.Time

	// The name of the component as displayed. If DisplayName isn't specified,
	// TrialComponentName is displayed.
	DisplayName *string

	// When the component ended.
	EndTime *time.Time

	// The input artifacts of the component.
	InputArtifacts map[string]*TrialComponentArtifact

	// Information about the user who created or modified an experiment, trial, or
	// trial component.
	LastModifiedBy *UserContext

	// When the component was last modified.
	LastModifiedTime *time.Time

	// The metrics for the component.
	Metrics []*TrialComponentMetricSummary

	// The output artifacts of the component.
	OutputArtifacts map[string]*TrialComponentArtifact

	// The hyperparameters of the component.
	Parameters map[string]*TrialComponentParameterValue

	// An array of the parents of the component. A parent is a trial the component is
	// associated with and the experiment the trial is part of. A component might not
	// have any parents.
	Parents []*Parent

	// The Amazon Resource Name (ARN) and job type of the source of the component.
	Source *TrialComponentSource

	// Details of the source of the component.
	SourceDetail *TrialComponentSourceDetail

	// When the component started.
	StartTime *time.Time

	// The status of the trial component.
	Status *TrialComponentStatus

	// The list of tags that are associated with the component. You can use Search API
	// to search on the tags.
	Tags []*Tag

	// The Amazon Resource Name (ARN) of the trial component.
	TrialComponentArn *string

	// The name of the trial component.
	TrialComponentName *string
}

type TrialComponentArtifact struct {

	// The location of the artifact.
	//
	// This member is required.
	Value *string

	// The media type of the artifact, which indicates the type of data in the artifact
	// file. The media type consists of a type and a subtype concatenated with a slash
	// (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies
	// the category of the media. The subtype specifies the kind of data.
	MediaType *string
}

type TrialComponentMetricSummary struct {

	// The average value of the metric.
	Avg *float64

	// The number of samples used to generate the metric.
	Count *int32

	// The most recent value of the metric.
	Last *float64

	// The maximum value of the metric.
	Max *float64

	// The name of the metric.
	MetricName *string

	// The minimum value of the metric.
	Min *float64

	// The Amazon Resource Name (ARN) of the source.
	SourceArn *string

	// The standard deviation of the metric.
	StdDev *float64

	// When the metric was last updated.
	TimeStamp *time.Time
}

type TrialComponentParameterValue struct {

	// The numeric value of a numeric hyperparameter. If you specify a value for this
	// parameter, you can't specify the StringValue parameter.
	NumberValue *float64

	// The string value of a categorical hyperparameter. If you specify a value for
	// this parameter, you can't specify the NumberValue parameter.
	StringValue *string
}

type TrialComponentPrimaryStatus string

const (
	TrialComponentPrimaryStatusIn_progress TrialComponentPrimaryStatus = "InProgress"
	TrialComponentPrimaryStatusCompleted   TrialComponentPrimaryStatus = "Completed"
	TrialComponentPrimaryStatusFailed      TrialComponentPrimaryStatus = "Failed"
	TrialComponentPrimaryStatusStopping    TrialComponentPrimaryStatus = "Stopping"
	TrialComponentPrimaryStatusStopped     TrialComponentPrimaryStatus = "Stopped"
)

type TrialComponentSimpleSummary struct {

	// Information about the user who created or modified an experiment, trial, or
	// trial component.
	CreatedBy *UserContext

	// When the component was created.
	CreationTime *time.Time

	// The Amazon Resource Name (ARN) of the trial component.
	TrialComponentArn *string

	// The name of the trial component.
	TrialComponentName *string

	// The Amazon Resource Name (ARN) and job type of the source of a trial component.
	TrialComponentSource *TrialComponentSource
}

type TrialComponentSource struct {

	// The source ARN.
	//
	// This member is required.
	SourceArn *string

	// The source job type.
	SourceType *string
}

type TrialComponentSourceDetail struct {

	// Information about a processing job that's the source of a trial component.
	ProcessingJob *ProcessingJob

	// The Amazon Resource Name (ARN) of the source.
	SourceArn *string

	// Information about a training job that's the source of a trial component.
	TrainingJob *TrainingJob

	// Information about a transform job that's the source of the trial component.
	TransformJob *TransformJob
}

type TrialComponentStatus struct {

	// If the component failed, a message describing why.
	Message *string

	// The status of the trial component.
	PrimaryStatus TrialComponentPrimaryStatus
}

type TrialComponentSummary struct {

	// Who created the component.
	CreatedBy *UserContext

	// When the component was created.
	CreationTime *time.Time

	// The name of the component as displayed. If DisplayName isn't specified,
	// TrialComponentName is displayed.
	DisplayName *string

