types

type ActionSource struct {

	// The URI of the source.
	//
	// This member is required.
	SourceUri *string

	// The ID of the source.
	SourceId *string

	// The type of the source.
	SourceType *string
	// contains filtered or unexported fields
}

type ActionStatus string

type ActionSummary struct {

	// The Amazon Resource Name (ARN) of the action.
	ActionArn *string

	// The name of the action.
	ActionName *string

	// The type of the action.
	ActionType *string

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

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

	// The source of the action.
	Source *ActionSource

	// The status of the action.
	Status ActionStatus
	// contains filtered or unexported fields
}

type AdditionalInferenceSpecificationDefinition struct {

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

	// A unique name to identify the additional inference specification. The name must
	// be unique within the list of your additional inference specifications for a
	// particular model package.
	//
	// This member is required.
	Name *string

	// A description of the additional Inference specification
	Description *string

	// The supported MIME types for the input data.
	SupportedContentTypes []string

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

	// The supported MIME types for the output data.
	SupportedResponseMIMETypes []string

	// A list of the instance types on which a transformation job can be run or on
	// which an endpoint can be deployed.
	SupportedTransformInstanceTypes []TransformInstanceType
	// contains filtered or unexported fields
}

type AdditionalS3DataSource struct {

	// The data type of the additional data source that you specify for use in
	// inference or training.
	//
	// This member is required.
	S3DataType AdditionalS3DataSourceDataType

	// The uniform resource identifier (URI) used to identify an additional data
	// source used in inference or training.
	//
	// This member is required.
	S3Uri *string

	// The type of compression used for an additional data source used in inference or
	// training. Specify None if your additional data source is not compressed.
	CompressionType CompressionType
	// contains filtered or unexported fields
}

type AdditionalS3DataSourceDataType string

type AgentVersion struct {

	// The number of Edge Manager agents.
	//
	// This member is required.
	AgentCount *int64

	// Version of the agent.
	//
	// This member is required.
	Version *string
	// contains filtered or unexported fields
}

type AggregationTransformationValue string

type Alarm struct {

	// The name of a CloudWatch alarm in your account.
	AlarmName *string
	// contains filtered or unexported fields
}

type AlgorithmSortBy string

type AlgorithmSpecification struct {

	// The training input mode that the algorithm supports. For more information about
	// input modes, see Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html)
	// . Pipe mode If an algorithm supports Pipe mode, Amazon SageMaker streams data
	// directly from Amazon S3 to the container. File mode If an algorithm supports
	// File mode, SageMaker downloads the training data from S3 to the provisioned ML
	// storage volume, and mounts the directory to the Docker volume for the training
	// container. You must provision the ML storage volume with sufficient capacity to
	// accommodate the data downloaded from S3. In addition to the training data, the
	// ML storage volume also stores the output model. The algorithm container uses the
	// ML storage volume to also store intermediate information, if any. For
	// distributed algorithms, training data is distributed uniformly. Your training
	// duration is predictable if the input data objects sizes are approximately the
	// same. 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 when one host in a training cluster is overloaded, thus becoming a
	// bottleneck in training. FastFile mode If an algorithm supports FastFile mode,
	// SageMaker streams data directly from S3 to the container with no code changes,
	// and provides file system access to the data. Users can author their training
	// script to interact with these files as if they were stored on disk. FastFile
	// mode works best when the data is read sequentially. Augmented manifest files
	// aren't supported. The startup time is lower when there are fewer files in the S3
	// bucket provided.
	//
	// 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 Amazon Web Services
	// Marketplace. You must specify either the algorithm name to the AlgorithmName
	// parameter or the image URI of the algorithm container to the TrainingImage
	// parameter. Note that the AlgorithmName parameter is mutually exclusive with the
	// TrainingImage parameter. If you specify a value for the AlgorithmName
	// parameter, you can't specify a value for TrainingImage , and vice versa. If you
	// specify values for both parameters, the training job might break; if you don't
	// specify any value for both parameters, the training job might raise a null
	// error.
	AlgorithmName *string

	// The arguments for a container used to run a training job. See How Amazon
	// SageMaker Runs Your Training Image (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo-dockerfile.html)
	// for additional information.
	ContainerArguments []string

	// The entrypoint script for a Docker container (https://docs.docker.com/engine/reference/builder/)
	// used to run a training job. This script takes precedence over the default train
	// processing instructions. See How Amazon SageMaker Runs Your Training Image (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo-dockerfile.html)
	// for more information.
	ContainerEntrypoint []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 SageMaker built-in algorithms
	//   - You use one of the following Prebuilt 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 (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_MetricDefinition.html)
	EnableSageMakerMetricsTimeSeries *bool

	// A list of metric definition objects. Each object specifies the metric name and
	// regular expressions used to parse algorithm logs. 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 SageMaker built-in algorithms, see
	// Docker Registry Paths and Example Code (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)
	// in the Amazon SageMaker developer guide. SageMaker supports both
	// registry/repository[:tag] and registry/repository[@digest] image path formats.
	// For more information about using your custom training container, see Using Your
	// Own Algorithms with Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html)
	// . You must specify either the algorithm name to the AlgorithmName parameter or
	// the image URI of the algorithm container to the TrainingImage parameter. For
	// more information, see the note in the AlgorithmName parameter description.
	TrainingImage *string

	// The configuration to use an image from a private Docker registry for a training
	// job.
	TrainingImageConfig *TrainingImageConfig
	// contains filtered or unexported fields
}

type AlgorithmStatus string

type AlgorithmStatusDetails struct {

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

	// The status of algorithm validation.
	ValidationStatuses []AlgorithmStatusItem
	// contains filtered or unexported fields
}

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
	// contains filtered or unexported fields
}

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
	// contains filtered or unexported fields
}

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 SageMaker
	// runs to validate your algorithm.
	//
	// This member is required.
	TrainingJobDefinition *TrainingJobDefinition

	// The TransformJobDefinition object that describes the transform job that
	// SageMaker runs to validate your algorithm.
	TransformJobDefinition *TransformJobDefinition
	// contains filtered or unexported fields
}

type AlgorithmValidationSpecification struct {

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

	// The IAM roles that SageMaker uses to run the training jobs.
	//
	// This member is required.
	ValidationRole *string
	// contains filtered or unexported fields
}

