// Code generated by smithy-go-codegen DO NOT EDIT. package types import ( smithydocument "github.com/aws/smithy-go/document" "time" ) // A structure describing the source of an action. 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 noSmithyDocumentSerde } // Lists the properties of an action. An action represents an action or activity. // Some examples are a workflow step and a model deployment. Generally, an action // involves at least one input artifact or output artifact. 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 noSmithyDocumentSerde } // A structure of additional Inference Specification. Additional Inference // Specification specifies details about inference jobs that can be run with models // based on this model package 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 noSmithyDocumentSerde } // Edge Manager agent version. 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 noSmithyDocumentSerde } // An Amazon CloudWatch alarm configured to monitor metrics on an endpoint. type Alarm struct { // The name of a CloudWatch alarm in your account. AlarmName *string noSmithyDocumentSerde } // Specifies the training algorithm to use in a CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) // request. For more information about algorithms provided by SageMaker, see // Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html) . For // information about using your own algorithms, see Using Your Own Algorithms with // Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html) // . 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 noSmithyDocumentSerde } // Specifies the validation and image scan statuses of the algorithm. type AlgorithmStatusDetails struct { // The status of the scan of the algorithm's Docker image container. ImageScanStatuses []AlgorithmStatusItem // The status of algorithm validation. ValidationStatuses []AlgorithmStatusItem noSmithyDocumentSerde } // Represents the overall status of an algorithm. 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 noSmithyDocumentSerde } // Provides summary information about an algorithm. 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 noSmithyDocumentSerde } // Defines a training job and a batch transform job that SageMaker runs to // validate your algorithm. The data provided in the validation profile is made // available to your buyers on Amazon Web Services Marketplace. 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 noSmithyDocumentSerde } // Specifies configurations for one or more training jobs that SageMaker runs to // test the algorithm. 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 noSmithyDocumentSerde } // Configures how labels are consolidated across human workers and processes // output data. 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 noSmithyDocumentSerde } // Details about an Amazon SageMaker app. type AppDetails struct { // The name of the app. AppName *string // The type of app. AppType AppType // The creation time. CreationTime *time.Time // The domain ID. DomainId *string // The name of the space. SpaceName *string // The status. Status AppStatus // The user profile name. UserProfileName *string noSmithyDocumentSerde } // The configuration for running a SageMaker image as a KernelGateway app. type AppImageConfigDetails struct { // The Amazon Resource Name (ARN) of the AppImageConfig. AppImageConfigArn *string // The name of the AppImageConfig. Must be unique to your account. AppImageConfigName *string // When the AppImageConfig was created. CreationTime *time.Time // The configuration for the file system and kernels in the SageMaker image. KernelGatewayImageConfig *KernelGatewayImageConfig // When the AppImageConfig was last modified. LastModifiedTime *time.Time noSmithyDocumentSerde } // Configuration to run a processing job in a specified container image. 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 noSmithyDocumentSerde } // A structure describing the source of an artifact. type ArtifactSource struct { // The URI of the source. // // This member is required. SourceUri *string // A list of source types. SourceTypes []ArtifactSourceType noSmithyDocumentSerde } // The ID and ID type of an artifact source. type ArtifactSourceType struct { // The type of ID. // // This member is required. SourceIdType ArtifactSourceIdType // The ID. // // This member is required. Value *string noSmithyDocumentSerde } // Lists a summary of the properties of an artifact. An artifact represents a URI // addressable object or data. Some examples are a dataset and a model. 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 noSmithyDocumentSerde } // Lists a summary of the properties of an association. An association is an // entity that links other lineage or experiment entities. An example would be an // association between a training job and a model. 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 noSmithyDocumentSerde } // Configures the behavior of the client used by SageMaker to interact with the // model container during asynchronous inference. 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 noSmithyDocumentSerde } // Specifies configuration for how an endpoint performs asynchronous inference. 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 noSmithyDocumentSerde } // Specifies the configuration for notifications of inference results for // asynchronous inference. 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 noSmithyDocumentSerde } // Specifies the configuration for asynchronous inference invocation outputs. 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 noSmithyDocumentSerde } // Configuration for Athena Dataset Definition input. 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 noSmithyDocumentSerde } // The collection of algorithms run on a dataset for training the model candidates // of an Autopilot job. 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 noSmithyDocumentSerde } // Information about a candidate produced by an AutoML training job, including its // status, steps, and other properties. 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 noSmithyDocumentSerde } // Stores the configuration information for how a candidate is generated // (optional). 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 noSmithyDocumentSerde } // Information about the steps for a candidate and what step it is working on. 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 noSmithyDocumentSerde } // A channel is a named input source that training algorithms can consume. The // validation dataset size is limited to less than 2 GB. The training dataset size // must be less than 100 GB. For more information, see Channel (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Channel.html) // . A validation dataset must contain the same headers as the training dataset. type AutoMLChannel struct { // The data source for an AutoML channel. // // This member is required. DataSource *AutoMLDataSource // 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 // 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 noSmithyDocumentSerde } // A list of container definitions that describe the different containers that // make up an AutoML candidate. For more information, see ContainerDefinition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html) // . 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 noSmithyDocumentSerde } // The data source for the Autopilot job. type AutoMLDataSource struct { // The Amazon S3 location of the input data. // // This member is required. S3DataSource *AutoMLS3DataSource noSmithyDocumentSerde } // This structure specifies how to split the data into train and validation // datasets. The validation and training datasets must contain the same headers. // For jobs created by calling CreateAutoMLJob , the validation dataset must be // less than 2 GB in size. 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 noSmithyDocumentSerde } // The artifacts that are generated during an AutoML job. type AutoMLJobArtifacts struct { // The URL of the notebook location. CandidateDefinitionNotebookLocation *string // The URL of the notebook location. DataExplorationNotebookLocation *string noSmithyDocumentSerde } // A channel is a named input source that training algorithms can consume. This // channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2 (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html) // ). 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 . ContentType *string // The data source for an AutoML channel (Required). DataSource *AutoMLDataSource noSmithyDocumentSerde } // How long a job is allowed to run, or how many candidates a job is allowed to // generate. 