	// When the component ended.
	EndTime *time.Time

	// Who last modified the component.
	LastModifiedBy *UserContext

	// When the component was last modified.
	LastModifiedTime *time.Time

	// When the component started.
	StartTime *time.Time

	// The status of the component. States include:
	//
	//     * InProgress
	//
	//     *
	// Completed
	//
	//     * Failed
	Status *TrialComponentStatus

	// The ARN of the trial component.
	TrialComponentArn *string

	// The name of the trial component.
	TrialComponentName *string

	// The Amazon Resource Name (ARN) and job type of the source of a trial component.
	TrialComponentSource *TrialComponentSource
}

type TrialSource struct {

	// The Amazon Resource Name (ARN) of the source.
	//
	// This member is required.
	SourceArn *string

	// The source job type.
	SourceType *string
}

type TrialSummary struct {

	// When the trial was created.
	CreationTime *time.Time

	// The name of the trial as displayed. If DisplayName isn't specified, TrialName is
	// displayed.
	DisplayName *string

	// When the trial was last modified.
	LastModifiedTime *time.Time

	// The Amazon Resource Name (ARN) of the trial.
	TrialArn *string

	// The name of the trial.
	TrialName *string

	// The source of the trial.
	TrialSource *TrialSource
}

type TuningJobCompletionCriteria struct {

	// The objective metric's value.
	//
	// This member is required.
	TargetObjectiveMetricValue *float32
}

type USD struct {

	// The fractional portion, in cents, of the amount.
	Cents *int32

	// The whole number of dollars in the amount.
	Dollars *int32

	// Fractions of a cent, in tenths.
	TenthFractionsOfACent *int32
}

type UiConfig struct {

	// The ARN of the worker task template used to render the worker UI and tools for
	// labeling job tasks. Use this parameter when you are creating a labeling job for
	// 3D point cloud and video fram labeling jobs. Use your labeling job task type to
	// select one of the following ARN's and use it with this parameter when you create
	// a labeling job. Replace aws-region with the AWS region you are creating your
	// labeling job in. 3D Point Cloud HumanTaskUiArns Use this HumanTaskUiArn for 3D
	// point cloud object detection and 3D point cloud object detection adjustment
	// labeling jobs.
	//
	//     *
	// arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection
	//
	// Use
	// this HumanTaskUiArn for 3D point cloud object tracking and 3D point cloud object
	// tracking adjustment labeling jobs.
	//
	//     *
	// arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking
	//
	// Use
	// this HumanTaskUiArn for 3D point cloud semantic segmentation and 3D point cloud
	// semantic segmentation adjustment labeling jobs.
	//
	//     *
	// arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation
	//
	// Video
	// Frame HumanTaskUiArns Use this HumanTaskUiArn for video frame object detection
	// and video frame object detection adjustment labeling jobs.
	//
	//     *
	// arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetection
	//
	// Use
	// this HumanTaskUiArn for video frame object tracking and video frame object
	// tracking adjustment labeling jobs.
	//
	//     *
	// arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTracking
	HumanTaskUiArn *string

	// The Amazon S3 bucket location of the UI template, or worker task template. This
	// is the template used to render the worker UI and tools for labeling job tasks.
	// For more information about the contents of a UI template, see  Creating Your
	// Custom Labeling Task Template
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates-step2.html).
	UiTemplateS3Uri *string
}

type UiTemplate struct {

	// The content of the Liquid template for the worker user interface.
	//
	// This member is required.
	Content *string
}

type UiTemplateInfo struct {

	// The SHA-256 digest of the contents of the template.
	ContentSha256 *string

	// The URL for the user interface template.
	Url *string
}

type UserContext struct {

	// The domain associated with the user.
	DomainId *string

	// The Amazon Resource Name (ARN) of the user's profile.
	UserProfileArn *string

	// The name of the user's profile.
	UserProfileName *string
}

type UserProfileDetails struct {

	// The creation time.
	CreationTime *time.Time

	// The domain ID.
	DomainId *string

	// The last modified time.
	LastModifiedTime *time.Time

	// The status.
	Status UserProfileStatus

	// The user profile name.
	UserProfileName *string
}

type UserProfileSortKey string

const (
	UserProfileSortKeyCreationtime     UserProfileSortKey = "CreationTime"
	UserProfileSortKeyLastmodifiedtime UserProfileSortKey = "LastModifiedTime"
)

type UserProfileStatus string

const (
	UserProfileStatusDeleting  UserProfileStatus = "Deleting"
	UserProfileStatusFailed    UserProfileStatus = "Failed"
	UserProfileStatusInservice UserProfileStatus = "InService"
	UserProfileStatusPending   UserProfileStatus = "Pending"
)

type UserSettings struct {

	// The execution role for the user.
	ExecutionRole *string

	// The Jupyter server's app settings.
	JupyterServerAppSettings *JupyterServerAppSettings