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-BoundingBox
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox
	//   - arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox
	//   - arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox
	//   - arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox
	//   - arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox
	//   - arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox
	//   - arn: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-ImageMultiClass
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass
	//   - arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass
	//   - arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass
	//   - arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass
	//   - arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass
	//   - arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass
	//   - arn: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-ImageMultiClassMultiLabel
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel
	//   -
	//   arn: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-SemanticSegmentation
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation
	//   - arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation
	//   - arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation
	//   - arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation
	//   - arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation
	//   - arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation
	//   - arn: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-TextMultiClass
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass
	//   - arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass
	//   - arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass
	//   - arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass
	//   - arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass
	//   - arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass
	//   - arn: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-TextMultiClassMultiLabel
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel
	//   -
	//   arn: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-NamedEntityRecognition
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition
	//   - arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition
	//   - arn: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-VideoMultiClass
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClass
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClass
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClass
	//   - arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClass
	//   - arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClass
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClass
	//   - arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClass
	//   - arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClass
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClass
	//   - arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClass
	//   - arn: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-VideoObjectDetection
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetection
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetection
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetection
	//   - arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetection
	//   - arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetection
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetection
	//   - arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetection
	//   - arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetection
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetection
	//   - arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetection
	//   - arn: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-VideoObjectTracking
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTracking
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTracking
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTracking
	//   - arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTracking
	//   - arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTracking
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTracking
	//   - arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTracking
	//   - arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTracking
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTracking
	//   - arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTracking
	//   - arn: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-3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection
	//   -
	//   arn: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-3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking
	//   -
	//   arn: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-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation
	//   -
	//   arn: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-AdjustmentSemanticSegmentation
	//   -
	//   arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation
	//   -
	//   arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation
	//   -
	//   arn: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-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation
	//   -
	//   arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation
	// 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-AdjustmentBoundingBox
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox
	//   - arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox
	//   - arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox
	// 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-VerificationBoundingBox
	//   - arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox
	//   - arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox
	//   - arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox
	//   - arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox
	//   - arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox
	//   -
	//   arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox
	// 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-AdjustmentVideoObjectDetection
	//   -
	//   arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetection
	//   -
	//   arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetection
	//   -
	//   arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetection
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetection
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetection
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetection
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetection
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetection
	//   -
	//   arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetection
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetection
	//   -
	//   arn: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-AdjustmentVideoObjectTracking
	//   -
	//   arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTracking
	//   -
	//   arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTracking
	//   -
	//   arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTracking
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTracking
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTracking
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTracking
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTracking
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTracking
	//   -
	//   arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTracking
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTracking
	//   -
	//   arn: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-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetection
	//   -
	//   arn: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-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTracking
	//   -
	//   arn: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-3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//   -
	//   arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation
	//
	// This member is required.
	AnnotationConsolidationLambdaArn *string
	// contains filtered or unexported fields
}

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

	// Specifies the ARN's of a SageMaker image and SageMaker image version, and the
	// instance type that the version runs on.
	ResourceSpec *ResourceSpec

	// The name of the space.
	SpaceName *string

	// The status.
	Status AppStatus

	// The user profile name.
	UserProfileName *string
	// contains filtered or unexported fields
}

type AppImageConfigDetails struct {

	// The ARN of the AppImageConfig.
	AppImageConfigArn *string

	// The name of the AppImageConfig. Must be unique to your account.
	AppImageConfigName *string

	// The configuration for the file system and the runtime, such as the environment
	// variables and entry point.
	CodeEditorAppImageConfig *CodeEditorAppImageConfig

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

	// The configuration for the file system and the runtime, such as the environment
	// variables and entry point.
	JupyterLabAppImageConfig *JupyterLabAppImageConfig

	// The configuration for the file system and kernels in the SageMaker image.
	KernelGatewayImageConfig *KernelGatewayImageConfig

	// When the AppImageConfig was last modified.
	LastModifiedTime *time.Time
	// contains filtered or unexported fields
}

type AppImageConfigSortKey string

type AppInstanceType string

type AppNetworkAccessType string

type AppSecurityGroupManagement string

type AppSortKey string

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
	// contains filtered or unexported fields
}

type AppStatus string

type AppType string

type ArtifactSource struct {

	// The URI of the source.
	//
	// This member is required.
	SourceUri *string

	// A list of source types.
	SourceTypes []ArtifactSourceType
	// contains filtered or unexported fields
}

type ArtifactSourceIdType string

type ArtifactSourceType struct {

	// The type of ID.
	//
	// This member is required.
	SourceIdType ArtifactSourceIdType

	// The ID.
	//
	// This member is required.
	Value *string
	// contains filtered or unexported fields
}

type ArtifactSummary struct {

	// The Amazon Resource Name (ARN) of the artifact.
	ArtifactArn *string

	// The name of the artifact.
	ArtifactName *string

	// The type of the artifact.
	ArtifactType *string

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

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

	// The source of the artifact.
	Source *ArtifactSource
	// contains filtered or unexported fields
}

type AssemblyType string

type AssociationEdgeType string

type AssociationSummary struct {

	// The type of the association.
	AssociationType AssociationEdgeType

	// Information about the user who created or modified an experiment, trial, trial
	// component, lineage group, project, or model card.
	CreatedBy *UserContext

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

	// The Amazon Resource Name (ARN) of the destination.
	DestinationArn *string

	// The name of the destination.
	DestinationName *string

	// The destination type.
	DestinationType *string

	// The ARN of the source.
	SourceArn *string

	// The name of the source.
	SourceName *string

	// The source type.
	SourceType *string
	// contains filtered or unexported fields
}

type AsyncInferenceClientConfig struct {

	// The maximum number of concurrent requests sent by the SageMaker client to the
	// model container. If no value is provided, SageMaker chooses an optimal value.
	MaxConcurrentInvocationsPerInstance *int32
	// contains filtered or unexported fields
}

type AsyncInferenceConfig struct {

	// Specifies the configuration for asynchronous inference invocation outputs.
	//
	// This member is required.
	OutputConfig *AsyncInferenceOutputConfig

	// Configures the behavior of the client used by SageMaker to interact with the
	// model container during asynchronous inference.
	ClientConfig *AsyncInferenceClientConfig
	// contains filtered or unexported fields
}

type AsyncInferenceNotificationConfig struct {

	// Amazon SNS topic to post a notification to when inference fails. If no topic is
	// provided, no notification is sent on failure.
	ErrorTopic *string

	// The Amazon SNS topics where you want the inference response to be included. The
	// inference response is included only if the response size is less than or equal
	// to 128 KB.
	IncludeInferenceResponseIn []AsyncNotificationTopicTypes