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, as well as time-series forecasting 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. MaxRuntimePerTrainingJobInSeconds *int32 noSmithyDocumentSerde } // A collection of settings used for an AutoML job. 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 noSmithyDocumentSerde } // Specifies a metric to minimize or maximize as the objective of a job. 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. For the // list of all available metrics supported by Autopilot, see Autopilot metrics (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html#autopilot-metrics) // . If you do not specify a metric explicitly, the default behavior is to // automatically use: // - For tabular problem types: // - Regression: MSE . // - Binary classification: F1 . // - Multiclass classification: Accuracy . // - For image or text classification problem types: Accuracy // - For time-series forecasting problem types: AverageWeightedQuantileLoss // // This member is required. MetricName AutoMLMetricEnum noSmithyDocumentSerde } // Metadata for an AutoML job step. type AutoMLJobStepMetadata struct { // The Amazon Resource Name (ARN) of the AutoML job. Arn *string noSmithyDocumentSerde } // Provides a summary about an AutoML job. 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 noSmithyDocumentSerde } // The output data configuration. type AutoMLOutputDataConfig struct { // The Amazon S3 output path. Must be 128 characters or less. // // This member is required. S3OutputPath *string // The Key Management Service (KMS) encryption key ID. KmsKeyId *string noSmithyDocumentSerde } // The reason for a partial failure of an AutoML job. type AutoMLPartialFailureReason struct { // The message containing the reason for a partial failure of an AutoML job. PartialFailureMessage *string noSmithyDocumentSerde } // A collection of settings specific to the problem type used to configure an // AutoML job V2. There must be one and only one config of the following type. // // The following types satisfy this interface: // // AutoMLProblemTypeConfigMemberImageClassificationJobConfig // AutoMLProblemTypeConfigMemberTabularJobConfig // AutoMLProblemTypeConfigMemberTextClassificationJobConfig // AutoMLProblemTypeConfigMemberTimeSeriesForecastingJobConfig type AutoMLProblemTypeConfig interface { isAutoMLProblemTypeConfig() } // Settings used to configure an AutoML job V2 for the image classification // problem type. type AutoMLProblemTypeConfigMemberImageClassificationJobConfig struct { Value ImageClassificationJobConfig noSmithyDocumentSerde } func (*AutoMLProblemTypeConfigMemberImageClassificationJobConfig) isAutoMLProblemTypeConfig() {} // Settings used to configure an AutoML job V2 for a tabular problem type // (regression, classification). type AutoMLProblemTypeConfigMemberTabularJobConfig struct { Value TabularJobConfig noSmithyDocumentSerde } func (*AutoMLProblemTypeConfigMemberTabularJobConfig) isAutoMLProblemTypeConfig() {} // Settings used to configure an AutoML job V2 for the text classification problem // type. type AutoMLProblemTypeConfigMemberTextClassificationJobConfig struct { Value TextClassificationJobConfig noSmithyDocumentSerde } func (*AutoMLProblemTypeConfigMemberTextClassificationJobConfig) isAutoMLProblemTypeConfig() {} // Settings used to configure an AutoML job V2 for a time-series forecasting // problem type. The TimeSeriesForecastingJobConfig problem type is only available // in private beta. Contact Amazon Web Services Support or your account manager to // learn more about access privileges. type AutoMLProblemTypeConfigMemberTimeSeriesForecastingJobConfig struct { Value TimeSeriesForecastingJobConfig noSmithyDocumentSerde } func (*AutoMLProblemTypeConfigMemberTimeSeriesForecastingJobConfig) isAutoMLProblemTypeConfig() {} // The resolved attributes specific to the problem type of an AutoML job V2. // // The following types satisfy this interface: // // AutoMLProblemTypeResolvedAttributesMemberTabularResolvedAttributes type AutoMLProblemTypeResolvedAttributes interface { isAutoMLProblemTypeResolvedAttributes() } // Defines the resolved attributes for the TABULAR problem type. type AutoMLProblemTypeResolvedAttributesMemberTabularResolvedAttributes struct { Value TabularResolvedAttributes noSmithyDocumentSerde } func (*AutoMLProblemTypeResolvedAttributesMemberTabularResolvedAttributes) isAutoMLProblemTypeResolvedAttributes() { } // The resolved attributes used to configure an AutoML job V2. type AutoMLResolvedAttributes struct { // Specifies a metric to minimize or maximize as the objective of a 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 noSmithyDocumentSerde } // Describes the Amazon S3 data source. 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 noSmithyDocumentSerde } // Security options. 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 noSmithyDocumentSerde } // The name and an example value of the hyperparameter that you want to use in // Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is // eligible for Autotune, an optimal hyperparameter range is selected for you. 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 noSmithyDocumentSerde } // Automatic rollback configuration for handling endpoint deployment failures and // recovery. 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 noSmithyDocumentSerde } // A flag to indicate if you want to use Autotune to automatically find optimal // values for the following fields: // - ParameterRanges (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html#sagemaker-Type-HyperParameterTuningJobConfig-ParameterRanges) // : The names and ranges of parameters that a hyperparameter tuning job can // optimize. // - ResourceLimits (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceLimits.html) // : The maximum resources that can be used for a training job. These resources // include the maximum number of training jobs, the maximum runtime of a tuning // job, and the maximum number of training jobs to run at the same time. // - TrainingJobEarlyStoppingType (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html#sagemaker-Type-HyperParameterTuningJobConfig-TrainingJobEarlyStoppingType) // : A flag that specifies whether or not to use early stopping for training jobs // launched by a hyperparameter tuning job. // - RetryStrategy (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html#sagemaker-Type-HyperParameterTrainingJobDefinition-RetryStrategy) // : The number of times to retry a training job. // - Strategy (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html) // : Specifies how hyperparameter tuning chooses the combinations of hyperparameter // values to use for the training jobs that it launches. // - ConvergenceDetected (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ConvergenceDetected.html) // : A flag to indicate that Automatic model tuning (AMT) has detected model // convergence. type Autotune struct { // Set Mode to Enabled if you want to use Autotune. // // This member is required. Mode AutotuneMode noSmithyDocumentSerde } // Configuration to control how SageMaker captures inference data for batch // transform jobs. 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 noSmithyDocumentSerde } // The error code and error description associated with the resource. type BatchDescribeModelPackageError struct { // // // This member is required. ErrorCode *string // // // This member is required. ErrorResponse *string noSmithyDocumentSerde } // Provides summary information about the model package. 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 noSmithyDocumentSerde } // Input object for the batch transform job. 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 substract 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 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 noSmithyDocumentSerde } // A structure that keeps track of which training jobs launched by your // hyperparameter tuning job are not improving model performance as evaluated // against an objective function. 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 noSmithyDocumentSerde } // Contains bias metrics for a model. 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 noSmithyDocumentSerde } // Update policy for a blue/green deployment. If this update policy is specified, // SageMaker creates a new fleet during the deployment while maintaining the old // fleet. SageMaker flips traffic to the new fleet according to the specified // traffic routing configuration. Only one update policy should be used in the // deployment configuration. If no update policy is specified, SageMaker uses a // blue/green deployment strategy with all at once traffic shifting by default. 