	// The kernel gateway app settings.
	KernelGatewayAppSettings *KernelGatewayAppSettings

	// The security groups.
	SecurityGroups []*string

	// The sharing settings.
	SharingSettings *SharingSettings

	// The TensorBoard app settings.
	TensorBoardAppSettings *TensorBoardAppSettings
}

type VariantProperty struct {

	// The type of variant property. The supported values are:
	//
	//     *
	// DesiredInstanceCount: Overrides the existing variant instance counts using the
	// ProductionVariant$InitialInstanceCount values in the
	// CreateEndpointConfigInput$ProductionVariants.
	//
	//     * DesiredWeight: Overrides
	// the existing variant weights using the ProductionVariant$InitialVariantWeight
	// values in the CreateEndpointConfigInput$ProductionVariants.
	//
	//     *
	// DataCaptureConfig: (Not currently supported.)
	//
	// This member is required.
	VariantPropertyType VariantPropertyType
}

type VariantPropertyType string

const (
	VariantPropertyTypeDesiredinstancecount VariantPropertyType = "DesiredInstanceCount"
	VariantPropertyTypeDesiredweight        VariantPropertyType = "DesiredWeight"
	VariantPropertyTypeDatacaptureconfig    VariantPropertyType = "DataCaptureConfig"
)

type VpcConfig struct {

	// The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups
	// for the VPC that is specified in the Subnets field.
	//
	// This member is required.
	SecurityGroupIds []*string

	// The ID of the subnets in the VPC to which you want to connect your training job
	// or model. For information about the availability of specific instance types, see
	// Supported Instance Types and Availability Zones
	// (https://docs.aws.amazon.com/sagemaker/latest/dg/instance-types-az.html).
	//
	// This member is required.
	Subnets []*string
}

type Workforce struct {

	// The Amazon Resource Name (ARN) of the private workforce.
	//
	// This member is required.
	WorkforceArn *string

	// The name of the private workforce.
	//
	// This member is required.
	WorkforceName *string

	// The configuration of an Amazon Cognito workforce. A single Cognito workforce is
	// created using and corresponds to a single  Amazon Cognito user pool
	// (https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html).
	CognitoConfig *CognitoConfig

	// The date that the workforce is created.
	CreateDate *time.Time

	// The most recent date that was used to successfully add one or more IP address
	// ranges (CIDRs
	// (https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html)) to a
	// private workforce's allow list.
	LastUpdatedDate *time.Time

	// The configuration of an OIDC Identity Provider (IdP) private workforce.
	OidcConfig *OidcConfigForResponse

	// A list of one to ten IP address ranges (CIDRs
	// (https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html)) to be added
	// to the workforce allow list.
	SourceIpConfig *SourceIpConfig

	// The subdomain for your OIDC Identity Provider.
	SubDomain *string
}

type Workteam struct {

	// A description of the work team.
	//
	// This member is required.
	Description *string

	// The Amazon Cognito user groups that make up the work team.
	//
	// This member is required.
	MemberDefinitions []*MemberDefinition

	// The Amazon Resource Name (ARN) that identifies the work team.
	//
	// This member is required.
	WorkteamArn *string

	// The name of the work team.
	//
	// This member is required.
	WorkteamName *string

	// The date and time that the work team was created (timestamp).
	CreateDate *time.Time

	// The date and time that the work team was last updated (timestamp).
	LastUpdatedDate *time.Time

	// Configures SNS notifications of available or expiring work items for work teams.
	NotificationConfiguration *NotificationConfiguration

	// The Amazon Marketplace identifier for a vendor's work team.
	ProductListingIds []*string

	// The URI of the labeling job's user interface. Workers open this URI to start
	// labeling your data objects.
	SubDomain *string

	// The Amazon Resource Name (ARN) of the workforce.
	WorkforceArn *string
}