	// Amazon SNS topic to post a notification to when inference completes
	// successfully. If no topic is provided, no notification is sent on success.
	SuccessTopic *string
	// contains filtered or unexported fields
}

type AsyncInferenceOutputConfig struct {

	// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key
	// that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
	KmsKeyId *string

	// Specifies the configuration for notifications of inference results for
	// asynchronous inference.
	NotificationConfig *AsyncInferenceNotificationConfig

	// The Amazon S3 location to upload failure inference responses to.
	S3FailurePath *string

	// The Amazon S3 location to upload inference responses to.
	S3OutputPath *string
	// contains filtered or unexported fields
}

type AsyncNotificationTopicTypes string

type AthenaDatasetDefinition struct {

	// The name of the data catalog used in Athena query execution.
	//
	// This member is required.
	Catalog *string

	// The name of the database used in the Athena query execution.
	//
	// This member is required.
	Database *string

	// The data storage format for Athena query results.
	//
	// This member is required.
	OutputFormat AthenaResultFormat

	// The location in Amazon S3 where Athena query results are stored.
	//
	// This member is required.
	OutputS3Uri *string

	// The SQL query statements, to be executed.
	//
	// This member is required.
	QueryString *string

	// The Amazon Web Services Key Management Service (Amazon Web Services KMS) key
	// that Amazon SageMaker uses to encrypt data generated from an Athena query
	// execution.
	KmsKeyId *string

	// The compression used for Athena query results.
	OutputCompression AthenaResultCompressionType

	// The name of the workgroup in which the Athena query is being started.
	WorkGroup *string
	// contains filtered or unexported fields
}

type AthenaResultCompressionType string

type AthenaResultFormat string

type AuthMode string

type AutoMLAlgorithm string

type AutoMLAlgorithmConfig struct {

	// The selection of algorithms run on a dataset to train the model candidates of
	// an Autopilot job. Selected algorithms must belong to the list corresponding to
	// the training mode set in AutoMLJobConfig.Mode (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobConfig.html#sagemaker-Type-AutoMLJobConfig-Mode)
	// ( ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.
	//   - In ENSEMBLING mode:
	//   - "catboost"
	//   - "extra-trees"
	//   - "fastai"
	//   - "lightgbm"
	//   - "linear-learner"
	//   - "nn-torch"
	//   - "randomforest"
	//   - "xgboost"
	//   - In HYPERPARAMETER_TUNING mode:
	//   - "linear-learner"
	//   - "mlp"
	//   - "xgboost"
	//
	// This member is required.
	AutoMLAlgorithms []AutoMLAlgorithm
	// contains filtered or unexported fields
}

type AutoMLCandidate struct {

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

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

	// Information about 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's status.
	//
	// This member is required.
	ObjectiveStatus ObjectiveStatus

	// The properties of an AutoML candidate job.
	CandidateProperties *CandidateProperties

	// The end time.
	EndTime *time.Time

	// The failure reason.
	FailureReason *string

	// The best candidate result from an AutoML training job.
	FinalAutoMLJobObjectiveMetric *FinalAutoMLJobObjectiveMetric

	// The mapping of all supported processing unit (CPU, GPU, etc...) to inference
	// container definitions for the candidate. This field is populated for the AutoML
	// jobs V2 (for example, for jobs created by calling CreateAutoMLJobV2 ) related to
	// image or text classification problem types only.
	InferenceContainerDefinitions map[string][]AutoMLContainerDefinition

	// Information about the recommended inference container definitions.
	InferenceContainers []AutoMLContainerDefinition
	// contains filtered or unexported fields
}

type AutoMLCandidateGenerationConfig struct {

	// Stores the configuration information for the selection of algorithms used to
	// train the model candidates. The list of available algorithms to choose from
	// depends on the training mode set in AutoMLJobConfig.Mode (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobConfig.html)
	// .
	//   - AlgorithmsConfig should not be set in AUTO training mode.
	//   - When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be
	//   set and one only. If the list of algorithms provided as values for
	//   AutoMLAlgorithms is empty, AutoMLCandidateGenerationConfig uses the full set
	//   of algorithms for the given training mode.
	//   - When AlgorithmsConfig is not provided, AutoMLCandidateGenerationConfig uses
	//   the full set of algorithms for the given training mode.
	// For the list of all algorithms per training mode, see  AutoMLAlgorithmConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLAlgorithmConfig.html)
	// . For more information on each algorithm, see the Algorithm support (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support)
	// section in Autopilot developer guide.
	AlgorithmsConfig []AutoMLAlgorithmConfig

	// A URL to the Amazon S3 data source containing selected features from the input
	// data source to run an Autopilot job. You can input FeatureAttributeNames
	// (optional) in JSON format as shown below: { "FeatureAttributeNames":["col1",
	// "col2", ...] } . You can also specify the data type of the feature (optional) in
	// the format shown below: { "FeatureDataTypes":{"col1":"numeric",
	// "col2":"categorical" ... } } These column keys may not include the target
	// column. In ensembling mode, Autopilot only supports the following data types:
	// numeric , categorical , text , and datetime . In HPO mode, Autopilot can support
	// numeric , categorical , text , datetime , and sequence . If only
	// FeatureDataTypes is provided, the column keys ( col1 , col2 ,..) should be a
	// subset of the column names in the input data. If both FeatureDataTypes and
	// FeatureAttributeNames are provided, then the column keys should be a subset of
	// the column names provided in FeatureAttributeNames . The key name
	// FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are
	// case sensitive and should be a list of strings containing unique values that are
	// a subset of the column names in the input data. The list of columns provided
	// must not include the target column.
	FeatureSpecificationS3Uri *string
	// contains filtered or unexported fields
}

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
	// contains filtered or unexported fields
}

type AutoMLChannel struct {

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

	// The channel type (optional) is an enum string. The default value is training .
	// Channels for training and validation must share the same ContentType and
	// TargetAttributeName . For information on specifying training and validation
	// channel types, see How to specify training and validation datasets (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-data-sources-training-or-validation)
	// .
	ChannelType AutoMLChannelType

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

	// The content type of the data from the input source. You can use
	// text/csv;header=present or x-application/vnd.amazon+parquet . The default value
	// is text/csv;header=present .
	ContentType *string