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 noSmithyDocumentSerde } // Details on the cache hit of a pipeline execution step. type CacheHitResult struct { // The Amazon Resource Name (ARN) of the pipeline execution. SourcePipelineExecutionArn *string noSmithyDocumentSerde } // Metadata about a callback step. 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 noSmithyDocumentSerde } // The location of artifacts for an AutoML candidate job. 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 noSmithyDocumentSerde } // Stores the configuration information for how model candidates are generated // using an AutoML job V2. 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 noSmithyDocumentSerde } // The properties of an AutoML candidate job. 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 noSmithyDocumentSerde } // The SageMaker Canvas application settings. type CanvasAppSettings struct { // The model registry settings for the SageMaker Canvas application. ModelRegisterSettings *ModelRegisterSettings // Time series forecast settings for the Canvas application. TimeSeriesForecastingSettings *TimeSeriesForecastingSettings // The workspace settings for the SageMaker Canvas application. WorkspaceSettings *WorkspaceSettings noSmithyDocumentSerde } // Specifies the type and size of the endpoint capacity to activate for a // blue/green deployment, a rolling deployment, or a rollback strategy. You can // specify your batches as either instance count or the overall percentage or your // fleet. For a rollback strategy, if you don't specify the fields in this object, // or if you set the Value to 100%, then SageMaker uses a blue/green rollback // strategy and rolls all traffic back to the blue fleet. 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 noSmithyDocumentSerde } // Configuration specifying how to treat different headers. If no headers are // specified SageMaker will by default base64 encode when capturing the data. type CaptureContentTypeHeader struct { // The list of all content type headers that 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 noSmithyDocumentSerde } // Specifies data Model Monitor will capture. type CaptureOption struct { // Specify the boundary of data to capture. // // This member is required. CaptureMode CaptureMode noSmithyDocumentSerde } // Environment parameters you want to benchmark your load test against. 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 noSmithyDocumentSerde } // A list of categorical hyperparameters to tune. 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 noSmithyDocumentSerde } // Defines the possible values for a categorical hyperparameter. type CategoricalParameterRangeSpecification struct { // The allowed categories for the hyperparameter. // // This member is required. Values []string noSmithyDocumentSerde } // A channel is a named input source that training algorithms can consume. 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 noSmithyDocumentSerde } // Defines a named input source, called a channel, to be used by an algorithm. 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 noSmithyDocumentSerde } // Contains information about the output location for managed spot training // checkpoint data. 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 noSmithyDocumentSerde } // The container for the metadata for the ClarifyCheck step. For more information, // see the topic on ClarifyCheck step (https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#step-type-clarify-check) // in the Amazon SageMaker Developer Guide. 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 noSmithyDocumentSerde } // The configuration parameters for the SageMaker Clarify explainer. 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 noSmithyDocumentSerde } // The inference configuration parameter for the model container. 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 noSmithyDocumentSerde } // The configuration for the SHAP baseline (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-feature-attribute-shap-baselines.html) // (also called the background or reference dataset) of the Kernal SHAP algorithm. // - The number of records in the baseline data 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) // . // - ShapBaseline and ShapBaselineUri are mutually exclusive parameters. One or // the either is required to configure a SHAP baseline. 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 noSmithyDocumentSerde } // The configuration for SHAP analysis using SageMaker Clarify Explainer. 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 noSmithyDocumentSerde } // A parameter used to configure the SageMaker Clarify explainer to treat text // features as text so that explanations are provided for individual units of text. // Required only for natural language processing (NLP) explainability. 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 noSmithyDocumentSerde } // A Git repository that SageMaker automatically displays to users for cloning in // the JupyterServer application. type CodeRepository struct { // The URL of the Git repository. // // This member is required. RepositoryUrl *string noSmithyDocumentSerde } // Specifies summary information about a Git repository. type CodeRepositorySummary struct { // The Amazon Resource Name (ARN) of the Git repository. // // This member is required. CodeRepositoryArn *string // The name of the Git repository. // // This member is required. CodeRepositoryName *string // The date and time that the Git repository was created. // // This member is required. CreationTime *time.Time // The date and time that the Git repository was last modified. // // This member is required. LastModifiedTime *time.Time // Configuration details for the Git repository, including the URL where it is // located and the ARN of the Amazon Web Services Secrets Manager secret that // contains the credentials used to access the repository. GitConfig *GitConfig noSmithyDocumentSerde } // Use this parameter to configure your Amazon Cognito workforce. A single Cognito // workforce is created using and corresponds to a single Amazon Cognito user pool (https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html) // . type CognitoConfig struct { // The client ID for your Amazon Cognito user pool. // // This member is required. ClientId *string // A user pool (https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html) // is a user directory in Amazon Cognito. With a user pool, your users can sign in // to your web or mobile app through Amazon Cognito. Your users can also sign in // through social identity providers like Google, Facebook, Amazon, or Apple, and // through SAML identity providers. // // This member is required. UserPool *string noSmithyDocumentSerde } // Identifies a Amazon Cognito user group. A user group can be used in on or more // work teams. type CognitoMemberDefinition struct { // An identifier for an application client. You must create the app client ID // using Amazon Cognito. // // This member is required. ClientId *string // An identifier for a user group. // // This member is required. UserGroup *string // An identifier for a user pool. The user pool must be in the same region as the // service that you are calling. // // This member is required. UserPool *string noSmithyDocumentSerde } // Configuration information for the Amazon SageMaker Debugger output tensor // collections. type CollectionConfiguration struct { // The name of the tensor collection. The name must be unique relative to other // rule configuration names. CollectionName *string // Parameter values for the tensor collection. The allowed parameters are "name" , // "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and // "save_histogram" . CollectionParameters map[string]string noSmithyDocumentSerde } // A summary of a model compilation job. type CompilationJobSummary struct { // The Amazon Resource Name (ARN) of the model compilation job. // // This member is required. CompilationJobArn *string // The name of the model compilation job that you want a summary for. // // This member is required. CompilationJobName *string // The status of the model compilation job. // // This member is required. CompilationJobStatus CompilationJobStatus // The time when the model compilation job was created. // // This member is required. CreationTime *time.Time // The time when the model compilation job completed. CompilationEndTime *time.Time // The time when the model compilation job started. CompilationStartTime *time.Time // The type of device that the model will run on after the compilation job has // completed. CompilationTargetDevice TargetDevice // The type of accelerator that the model will run on