	// The data source for an AutoML channel.
	DataSource *AutoMLDataSource

	// If specified, this column name indicates which column of the dataset should be
	// treated as sample weights for use by the objective metric during the training,
	// evaluation, and the selection of the best model. This column is not considered
	// as a predictive feature. For more information on Autopilot metrics, see Metrics
	// and validation (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html)
	// . Sample weights should be numeric, non-negative, with larger values indicating
	// which rows are more important than others. Data points that have invalid or no
	// weight value are excluded. Support for sample weights is available in Ensembling (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLAlgorithmConfig.html)
	// mode only.
	SampleWeightAttributeName *string
	// contains filtered or unexported fields
}

type AutoMLChannelType string

type AutoMLContainerDefinition struct {

	// The Amazon Elastic Container Registry (Amazon ECR) path of the container. For
	// more information, see ContainerDefinition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html)
	// .
	//
	// This member is required.
	Image *string

	// The location of the model artifacts. For more information, see
	// ContainerDefinition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html)
	// .
	//
	// This member is required.
	ModelDataUrl *string

	// The environment variables to set in the container. For more information, see
	// ContainerDefinition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html)
	// .
	Environment map[string]string
	// contains filtered or unexported fields
}

type AutoMLDataSource struct {

	// The Amazon S3 location of the input data.
	//
	// This member is required.
	S3DataSource *AutoMLS3DataSource
	// contains filtered or unexported fields
}

type AutoMLDataSplitConfig struct {

	// The validation fraction (optional) is a float that specifies the portion of the
	// training dataset to be used for validation. The default value is 0.2, and values
	// must be greater than 0 and less than 1. We recommend setting this value to be
	// less than 0.5.
	ValidationFraction *float32
	// contains filtered or unexported fields
}

type AutoMLJobArtifacts struct {

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

	// The URL of the notebook location.
	DataExplorationNotebookLocation *string
	// contains filtered or unexported fields
}

type AutoMLJobChannel struct {

	// The type of channel. Defines whether the data are used for training or
	// validation. The default value is training . Channels for training and validation
	// must share the same ContentType The type of channel defaults to training for
	// the time-series forecasting problem type.
	ChannelType AutoMLChannelType

	// The allowed compression types depend on the input format and problem type. We
	// allow the compression type Gzip for S3Prefix inputs on tabular data only. For
	// all other inputs, the compression type should be None . If no compression type
	// is provided, we default to None .
	CompressionType CompressionType

	// The content type of the data from the input source. The following are the
	// allowed content types for different problems:
	//   - For tabular problem types: text/csv;header=present or
	//   x-application/vnd.amazon+parquet . The default value is
	//   text/csv;header=present .
	//   - For image classification: image/png , image/jpeg , or image/* . The default
	//   value is image/* .
	//   - For text classification: text/csv;header=present or
	//   x-application/vnd.amazon+parquet . The default value is
	//   text/csv;header=present .
	//   - For time-series forecasting: text/csv;header=present or
	//   x-application/vnd.amazon+parquet . The default value is
	//   text/csv;header=present .
	//   - For text generation (LLMs fine-tuning): text/csv;header=present or
	//   x-application/vnd.amazon+parquet . The default value is
	//   text/csv;header=present .
	ContentType *string

	// The data source for an AutoML channel (Required).
	DataSource *AutoMLDataSource
	// contains filtered or unexported fields
}

type AutoMLJobCompletionCriteria struct {

	// The maximum runtime, in seconds, an AutoML job has to complete. If an AutoML
	// job exceeds the maximum runtime, the job is stopped automatically and its
	// processing is ended gracefully. The AutoML job identifies the best model whose
	// training was completed and marks it as the best-performing model. Any unfinished
	// steps of the job, such as automatic one-click Autopilot model deployment, are
	// not completed.
	MaxAutoMLJobRuntimeInSeconds *int32

	// The maximum number of times a training job is allowed to run. For text and
	// image classification, time-series forecasting, as well as text generation (LLMs
	// fine-tuning) problem types, the supported value is 1. For tabular problem types,
	// the maximum value is 750.
	MaxCandidates *int32

	// The maximum time, in seconds, that each training job executed inside
	// hyperparameter tuning is allowed to run as part of a hyperparameter tuning job.
	// For more information, see the StoppingCondition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_StoppingCondition.html)
	// used by the CreateHyperParameterTuningJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateHyperParameterTuningJob.html)
	// action. For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field
	// controls the runtime of the job candidate. For TextGenerationJobConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TextClassificationJobConfig.html)
	// problem types, the maximum time defaults to 72 hours (259200 seconds).
	MaxRuntimePerTrainingJobInSeconds *int32
	// contains filtered or unexported fields
}

type AutoMLJobConfig struct {

	// The configuration for generating a candidate for an AutoML job (optional).
	CandidateGenerationConfig *AutoMLCandidateGenerationConfig

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

	// The configuration for splitting the input training dataset. Type:
	// AutoMLDataSplitConfig
	DataSplitConfig *AutoMLDataSplitConfig

	// The method that Autopilot uses to train the data. You can either specify the
	// mode manually or let Autopilot choose for you based on the dataset size by
	// selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets
	// smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones. The ENSEMBLING
	// mode uses a multi-stack ensemble model to predict classification and regression
	// tasks directly from your dataset. This machine learning mode combines several
	// base models to produce an optimal predictive model. It then uses a stacking
	// ensemble method to combine predictions from contributing members. A multi-stack
	// ensemble model can provide better performance over a single model by combining
	// the predictive capabilities of multiple models. See Autopilot algorithm support (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support)
	// for a list of algorithms supported by ENSEMBLING mode. The HYPERPARAMETER_TUNING
	// (HPO) mode uses the best hyperparameters to train the best version of a model.
	// HPO automatically selects an algorithm for the type of problem you want to
	// solve. Then HPO finds the best hyperparameters according to your objective
	// metric. See Autopilot algorithm support (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support)
	// for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
	Mode AutoMLMode

	// The security configuration for traffic encryption or Amazon VPC settings.
	SecurityConfig *AutoMLSecurityConfig
	// contains filtered or unexported fields
}

type AutoMLJobObjective struct {

	// The name of the objective metric used to measure the predictive quality of a
	// machine learning system. During training, the model's parameters are updated
	// iteratively to optimize its performance based on the feedback provided by the
	// objective metric when evaluating the model on the validation dataset. The list
	// of available metrics supported by Autopilot and the default metric applied when
	// you do not specify a metric name explicitly depend on the problem type.
	//   - For tabular problem types:
	//   - List of available metrics:
	//   - Regression: MAE , MSE , R2 , RMSE
	//   - Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , Precision ,
	//   Recall
	//   - Multiclass classification: Accuracy , BalancedAccuracy , F1macro ,
	//   PrecisionMacro , RecallMacro For a description of each metric, see Autopilot
	//   metrics for classification and regression (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html#autopilot-metrics)
	//   .
	//   - Default objective metrics:
	//   - Regression: MSE .
	//   - Binary classification: F1 .
	//   - Multiclass classification: Accuracy .
	//   - For image or text classification problem types:
	//   - List of available metrics: Accuracy For a description of each metric, see
	//   Autopilot metrics for text and image classification (https://docs.aws.amazon.com/sagemaker/latest/dg/text-classification-data-format-and-metric.html)
	//   .
	//   - Default objective metrics: Accuracy
	//   - For time-series forecasting problem types:
	//   - List of available metrics: RMSE , wQL , Average wQL , MASE , MAPE , WAPE For
	//   a description of each metric, see Autopilot metrics for time-series
	//   forecasting (https://docs.aws.amazon.com/sagemaker/latest/dg/timeseries-objective-metric.html)
	//   .
	//   - Default objective metrics: AverageWeightedQuantileLoss
	//   - For text generation problem types (LLMs fine-tuning): Fine-tuning language
	//   models in Autopilot does not require setting the AutoMLJobObjective field.
	//   Autopilot fine-tunes LLMs without requiring multiple candidates to be trained
	//   and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your
	//   target model to enhance a default objective metric, the cross-entropy loss.
	//   After fine-tuning a language model, you can evaluate the quality of its
	//   generated text using different metrics. For a list of the available metrics, see
	//   Metrics for fine-tuning LLMs in Autopilot (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-metrics.html)
	//   .
	//
	// This member is required.
	MetricName AutoMLMetricEnum
	// contains filtered or unexported fields
}

type AutoMLJobObjectiveType string

type AutoMLJobSecondaryStatus string

type AutoMLJobStatus string

type AutoMLJobStepMetadata struct {

	// The Amazon Resource Name (ARN) of the AutoML job.
	Arn *string
	// contains filtered or unexported fields
}

type AutoMLJobSummary struct {

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

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

	// The secondary status of the AutoML job.
	//
	// This member is required.
	AutoMLJobSecondaryStatus AutoMLJobSecondaryStatus

	// The status of the AutoML job.
	//
	// This member is required.
	AutoMLJobStatus AutoMLJobStatus

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

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

	// The end time of an AutoML job.
	EndTime *time.Time

	// The failure reason of an AutoML job.
	FailureReason *string

	// The list of reasons for partial failures within an AutoML job.
	PartialFailureReasons []AutoMLPartialFailureReason
	// contains filtered or unexported fields
}

type AutoMLMetricEnum string

type AutoMLMetricExtendedEnum string

type AutoMLMode string

type AutoMLOutputDataConfig struct {

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

	// The Key Management Service encryption key ID.
	KmsKeyId *string
	// contains filtered or unexported fields
}

type AutoMLPartialFailureReason struct {

	// The message containing the reason for a partial failure of an AutoML job.
	PartialFailureMessage *string
	// contains filtered or unexported fields
}

type AutoMLProblemTypeConfig interface {
	// contains filtered or unexported methods
}

type AutoMLProblemTypeConfigMemberImageClassificationJobConfig struct {
	Value ImageClassificationJobConfig
	// contains filtered or unexported fields
}

type AutoMLProblemTypeConfigMemberTabularJobConfig struct {
	Value TabularJobConfig
	// contains filtered or unexported fields
}

type AutoMLProblemTypeConfigMemberTextClassificationJobConfig struct {
	Value TextClassificationJobConfig
	// contains filtered or unexported fields
}

type AutoMLProblemTypeConfigMemberTextGenerationJobConfig struct {
	Value TextGenerationJobConfig
	// contains filtered or unexported fields
}

type AutoMLProblemTypeConfigMemberTimeSeriesForecastingJobConfig struct {
	Value TimeSeriesForecastingJobConfig
	// contains filtered or unexported fields
}

type AutoMLProblemTypeConfigName string

type AutoMLProblemTypeResolvedAttributes interface {
	// contains filtered or unexported methods
}

type AutoMLProblemTypeResolvedAttributesMemberTabularResolvedAttributes struct {
	Value TabularResolvedAttributes
	// contains filtered or unexported fields
}

type AutoMLProblemTypeResolvedAttributesMemberTextGenerationResolvedAttributes struct {
	Value TextGenerationResolvedAttributes
	// contains filtered or unexported fields
}

type AutoMLProcessingUnit string

type AutoMLResolvedAttributes struct {

	// Specifies a metric to minimize or maximize as the objective of an AutoML job.
	AutoMLJobObjective *AutoMLJobObjective

	// Defines the resolved attributes specific to a problem type.
	AutoMLProblemTypeResolvedAttributes AutoMLProblemTypeResolvedAttributes

	// How long a job is allowed to run, or how many candidates a job is allowed to
	// generate.
	CompletionCriteria *AutoMLJobCompletionCriteria
	// contains filtered or unexported fields
}

type AutoMLS3DataSource struct {

	// The data type.
	//   - If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses
	//   all objects that match the specified key name prefix for model training. The
	//   S3Prefix should have the following format:
	//   s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
	//   - If you choose ManifestFile , S3Uri identifies an object that is a manifest
	//   file containing a list of object keys that you want SageMaker to use for model
	//   training. A ManifestFile should have the format shown below: [ {"prefix":
	//   "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},
	//   "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",
	//   "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ...
	//   "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
	//   - 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 is available for V2 API
	//   jobs only (for example, for jobs created by calling CreateAutoMLJobV2 ). Here
	//   is a minimal, single-record example of an AugmentedManifestFile :
	//   {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",
	//   "label-metadata": {"class-name": "cat" } For more information on
	//   AugmentedManifestFile , see Provide Dataset Metadata to Training Jobs with an
	//   Augmented Manifest File (https://docs.aws.amazon.com/sagemaker/latest/dg/augmented-manifest.html)
	//   .
	//
	// This member is required.
	S3DataType AutoMLS3DataType

	// The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or
	// ManifestFile depending on the data type.
	//
	// This member is required.
	S3Uri *string
	// contains filtered or unexported fields
}

type AutoMLS3DataType string

type AutoMLSecurityConfig struct {

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

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

	// The VPC configuration.
	VpcConfig *VpcConfig
	// contains filtered or unexported fields
}

type AutoMLSortBy string

type AutoMLSortOrder string

type AutoParameter struct {

	// The name of the hyperparameter to optimize using Autotune.
	//
	// This member is required.
	Name *string