after the compilation job // has completed. CompilationTargetPlatformAccelerator TargetPlatformAccelerator // The type of architecture that the model will run on after the compilation job // has completed. CompilationTargetPlatformArch TargetPlatformArch // The type of OS that the model will run on after the compilation job has // completed. CompilationTargetPlatformOs TargetPlatformOs // The time when the model compilation job was last modified. LastModifiedTime *time.Time noSmithyDocumentSerde } // Metadata for a Condition step. type ConditionStepMetadata struct { // The outcome of the Condition step evaluation. Outcome ConditionOutcome noSmithyDocumentSerde } // Describes the container, as part of model definition. type ContainerDefinition struct { // This parameter is ignored for models that contain only a PrimaryContainer . When // a ContainerDefinition is part of an inference pipeline, the value of the // parameter uniquely identifies the container for the purposes of logging and // metrics. For information, see Use Logs and Metrics to Monitor an Inference // Pipeline (https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-logs-metrics.html) // . If you don't specify a value for this parameter for a ContainerDefinition // that is part of an inference pipeline, a unique name is automatically assigned // based on the position of the ContainerDefinition in the pipeline. If you // specify a value for the ContainerHostName for any ContainerDefinition that is // part of an inference pipeline, you must specify a value for the // ContainerHostName parameter of every ContainerDefinition in that pipeline. ContainerHostname *string // The environment variables to set in the Docker container. Each key and value in // the Environment string to string map can have length of up to 1024. We support // up to 16 entries in the map. Environment map[string]string // The path where inference code is stored. This can be either in Amazon EC2 // Container Registry or in a Docker registry that is accessible from the same VPC // that you configure for your endpoint. If you are using your own custom algorithm // instead of an algorithm provided by SageMaker, the inference code must meet // SageMaker requirements. SageMaker supports both registry/repository[:tag] and // registry/repository[@digest] image path formats. For more information, see // Using Your Own Algorithms with Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html) // . The model artifacts in an Amazon S3 bucket and the Docker image for inference // container in Amazon EC2 Container Registry must be in the same region as the // model or endpoint you are creating. Image *string // Specifies whether the model container is in Amazon ECR or a private Docker // registry accessible from your Amazon Virtual Private Cloud (VPC). For // information about storing containers in a private Docker registry, see Use a // Private Docker Registry for Real-Time Inference Containers (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-containers-inference-private.html) // . The model artifacts in an Amazon S3 bucket and the Docker image for inference // container in Amazon EC2 Container Registry must be in the same region as the // model or endpoint you are creating. ImageConfig *ImageConfig // The inference specification name in the model package version. InferenceSpecificationName *string // Whether the container hosts a single model or multiple models. Mode ContainerMode // Specifies the location of ML model data to deploy. Currently you cannot use // ModelDataSource in conjunction with SageMaker batch transform, SageMaker // serverless endpoints, SageMaker multi-model endpoints, and SageMaker // Marketplace. ModelDataSource *ModelDataSource // The S3 path where the model artifacts, which result from model training, are // stored. This path must point to a single gzip compressed tar archive (.tar.gz // suffix). The S3 path is required for SageMaker built-in algorithms, but not if // you use your own algorithms. For more information on built-in algorithms, see // Common Parameters (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html) // . The model artifacts must be in an S3 bucket that is in the same region as the // model or endpoint you are creating. If you provide a value for this parameter, // SageMaker uses Amazon Web Services Security Token Service to download model // artifacts from the S3 path you provide. Amazon Web Services STS is activated in // your Amazon Web Services account by default. If you previously deactivated // Amazon Web Services STS for a region, you need to reactivate Amazon Web Services // STS for that region. For more information, see Activating and Deactivating // Amazon Web Services STS in an Amazon Web Services Region (https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html) // in the Amazon Web Services Identity and Access Management User Guide. If you use // a built-in algorithm to create a model, SageMaker requires that you provide a S3 // path to the model artifacts in ModelDataUrl . ModelDataUrl *string // The name or Amazon Resource Name (ARN) of the model package to use to create // the model. ModelPackageName *string // Specifies additional configuration for multi-model endpoints. MultiModelConfig *MultiModelConfig noSmithyDocumentSerde } // A structure describing the source of a context. type ContextSource 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 noSmithyDocumentSerde } // Lists a summary of the properties of a context. A context provides a logical // grouping of other entities. type ContextSummary struct { // The Amazon Resource Name (ARN) of the context. ContextArn *string // The name of the context. ContextName *string // The type of the context. ContextType *string // When the context was created. CreationTime *time.Time // When the context was last modified. LastModifiedTime *time.Time // The source of the context. Source *ContextSource noSmithyDocumentSerde } // A list of continuous hyperparameters to tune. type ContinuousParameterRange struct { // The maximum value for the hyperparameter. The tuning job uses floating-point // values between MinValue value and this value for tuning. // // This member is required. MaxValue *string // The minimum value for the hyperparameter. The tuning job uses floating-point // values between this value and MaxValue for tuning. // // This member is required. MinValue *string // The name of the continuous hyperparameter to tune. // // This member is required. Name *string // The scale that hyperparameter tuning uses to search the hyperparameter range. // For information about choosing a hyperparameter scale, see Hyperparameter // Scaling (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type) // . One of the following values: Auto SageMaker hyperparameter tuning chooses the // best scale for the hyperparameter. Linear Hyperparameter tuning searches the // values in the hyperparameter range by using a linear scale. Logarithmic // Hyperparameter tuning searches the values in the hyperparameter range by using a // logarithmic scale. Logarithmic scaling works only for ranges that have only // values greater than 0. ReverseLogarithmic Hyperparameter tuning searches the // values in the hyperparameter range by using a reverse logarithmic scale. Reverse // logarithmic scaling works only for ranges that are entirely within the range // 0<=x<1.0. ScalingType HyperParameterScalingType noSmithyDocumentSerde } // Defines the possible values for a continuous hyperparameter. type ContinuousParameterRangeSpecification struct { // The maximum floating-point value allowed. // // This member is required. MaxValue *string // The minimum floating-point value allowed. // // This member is required. MinValue *string noSmithyDocumentSerde } // A flag to indicating that automatic model tuning (AMT) has detected model // convergence, defined as a lack of significant improvement (1% or less) against // an objective metric. type ConvergenceDetected struct { // A flag to stop a tuning job once AMT has detected that the job has converged. CompleteOnConvergence CompleteOnConvergence noSmithyDocumentSerde } // A custom SageMaker image. For more information, see Bring your own SageMaker // image (https://docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi.html) . type CustomImage struct { // The name of the AppImageConfig. // // This member is required. AppImageConfigName *string // The name of the CustomImage. Must be unique to your account. // // This member is required. ImageName *string // The version number of the CustomImage. ImageVersionNumber *int32 noSmithyDocumentSerde } // Configuration to control how SageMaker captures inference data. type DataCaptureConfig struct { // Specifies data Model Monitor will capture. You can configure whether to collect // only input, only output, or both // // This member is required. CaptureOptions []CaptureOption // The Amazon S3 location used to capture the data. // // This member is required. DestinationS3Uri *string // The percentage of requests SageMaker will capture. A lower value is recommended // for Endpoints with high traffic. // // This member is required. InitialSamplingPercentage *int32 // Configuration specifying how to treat different headers. If no headers are // specified SageMaker will by default base64 encode when capturing the data. CaptureContentTypeHeader *CaptureContentTypeHeader // Whether data capture should be enabled or disabled (defaults to enabled). EnableCapture bool // The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service // key that SageMaker uses to encrypt the captured data at rest using Amazon S3 // server-side encryption. The KmsKeyId can be any of the following formats: // - Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab // - Key ARN: // arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab // - Alias name: alias/ExampleAlias // - Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias KmsKeyId *string noSmithyDocumentSerde } // The currently active data capture configuration used by your Endpoint. type DataCaptureConfigSummary struct { // Whether data capture is currently functional. // // This member is required. CaptureStatus CaptureStatus // The percentage of requests being captured by your Endpoint. // // This member is required. CurrentSamplingPercentage *int32 // The Amazon S3 location being used to capture the data. // // This member is required. DestinationS3Uri *string // Whether data capture is enabled or disabled. // // This member is required. EnableCapture bool // The KMS key being used to encrypt the data in Amazon S3. // // This member is required. KmsKeyId *string noSmithyDocumentSerde } // The meta data of the Glue table which serves as data catalog for the // OfflineStore . type DataCatalogConfig struct { // The name of the Glue table catalog. // // This member is required. Catalog *string // The name of the Glue table database. // // This member is required. Database *string // The name of the Glue table. // // This member is required. TableName *string noSmithyDocumentSerde } // The data structure used to specify the data to be used for inference in a batch // transform job and to associate the data that is relevant to the prediction // results in the output. The input filter provided allows you to exclude input // data that is not needed for inference in a batch transform job. The output // filter provided allows you to include input data relevant to interpreting the // predictions in the output from the job. For more information, see Associate // Prediction Results with their Corresponding Input Records (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html) // . type DataProcessing struct { // A JSONPath (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#data-processing-operators) // expression used to select a portion of the input data to pass to the algorithm. // Use the InputFilter parameter to exclude fields, such as an ID column, from the // input. If you want SageMaker to pass the entire input dataset to the algorithm, // accept the default value $ . Examples: "$" , "$[1:]" , "$.features" InputFilter *string // Specifies the source of the data to join with the transformed data. The valid // values are None and Input . The default value is None , which specifies not to // join the input with the transformed data. If you want the batch transform job to // join the original input data with the transformed data, set JoinSource to Input // . You can specify OutputFilter as an additional filter to select a portion of // the joined dataset and store it in the output file. For JSON or JSONLines // objects, such as a JSON array, SageMaker adds the transformed data to the input // JSON object in an attribute called SageMakerOutput . The joined result for JSON // must be a key-value pair object. If the input is not a key-value pair object, // SageMaker creates a new JSON file. In the new JSON file, and the input data is // stored under the SageMakerInput key and the results are stored in // SageMakerOutput . For CSV data, SageMaker takes each row as a JSON array and // joins the transformed data with the input by appending each transformed row to // the end of the input. The joined data has the original input data followed by // the transformed data and the output is a CSV file. For information on how // joining in applied, see Workflow for Associating Inferences with Input Records (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#batch-transform-data-processing-workflow) // . JoinSource JoinSource // A JSONPath (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#data-processing-operators) // expression used to select a portion of the joined dataset to save in the output // file for a batch transform job. If you want SageMaker to store the entire input // dataset in the output file, leave the default value, $ . If you specify indexes // that aren't within the dimension size of the joined dataset, you get an error. // Examples: "$" , "$[0,5:]" , "$['id','SageMakerOutput']" OutputFilter *string noSmithyDocumentSerde } // Information about the container that a data quality monitoring job runs. type DataQualityAppSpecification struct { // The container image that the data quality monitoring job runs. // // This member is required. ImageUri *string // The arguments to send to the container that the monitoring job runs. ContainerArguments []string // The entrypoint for a container used to run a monitoring job. ContainerEntrypoint []string // Sets the environment variables in the container that the monitoring job runs. Environment map[string]string // An Amazon S3 URI to a script that is called after analysis has been performed. // Applicable only for the built-in (first party) containers. PostAnalyticsProcessorSourceUri *string // An Amazon S3 URI to a script that is called per row prior to running analysis. // It can base64 decode the payload and convert it into a flatted json so that the // built-in container can use the converted data. Applicable only for the built-in // (first party) containers. RecordPreprocessorSourceUri *string noSmithyDocumentSerde } // Configuration for monitoring constraints and monitoring statistics. These // baseline resources are compared against the results of the current job from the // series of jobs scheduled to collect data periodically. type DataQualityBaselineConfig struct { // The name of the job that performs baselining for the data quality monitoring // job. BaseliningJobName *string // The constraints resource for a monitoring job. ConstraintsResource *MonitoringConstraintsResource // The statistics resource for a monitoring job. StatisticsResource *MonitoringStatisticsResource noSmithyDocumentSerde } // The input for the data quality monitoring job. Currently endpoints are // supported for input. type DataQualityJobInput struct { // Input object for the batch transform job. BatchTransformInput *BatchTransformInput // Input object for the endpoint EndpointInput *EndpointInput noSmithyDocumentSerde } // Configuration for Dataset Definition inputs. The Dataset Definition input must // specify exactly one of either AthenaDatasetDefinition or // RedshiftDatasetDefinition types. type DatasetDefinition struct { // Configuration for Athena Dataset Definition input. AthenaDatasetDefinition *AthenaDatasetDefinition // Whether the generated dataset is FullyReplicated or ShardedByS3Key (default). DataDistributionType DataDistributionType // Whether to use File or Pipe input mode. In File (default) 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. InputMode InputMode // The local path where you want Amazon SageMaker to download the Dataset // Definition inputs to run a processing job. LocalPath is an absolute path to the // input data. This is a required parameter when AppManaged is False (default). LocalPath *string // Configuration for Redshift Dataset Definition input. RedshiftDatasetDefinition *RedshiftDatasetDefinition noSmithyDocumentSerde } // Describes the location of the channel data. type DataSource struct { // The file system that is associated with a channel. FileSystemDataSource *FileSystemDataSource // The S3 location of the data source that is associated with a channel. S3DataSource *S3DataSource noSmithyDocumentSerde } // Configuration information for the Amazon SageMaker Debugger hook parameters, // metric and tensor collections, and storage paths. To learn more about