	// An example value of the hyperparameter to optimize using Autotune.
	//
	// This member is required.
	ValueHint *string
	// contains filtered or unexported fields
}

type AutoRollbackConfig struct {

	// List of CloudWatch alarms in your account that are configured to monitor
	// metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker
	// rolls back the deployment.
	Alarms []Alarm
	// contains filtered or unexported fields
}

type Autotune struct {

	// Set Mode to Enabled if you want to use Autotune.
	//
	// This member is required.
	Mode AutotuneMode
	// contains filtered or unexported fields
}

type AutotuneMode string

type AwsManagedHumanLoopRequestSource string

type BatchDataCaptureConfig struct {

	// The Amazon S3 location being used to capture the data.
	//
	// This member is required.
	DestinationS3Uri *string

	// Flag that indicates whether to append inference id to the output.
	GenerateInferenceId *bool

	// The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service
	// key that SageMaker uses to encrypt data on the storage volume attached to the ML
	// compute instance that hosts the batch transform job. 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
	KmsKeyId *string
	// contains filtered or unexported fields
}

type BatchDescribeModelPackageError struct {

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

	//
	//
	// This member is required.
	ErrorResponse *string
	// contains filtered or unexported fields
}

type BatchDescribeModelPackageSummary struct {

	// The creation time of the mortgage package summary.
	//
	// This member is required.
	CreationTime *time.Time

	// Defines how to perform inference generation after a training job is run.
	//
	// This member is required.
	InferenceSpecification *InferenceSpecification

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

	// The group name for the model package
	//
	// This member is required.
	ModelPackageGroupName *string

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

	// The approval status of the model.
	ModelApprovalStatus ModelApprovalStatus

	// The description of the model package.
	ModelPackageDescription *string

	// The version number of a versioned model.
	ModelPackageVersion *int32
	// contains filtered or unexported fields
}

type BatchStrategy string

type BatchTransformInput struct {

	// The Amazon S3 location being used to capture the data.
	//
	// This member is required.
	DataCapturedDestinationS3Uri *string

	// The dataset format for your batch transform job.
	//
	// This member is required.
	DatasetFormat *MonitoringDatasetFormat

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

	// If specified, monitoring jobs subtract this time from the end time. For
	// information about using offsets for scheduling monitoring jobs, see Schedule
	// Model Quality Monitoring Jobs (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-model-quality-schedule.html)
	// .
	EndTimeOffset *string

	// The attributes of the input data to exclude from the analysis.
	ExcludeFeaturesAttribute *string

	// The attributes of the input data that are the input features.
	FeaturesAttribute *string

	// The attribute of the input data that represents the ground truth label.
	InferenceAttribute *string

	// In a classification problem, the attribute that represents the class
	// probability.
	ProbabilityAttribute *string

	// The threshold for the class probability to be evaluated as a positive result.
	ProbabilityThresholdAttribute *float64

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

	// Whether the Pipe or File is used as the input mode for transferring 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

	// If specified, monitoring jobs substract this time from the start time. For
	// information about using offsets for scheduling monitoring jobs, see Schedule
	// Model Quality Monitoring Jobs (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-model-quality-schedule.html)
	// .
	StartTimeOffset *string
	// contains filtered or unexported fields
}

type BestObjectiveNotImproving struct {

	// The number of training jobs that have failed to improve model performance by 1%
	// or greater over prior training jobs as evaluated against an objective function.
	MaxNumberOfTrainingJobsNotImproving *int32
	// contains filtered or unexported fields
}

type Bias struct {

	// The post-training bias report for a model.
	PostTrainingReport *MetricsSource

	// The pre-training bias report for a model.
	PreTrainingReport *MetricsSource

	// The bias report for a model
	Report *MetricsSource
	// contains filtered or unexported fields
}

type BlueGreenUpdatePolicy struct {

	// Defines the traffic routing strategy to shift traffic from the old fleet to the
	// new fleet during an endpoint deployment.
	//
	// This member is required.
	TrafficRoutingConfiguration *TrafficRoutingConfig

	// Maximum execution timeout for the deployment. Note that the timeout value
	// should be larger than the total waiting time specified in
	// TerminationWaitInSeconds and WaitIntervalInSeconds .
	MaximumExecutionTimeoutInSeconds *int32

	// Additional waiting time in seconds after the completion of an endpoint
	// deployment before terminating the old endpoint fleet. Default is 0.
	TerminationWaitInSeconds *int32
	// contains filtered or unexported fields
}

type BooleanOperator string

type CacheHitResult struct {

	// The Amazon Resource Name (ARN) of the pipeline execution.
	SourcePipelineExecutionArn *string
	// contains filtered or unexported fields
}

type CallbackStepMetadata struct {

	// The pipeline generated token from the Amazon SQS queue.
	CallbackToken *string

	// A list of the output parameters of the callback step.
	OutputParameters []OutputParameter

	// The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the
	// callback step.
	SqsQueueUrl *string
	// contains filtered or unexported fields
}

type CandidateArtifactLocations struct {

	// The Amazon S3 prefix to the explainability artifacts generated for the AutoML
	// candidate.
	//
	// This member is required.
	Explainability *string

	// The Amazon S3 prefix to the accuracy metrics and the inference results observed
	// over the testing window. Available only for the time-series forecasting problem
	// type.
	BacktestResults *string

	// The Amazon S3 prefix to the model insight artifacts generated for the AutoML
	// candidate.
	ModelInsights *string
	// contains filtered or unexported fields
}

type CandidateGenerationConfig struct {

	// Stores the configuration information for the selection of algorithms used to
	// train model candidates on tabular data. The list of available algorithms to
	// choose from depends on the training mode set in TabularJobConfig.Mode (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TabularJobConfig.html)
	// .
	//   - AlgorithmsConfig should not be set in AUTO training mode.
	//   - When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be
	//   set and one only. If the list of algorithms provided as values for
	//   AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of
	//   algorithms for the given training mode.
	//   - When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the
	//   full set of algorithms for the given training mode.
	// For the list of all algorithms per problem type and training mode, see
	// AutoMLAlgorithmConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLAlgorithmConfig.html)
	// . For more information on each algorithm, see the Algorithm support (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support)
	// section in Autopilot developer guide.
	AlgorithmsConfig []AutoMLAlgorithmConfig
	// contains filtered or unexported fields
}

type CandidateProperties struct {

	// The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
	CandidateArtifactLocations *CandidateArtifactLocations

	// Information about the candidate metrics for an AutoML job.
	CandidateMetrics []MetricDatum
	// contains filtered or unexported fields
}

type CandidateSortBy string

type CandidateStatus string

type CandidateStepType string

type CanvasAppSettings struct {

	// The model deployment settings for the SageMaker Canvas application.
	DirectDeploySettings *DirectDeploySettings

	// The generative AI settings for the SageMaker Canvas application.
	GenerativeAiSettings *GenerativeAiSettings