how to // configure the DebugHookConfig parameter, see Use the SageMaker and Debugger // Configuration API Operations to Create, Update, and Debug Your Training Job (https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html) // . type DebugHookConfig struct { // Path to Amazon S3 storage location for metrics and tensors. // // This member is required. S3OutputPath *string // Configuration information for Amazon SageMaker Debugger tensor collections. To // learn more about how to configure the CollectionConfiguration parameter, see // Use the SageMaker and Debugger Configuration API Operations to Create, Update, // and Debug Your Training Job (https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html) // . CollectionConfigurations []CollectionConfiguration // Configuration information for the Amazon SageMaker Debugger hook parameters. HookParameters map[string]string // Path to local storage location for metrics and tensors. Defaults to // /opt/ml/output/tensors/ . LocalPath *string noSmithyDocumentSerde } // Configuration information for SageMaker Debugger rules for debugging. To learn // more about how to configure the DebugRuleConfiguration parameter, see Use the // SageMaker and Debugger Configuration API Operations to Create, Update, and Debug // Your Training Job (https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html) // . type DebugRuleConfiguration struct { // The name of the rule configuration. It must be unique relative to other rule // configuration names. // // This member is required. RuleConfigurationName *string // The Amazon Elastic Container (ECR) Image for the managed rule evaluation. // // This member is required. RuleEvaluatorImage *string // The instance type to deploy a custom rule for debugging a training job. InstanceType ProcessingInstanceType // Path to local storage location for output of rules. Defaults to // /opt/ml/processing/output/rule/ . LocalPath *string // Runtime configuration for rule container. RuleParameters map[string]string // Path to Amazon S3 storage location for rules. S3OutputPath *string // The size, in GB, of the ML storage volume attached to the processing instance. VolumeSizeInGB int32 noSmithyDocumentSerde } // Information about the status of the rule evaluation. type DebugRuleEvaluationStatus struct { // Timestamp when the rule evaluation status was last modified. LastModifiedTime *time.Time // The name of the rule configuration. RuleConfigurationName *string // The Amazon Resource Name (ARN) of the rule evaluation job. RuleEvaluationJobArn *string // Status of the rule evaluation. RuleEvaluationStatus RuleEvaluationStatus // Details from the rule evaluation. StatusDetails *string noSmithyDocumentSerde } // A collection of settings that apply to spaces created in the Domain. type DefaultSpaceSettings struct { // The ARN of the execution role for the space. ExecutionRole *string // The JupyterServer app settings. JupyterServerAppSettings *JupyterServerAppSettings // The KernelGateway app settings. KernelGatewayAppSettings *KernelGatewayAppSettings // The security group IDs for the Amazon Virtual Private Cloud that the space uses // for communication. SecurityGroups []string noSmithyDocumentSerde } // Gets the Amazon EC2 Container Registry path of the docker image of the model // that is hosted in this ProductionVariant (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ProductionVariant.html) // . If you used the registry/repository[:tag] form to specify the image path of // the primary container when you created the model hosted in this // ProductionVariant , the path resolves to a path of the form // registry/repository[@digest] . A digest is a hash value that identifies a // specific version of an image. For information about Amazon ECR paths, see // Pulling an Image (https://docs.aws.amazon.com/AmazonECR/latest/userguide/docker-pull-ecr-image.html) // in the Amazon ECR User Guide. type DeployedImage struct { // The date and time when the image path for the model resolved to the // ResolvedImage ResolutionTime *time.Time // The specific digest path of the image hosted in this ProductionVariant . ResolvedImage *string // The image path you specified when you created the model. SpecifiedImage *string noSmithyDocumentSerde } // The deployment configuration for an endpoint, which contains the desired // deployment strategy and rollback configurations. type DeploymentConfig struct { // Automatic rollback configuration for handling endpoint deployment failures and // recovery. AutoRollbackConfiguration *AutoRollbackConfig // Update policy for a blue/green deployment. If this update policy is specified, // SageMaker creates a new fleet during the deployment while maintaining the old // fleet. SageMaker flips traffic to the new fleet according to the specified // traffic routing configuration. Only one update policy should be used in the // deployment configuration. If no update policy is specified, SageMaker uses a // blue/green deployment strategy with all at once traffic shifting by default. BlueGreenUpdatePolicy *BlueGreenUpdatePolicy // Specifies a rolling deployment strategy for updating a SageMaker endpoint. RollingUpdatePolicy *RollingUpdatePolicy noSmithyDocumentSerde } // A set of recommended deployment configurations for the model. To get more // advanced recommendations, see CreateInferenceRecommendationsJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateInferenceRecommendationsJob.html) // to create an inference recommendation job. type DeploymentRecommendation struct { // Status of the deployment recommendation. The status NOT_APPLICABLE means that // SageMaker is unable to provide a default recommendation for the model using the // information provided. If the deployment status is IN_PROGRESS , retry your API // call after a few seconds to get a COMPLETED deployment recommendation. // // This member is required. RecommendationStatus RecommendationStatus // A list of RealTimeInferenceRecommendation (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_RealTimeInferenceRecommendation.html) // items. RealTimeInferenceRecommendations []RealTimeInferenceRecommendation noSmithyDocumentSerde } // Contains information about a stage in an edge deployment plan. type DeploymentStage struct { // Configuration of the devices in the stage. // // This member is required. DeviceSelectionConfig *DeviceSelectionConfig // The name of the stage. // // This member is required. StageName *string // Configuration of the deployment details. DeploymentConfig *EdgeDeploymentConfig noSmithyDocumentSerde } // Contains information summarizing the deployment stage results. type DeploymentStageStatusSummary struct { // Configuration of the deployment details. // // This member is required. DeploymentConfig *EdgeDeploymentConfig // General status of the current state. // // This member is required. DeploymentStatus *EdgeDeploymentStatus // Configuration of the devices in the stage. // // This member is required. DeviceSelectionConfig *DeviceSelectionConfig // The name of the stage. // // This member is required. StageName *string noSmithyDocumentSerde } // Specifies weight and capacity values for a production variant. type DesiredWeightAndCapacity struct { // The name of the variant to update. // // This member is required. VariantName *string // The variant's capacity. DesiredInstanceCount *int32 // The variant's weight. DesiredWeight *float32 // Specifies the serverless update concurrency configuration for an endpoint // variant. ServerlessUpdateConfig *ProductionVariantServerlessUpdateConfig noSmithyDocumentSerde } // Information of a particular device. type Device struct { // The name of the device. // // This member is required. DeviceName *string // Description of the device. Description *string // Amazon Web Services Internet of Things (IoT) object name. IotThingName *string noSmithyDocumentSerde } // Contains information summarizing device details and deployment status. type DeviceDeploymentSummary struct { // The ARN of the device. // // This member is required. DeviceArn *string // The name of the device. // // This member is required. DeviceName *string // The ARN of the edge deployment plan. // // This member is required. EdgeDeploymentPlanArn *string // The name of the edge deployment plan. // // This member is required. EdgeDeploymentPlanName *string // The name of the stage in the edge deployment plan. // // This member is required. StageName *string // The name of the deployed stage. DeployedStageName *string // The time when the deployment on the device started. DeploymentStartTime *time.Time // The description of the device. Description *string // The deployment status of the device. DeviceDeploymentStatus DeviceDeploymentStatus // The detailed error message for the deployoment status result. DeviceDeploymentStatusMessage *string // The name of the fleet to which the device belongs to. DeviceFleetName *string noSmithyDocumentSerde } // Summary of the device fleet. type DeviceFleetSummary struct { // Amazon Resource Name (ARN) of the device fleet. // // This member is required. DeviceFleetArn *string // Name of the device fleet. // // This member is required. DeviceFleetName *string // Timestamp of when the device fleet was created. CreationTime *time.Time // Timestamp of when the device fleet was last updated. LastModifiedTime *time.Time noSmithyDocumentSerde } // Contains information about the configurations of selected devices. type DeviceSelectionConfig struct { // Type of device subsets to deploy to the current stage. // // This member is required. DeviceSubsetType DeviceSubsetType // A filter to select devices with names containing this name. DeviceNameContains *string // List of devices chosen to deploy. DeviceNames []string // Percentage of devices in the fleet to deploy to the current stage. Percentage int32 noSmithyDocumentSerde } // Status of devices. type DeviceStats struct { // The number of devices connected with a heartbeat. // // This member is required. ConnectedDeviceCount int64 // The number of registered devices. // // This member is required. RegisteredDeviceCount int64 noSmithyDocumentSerde } // Summary of the device. type DeviceSummary struct { // Amazon Resource Name (ARN) of the device. // // This member is required. DeviceArn *string // The unique identifier of the device. // // This member is required. DeviceName *string // Edge Manager agent version. AgentVersion *string // A description of the device. Description *string // The name of the fleet the device belongs to. DeviceFleetName *string // The Amazon Web Services Internet of Things (IoT) object thing name associated // with the device.. IotThingName *string // The last heartbeat received from the device. LatestHeartbeat *time.Time // Models on the device. Models []EdgeModelSummary // The timestamp of the last registration or de-reregistration. RegistrationTime *time.Time noSmithyDocumentSerde } // The domain's details. type DomainDetails struct { // The creation time. CreationTime *time.Time // The domain's Amazon Resource Name (ARN). DomainArn *string // The domain ID. DomainId *string // The domain name. DomainName *string // The last modified time. LastModifiedTime *time.Time // The status. Status DomainStatus // The domain's URL. Url *string noSmithyDocumentSerde } // A collection of settings that apply to the SageMaker Domain . These settings are // specified through the CreateDomain API call. type DomainSettings struct { // The configuration for attaching a SageMaker user profile name to the execution // role as a sts:SourceIdentity key (https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_control-access_monitor.html) // . ExecutionRoleIdentityConfig ExecutionRoleIdentityConfig // A collection of settings that configure the RStudioServerPro Domain-level app. RStudioServerProDomainSettings *RStudioServerProDomainSettings // The security groups for the Amazon Virtual Private Cloud that the Domain uses // for communication between Domain-level apps and user apps. SecurityGroupIds []string noSmithyDocumentSerde } // A collection of Domain configuration settings to update. type DomainSettingsForUpdate struct { // The configuration for attaching a SageMaker user profile name to the execution // role as a sts:SourceIdentity key (https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_control-access_monitor.html) // . This configuration can only be modified if there are no apps in the InService // or Pending state. ExecutionRoleIdentityConfig ExecutionRoleIdentityConfig // A collection of RStudioServerPro Domain-level app settings to update. A single // RStudioServerPro application is created for a domain. RStudioServerProDomainSettingsForUpdate *RStudioServerProDomainSettingsForUpdate // The security groups for the Amazon Virtual Private Cloud that the Domain uses // for communication between Domain-level apps and user apps. SecurityGroupIds []string noSmithyDocumentSerde } // Represents the drift check baselines that can be used when the model monitor is // set using the model package. type DriftCheckBaselines struct { // Represents the drift check bias baselines that can be used when the model // monitor is set using the model package. Bias *DriftCheckBias // Represents the drift check explainability baselines that can be used when the // model monitor is set using the model package. Explainability *DriftCheckExplainability // Represents the drift check model data quality baselines that can be used when // the model monitor is set using the model package. ModelDataQuality *DriftCheckModelDataQuality // Represents the drift check model quality baselines that can be used when the // model monitor is set using the model package. ModelQuality *DriftCheckModelQuality noSmithyDocumentSerde } // Represents the drift check bias baselines that can be used when the model // monitor is set using the model package. type DriftCheckBias struct { // The bias config file for a model. ConfigFile *FileSource // The post-training constraints. PostTrainingConstraints *MetricsSource // The pre-training constraints. PreTrainingConstraints *MetricsSource noSmithyDocumentSerde } // Represents the drift check explainability baselines that can be used when the // model monitor is set using the model package. type DriftCheckExplainability struct { // The explainability config file for the model. ConfigFile *FileSource // The drift check explainability constraints. Constraints *MetricsSource noSmithyDocumentSerde } // Represents the drift check data quality baselines that can be used when the // model monitor is set using the model package. type DriftCheckModelDataQuality struct { // The drift check model data quality constraints. Constraints *MetricsSource // The drift check model data quality statistics. Statistics *MetricsSource noSmithyDocumentSerde } // Represents the drift check model quality baselines that can be used when the // model monitor is set using the model package. type DriftCheckModelQuality struct { // The drift check model quality constraints. Constraints *MetricsSource // The drift check model quality statistics. Statistics *MetricsSource noSmithyDocumentSerde } // A directed edge connecting two lineage entities. type Edge struct { // The type of the Association(Edge) between the source and destination. For // example ContributedTo , Produced , or DerivedFrom . AssociationType AssociationEdgeType // The Amazon Resource Name (ARN) of the destination lineage entity of the // directed edge. DestinationArn *string // The Amazon Resource Name (ARN) of the source lineage entity of the directed // edge. SourceArn *string noSmithyDocumentSerde } // Contains information about the configuration of a deployment. type EdgeDeploymentConfig struct { // Toggle that determines whether to rollback to previous configuration if the // current deployment fails. By default this is turned on. You may turn this off if // you want to investigate the errors yourself. // // This member is required. FailureHandlingPolicy FailureHandlingPolicy noSmithyDocumentSerde } // Contains information about the configuration of a model in a deployment. type EdgeDeploymentModelConfig struct { // The edge packaging job associated with this deployment. // // This member is required. EdgePackagingJobName *string // The name the device application uses to reference this model. // // This member is required. ModelHandle *string noSmithyDocumentSerde } // Contains information summarizing an edge deployment plan. type EdgeDeploymentPlanSummary struct { // The name of the device fleet used for the deployment. // // This member is required. DeviceFleetName *string // The number of edge devices that failed the deployment. // // This member is required. EdgeDeploymentFailed int32 // The number of edge devices yet to pick up the deployment, or in progress. // // This member is required. EdgeDeploymentPending int32 // The ARN of the edge deployment plan. // // This member is required. EdgeDeploymentPlanArn *string // The name of the edge deployment plan. // // This member is required. EdgeDeploymentPlanName *string // The number of edge devices with the successful deployment. // // This member is required. EdgeDeploymentSuccess int32 // The time when the edge deployment plan was created. CreationTime *time.Time // The time when the edge deployment plan was last updated. LastModifiedTime *time.Time noSmithyDocumentSerde } // Contains information summarizing