	// The settings for connecting to an external data source with OAuth.
	IdentityProviderOAuthSettings []IdentityProviderOAuthSetting

	// The settings for document querying.
	KendraSettings *KendraSettings

	// The model registry settings for the SageMaker Canvas application.
	ModelRegisterSettings *ModelRegisterSettings

	// Time series forecast settings for the SageMaker Canvas application.
	TimeSeriesForecastingSettings *TimeSeriesForecastingSettings

	// The workspace settings for the SageMaker Canvas application.
	WorkspaceSettings *WorkspaceSettings
	// contains filtered or unexported fields
}

type CapacitySize struct {

	// Specifies the endpoint capacity type.
	//   - INSTANCE_COUNT : The endpoint activates based on the number of instances.
	//   - CAPACITY_PERCENT : The endpoint activates based on the specified percentage
	//   of capacity.
	//
	// This member is required.
	Type CapacitySizeType

	// Defines the capacity size, either as a number of instances or a capacity
	// percentage.
	//
	// This member is required.
	Value *int32
	// contains filtered or unexported fields
}

type CapacitySizeType string

type CaptureContentTypeHeader struct {

	// The list of all content type headers that Amazon SageMaker will treat as CSV
	// and capture accordingly.
	CsvContentTypes []string

	// The list of all content type headers that SageMaker will treat as JSON and
	// capture accordingly.
	JsonContentTypes []string
	// contains filtered or unexported fields
}

type CaptureMode string

type CaptureOption struct {

	// Specify the boundary of data to capture.
	//
	// This member is required.
	CaptureMode CaptureMode
	// contains filtered or unexported fields
}

type CaptureStatus string

type CategoricalParameter struct {

	// The Name of the environment variable.
	//
	// This member is required.
	Name *string

	// The list of values you can pass.
	//
	// This member is required.
	Value []string
	// contains filtered or unexported fields
}

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
	// contains filtered or unexported fields
}

type CategoricalParameterRangeSpecification struct {

	// The allowed categories for the hyperparameter.
	//
	// This member is required.
	Values []string
	// contains filtered or unexported fields
}

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 , SageMaker uses the value set for
	// TrainingInputMode . Use this parameter to override the TrainingInputMode
	// setting in a AlgorithmSpecification (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AlgorithmSpecification.html)
	// 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, 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
	// contains filtered or unexported fields
}

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
	// contains filtered or unexported fields
}

type CheckpointConfig struct {

	// Identifies the S3 path where you want 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
	// contains filtered or unexported fields
}

type ClarifyCheckStepMetadata struct {

	// The Amazon S3 URI of baseline constraints file to be used for the drift check.
	BaselineUsedForDriftCheckConstraints *string

	// The Amazon S3 URI of the newly calculated baseline constraints file.
	CalculatedBaselineConstraints *string

	// The Amazon Resource Name (ARN) of the check processing job that was run by this
	// step's execution.
	CheckJobArn *string

	// The type of the Clarify Check step
	CheckType *string

	// The model package group name.
	ModelPackageGroupName *string

	// This flag indicates if a newly calculated baseline can be accessed through step
	// properties BaselineUsedForDriftCheckConstraints and
	// BaselineUsedForDriftCheckStatistics . If it is set to False , the previous
	// baseline of the configured check type must also be available. These can be
	// accessed through the BaselineUsedForDriftCheckConstraints property.
	RegisterNewBaseline *bool

	// This flag indicates if the drift check against the previous baseline will be
	// skipped or not. If it is set to False , the previous baseline of the configured
	// check type must be available.
	SkipCheck *bool

	// The Amazon S3 URI of the violation report if violations are detected.
	ViolationReport *string
	// contains filtered or unexported fields
}

type ClarifyExplainerConfig struct {

	// The configuration for SHAP analysis.
	//
	// This member is required.
	ShapConfig *ClarifyShapConfig

	// A JMESPath boolean expression used to filter which records to explain.
	// Explanations are activated by default. See EnableExplanations (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable)
	// for additional information.
	EnableExplanations *string

	// The inference configuration parameter for the model container.
	InferenceConfig *ClarifyInferenceConfig
	// contains filtered or unexported fields
}

type ClarifyFeatureType string

type ClarifyInferenceConfig struct {

	// A template string used to format a JSON record into an acceptable model
	// container input. For example, a ContentTemplate string
	// '{"myfeatures":$features}' will format a list of features [1,2,3] into the
	// record string '{"myfeatures":[1,2,3]}' . Required only when the model container
	// input is in JSON Lines format.
	ContentTemplate *string

	// The names of the features. If provided, these are included in the endpoint
	// response payload to help readability of the InvokeEndpoint output. See the
	// Response (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-invoke-endpoint.html#clarify-online-explainability-response)
	// section under Invoke the endpoint in the Developer Guide for more information.
	FeatureHeaders []string

	// A list of data types of the features (optional). Applicable only to NLP
	// explainability. If provided, FeatureTypes must have at least one 'text' string
	// (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers
	// the feature types based on the baseline data. The feature types are included in
	// the endpoint response payload. For additional information see the response (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-invoke-endpoint.html#clarify-online-explainability-response)
	// section under Invoke the endpoint in the Developer Guide for more information.
	FeatureTypes []ClarifyFeatureType

	// Provides the JMESPath expression to extract the features from a model container
	// input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath
	// expression 'myfeatures' , it extracts a list of features [1,2,3] from request
	// data '{"myfeatures":[1,2,3]}' .
	FeaturesAttribute *string

	// A JMESPath expression used to locate the list of label headers in the model
	// container output. Example: If the model container output of a batch request is
	// '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set
	// LabelAttribute to 'labels' to extract the list of label headers
	// ["cat","dog","fish"]
	LabelAttribute *string

	// For multiclass classification problems, the label headers are the names of the
	// classes. Otherwise, the label header is the name of the predicted label. These
	// are used to help readability for the output of the InvokeEndpoint API. See the
	// response (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-invoke-endpoint.html#clarify-online-explainability-response)
	// section under Invoke the endpoint in the Developer Guide for more information.
	// If there are no label headers in the model container output, provide them
	// manually using this parameter.
	LabelHeaders []string

	// A zero-based index used to extract a label header or list of label headers from
	// model container output in CSV format. Example for a multiclass model: If the
	// model container output consists of label headers followed by probabilities:
	// '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select
	// the label headers ['cat','dog','fish'] .
	LabelIndex *int32

	// The maximum payload size (MB) allowed of a request from the explainer to the
	// model container. Defaults to 6 MB.
	MaxPayloadInMB *int32

	// The maximum number of records in a request that the model container can process
	// when querying the model container for the predictions of a synthetic dataset (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-synthetic)
	// . A record is a unit of input data that inference can be made on, for example, a
	// single line in CSV data. If MaxRecordCount is 1 , the model container expects
	// one record per request. A value of 2 or greater means that the model expects
	// batch requests, which can reduce overhead and speed up the inferencing process.
	// If this parameter is not provided, the explainer will tune the record count per
	// request according to the model container's capacity at runtime.
	MaxRecordCount *int32

	// A JMESPath expression used to extract the probability (or score) from the model
	// container output if the model container is in JSON Lines format. Example: If the
	// model container output of a single request is
	// '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to
	// 'probability' .
	ProbabilityAttribute *string

	// A zero-based index used to extract a probability value (score) or list from
	// model container output in CSV format. If this value is not provided, the entire
	// model container output will be treated as a probability value (score) or list.
	// Example for a single class model: If the model container output consists of a
	// string-formatted prediction label followed by its probability: '1,0.6' , set
	// ProbabilityIndex to 1 to select the probability value 0.6 . Example for a
	// multiclass model: If the model container output consists of a string-formatted
	// prediction label followed by its probability:
	// '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to
	// select the probability values [0.1,0.6,0.3] .
	ProbabilityIndex *int32
	// contains filtered or unexported fields
}

type ClarifyShapBaselineConfig struct {

	// The MIME type of the baseline data. Choose from 'text/csv' or
	// 'application/jsonlines' . Defaults to 'text/csv' .
	MimeType *string

	// The inline SHAP baseline data in string format. ShapBaseline can have one or
	// multiple records to be used as the baseline dataset. The format of the SHAP
	// baseline file should be the same format as the training dataset. For example, if
	// the training dataset is in CSV format and each record contains four features,
	// and all features are numerical, then the format of the baseline data should also
	// share these characteristics. For natural language processing (NLP) of text
	// columns, the baseline value should be the value used to replace the unit of text
	// specified by the Granularity of the TextConfig parameter. The size limit for
	// ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide
	// more than 4 KB of baseline data.
	ShapBaseline *string

	// The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline
	// file is stored. The format of the SHAP baseline file should be the same format
	// as the format of the training dataset. For example, if the training dataset is
	// in CSV format, and each record in the training dataset has four features, and
	// all features are numerical, then the baseline file should also have this same
	// format. Each record should contain only the features. If you are using a virtual
	// private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For
	// more information about setting up endpoints with Amazon Virtual Private Cloud,
	// see Give SageMaker access to Resources in your Amazon Virtual Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/infrastructure-give-access.html)
	// .
	ShapBaselineUri *string
	// contains filtered or unexported fields
}

type ClarifyShapConfig struct {

	// The configuration for the SHAP baseline of the Kernal SHAP algorithm.
	//
	// This member is required.
	ShapBaselineConfig *ClarifyShapBaselineConfig

	// The number of samples to be used for analysis by the Kernal SHAP algorithm. The
	// number of samples determines the size of the synthetic dataset, which has an
	// impact on latency of explainability requests. For more information, see the
	// Synthetic data of Configure and create an endpoint (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html)
	// .
	NumberOfSamples *int32

	// The starting value used to initialize the random number generator in the
	// explainer. Provide a value for this parameter to obtain a deterministic SHAP
	// result.
	Seed *int32

	// A parameter that indicates if text features are treated as text and
	// explanations are provided for individual units of text. Required for natural
	// language processing (NLP) explainability only.
	TextConfig *ClarifyTextConfig

	// A Boolean toggle to indicate if you want to use the logit function (true) or
	// log-odds units (false) for model predictions. Defaults to false.
	UseLogit *bool
	// contains filtered or unexported fields
}

type ClarifyTextConfig struct {

	// The unit of granularity for the analysis of text features. For example, if the
	// unit is 'token' , then each token (like a word in English) of the text is
	// treated as a feature. SHAP values are computed for each unit/feature.
	//
	// This member is required.
	Granularity ClarifyTextGranularity

	// Specifies the language of the text features in ISO 639-1 (https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes)
	// or ISO 639-3 (https://en.wikipedia.org/wiki/ISO_639-3) code of a supported
	// language. For a mix of multiple languages, use code 'xx' .
	//
	// This member is required.
	Language ClarifyTextLanguage
	// contains filtered or unexported fields
}

type ClarifyTextGranularity string

type ClarifyTextLanguage string

type ClusterInstanceGroupDetails struct {

	// The number of instances that are currently in the instance group of a SageMaker
	// HyperPod cluster.
	CurrentCount *int32

	// The execution role for the instance group to assume.
	ExecutionRole *string

	// The name of the instance group of a SageMaker HyperPod cluster.
	InstanceGroupName *string

	// The instance type of the instance group of a SageMaker HyperPod cluster.
	InstanceType ClusterInstanceType

	// Details of LifeCycle configuration for the instance group.
	LifeCycleConfig *ClusterLifeCycleConfig

	// The number of instances you specified to add to the instance group of a
	// SageMaker HyperPod cluster.
	TargetCount *int32

	// The number you specified to TreadsPerCore in CreateCluster for enabling or
	// disabling multithreading. For instance types that support multithreading, you
	// can specify 1 for disabling multithreading and 2 for enabling multithreading.
	// For more information, see the reference table of CPU cores and threads per CPU
	// core per instance type (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/cpu-options-supported-instances-values.html)
	// in the Amazon Elastic Compute Cloud User Guide.
	ThreadsPerCore *int32
	// contains filtered or unexported fields
}

type ClusterInstanceGroupSpecification struct {

	// Specifies an IAM execution role to be assumed by the instance group.
	//
	// This member is required.
	ExecutionRole *string

	// Specifies the number of instances to add to the instance group of a SageMaker
	// HyperPod cluster.
	//
	// This member is required.
	InstanceCount *int32

	// Specifies the name of the instance group.
	//
	// This member is required.
	InstanceGroupName *string

	// Specifies the instance type of the instance group.
	//
	// This member is required.
	InstanceType ClusterInstanceType

	// Specifies the LifeCycle configuration for the instance group.
	//
	// This member is required.
	LifeCycleConfig *ClusterLifeCycleConfig

	// Specifies the value for Threads per core. For instance types that support
	// multithreading, you can specify 1 for disabling multithreading and 2 for
	// enabling multithreading. For instance types that doesn't support multithreading,
	// specify 1 . For more information, see the reference table of CPU cores and
	// threads per CPU core per instance type (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/cpu-options-supported-instances-values.html)
	// in the Amazon Elastic Compute Cloud User Guide.
	ThreadsPerCore *int32
	// contains filtered or unexported fields
}