the deployment stage results. type EdgeDeploymentStatus struct { // The number of edge devices that failed the deployment in current stage. // // This member is required. EdgeDeploymentFailedInStage int32 // The number of edge devices yet to pick up the deployment in current stage, or // in progress. // // This member is required. EdgeDeploymentPendingInStage int32 // The number of edge devices with the successful deployment in the current stage. // // This member is required. EdgeDeploymentSuccessInStage int32 // The general status of the current stage. // // This member is required. StageStatus StageStatus // The time when the deployment API started. EdgeDeploymentStageStartTime *time.Time // A detailed message about deployment status in current stage. EdgeDeploymentStatusMessage *string noSmithyDocumentSerde } // The model on the edge device. type EdgeModel struct { // The name of the model. // // This member is required. ModelName *string // The model version. // // This member is required. ModelVersion *string // The timestamp of the last inference that was made. LatestInference *time.Time // The timestamp of the last data sample taken. LatestSampleTime *time.Time noSmithyDocumentSerde } // Status of edge devices with this model. type EdgeModelStat struct { // The number of devices that have this model version, a heart beat, and are // currently running. // // This member is required. ActiveDeviceCount int64 // The number of devices that have this model version and have a heart beat. // // This member is required. ConnectedDeviceCount int64 // The name of the model. // // This member is required. ModelName *string // The model version. // // This member is required. ModelVersion *string // The number of devices that have this model version and do not have a heart beat. // // This member is required. OfflineDeviceCount int64 // The number of devices with this model version and are producing sample data. // // This member is required. SamplingDeviceCount int64 noSmithyDocumentSerde } // Summary of model on edge device. type EdgeModelSummary struct { // The name of the model. // // This member is required. ModelName *string // The version model. // // This member is required. ModelVersion *string noSmithyDocumentSerde } // The output configuration. type EdgeOutputConfig struct { // The Amazon Simple Storage (S3) bucker URI. // // This member is required. S3OutputLocation *string // The Amazon Web Services Key Management Service (Amazon Web Services KMS) key // that Amazon SageMaker uses to encrypt data on the storage volume after // compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the // default KMS key for Amazon S3 for your role's account. KmsKeyId *string // The configuration used to create deployment artifacts. Specify configuration // options with a JSON string. The available configuration options for each type // are: // - ComponentName (optional) - Name of the GreenGrass V2 component. If not // specified, the default name generated consists of "SagemakerEdgeManager" and the // name of your SageMaker Edge Manager packaging job. // - ComponentDescription (optional) - Description of the component. // - ComponentVersion (optional) - The version of the component. Amazon Web // Services IoT Greengrass uses semantic versions for components. Semantic versions // follow a major.minor.patch number system. For example, version 1.0.0 represents // the first major release for a component. For more information, see the // semantic version specification (https://semver.org/) . // - PlatformOS (optional) - The name of the operating system for the platform. // Supported platforms include Windows and Linux. // - PlatformArchitecture (optional) - The processor architecture for the // platform. Supported architectures Windows include: Windows32_x86, Windows64_x64. // Supported architectures for Linux include: Linux x86_64, Linux ARMV8. PresetDeploymentConfig *string // The deployment type SageMaker Edge Manager will create. Currently only supports // Amazon Web Services IoT Greengrass Version 2 components. PresetDeploymentType EdgePresetDeploymentType noSmithyDocumentSerde } // Summary of edge packaging job. type EdgePackagingJobSummary struct { // The Amazon Resource Name (ARN) of the edge packaging job. // // This member is required. EdgePackagingJobArn *string // The name of the edge packaging job. // // This member is required. EdgePackagingJobName *string // The status of the edge packaging job. // // This member is required. EdgePackagingJobStatus EdgePackagingJobStatus // The name of the SageMaker Neo compilation job. CompilationJobName *string // The timestamp of when the job was created. CreationTime *time.Time // The timestamp of when the edge packaging job was last updated. LastModifiedTime *time.Time // The name of the model. ModelName *string // The version of the model. ModelVersion *string noSmithyDocumentSerde } // The output of a SageMaker Edge Manager deployable resource. type EdgePresetDeploymentOutput struct { // The deployment type created by SageMaker Edge Manager. Currently only supports // Amazon Web Services IoT Greengrass Version 2 components. // // This member is required. Type EdgePresetDeploymentType // The Amazon Resource Name (ARN) of the generated deployable resource. Artifact *string // The status of the deployable resource. Status EdgePresetDeploymentStatus // Returns a message describing the status of the deployed resource. StatusMessage *string noSmithyDocumentSerde } // The configurations and outcomes of an Amazon EMR step execution. type EMRStepMetadata struct { // The identifier of the EMR cluster. ClusterId *string // The path to the log file where the cluster step's failure root cause is // recorded. LogFilePath *string // The identifier of the EMR cluster step. StepId *string // The name of the EMR cluster step. StepName *string noSmithyDocumentSerde } // A hosted endpoint for real-time inference. type Endpoint struct { // The time that the endpoint was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the endpoint. // // This member is required. EndpointArn *string // The endpoint configuration associated with the endpoint. // // This member is required. EndpointConfigName *string // The name of the endpoint. // // This member is required. EndpointName *string // The status of the endpoint. // // This member is required. EndpointStatus EndpointStatus // The last time the endpoint was modified. // // This member is required. LastModifiedTime *time.Time // The currently active data capture configuration used by your Endpoint. DataCaptureConfig *DataCaptureConfigSummary // If the endpoint failed, the reason it failed. FailureReason *string // A list of monitoring schedules for the endpoint. For information about model // monitoring, see Amazon SageMaker Model Monitor (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) // . MonitoringSchedules []MonitoringSchedule // A list of the production variants hosted on the endpoint. Each production // variant is a model. ProductionVariants []ProductionVariantSummary // A list of the shadow variants hosted on the endpoint. Each shadow variant is a // model in shadow mode with production traffic replicated from the production // variant. ShadowProductionVariants []ProductionVariantSummary // A list of the tags associated with the endpoint. For more information, see // Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html) // in the Amazon Web Services General Reference Guide. Tags []Tag noSmithyDocumentSerde } // Provides summary information for an endpoint configuration. type EndpointConfigSummary struct { // A timestamp that shows when the endpoint configuration was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the endpoint configuration. // // This member is required. EndpointConfigArn *string // The name of the endpoint configuration. // // This member is required. EndpointConfigName *string noSmithyDocumentSerde } // Details about a customer endpoint that was compared in an Inference Recommender // job. type EndpointInfo struct { // The name of a customer's endpoint. // // This member is required. EndpointName *string noSmithyDocumentSerde } // Input object for the endpoint type EndpointInput struct { // An endpoint in customer's account which has enabled DataCaptureConfig enabled. // // This member is required. EndpointName *string // Path to the filesystem where the endpoint data is available to the container. // // This member is required. LocalPath *string // If specified, monitoring jobs substract 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 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 noSmithyDocumentSerde } // The endpoint configuration for the load test. type EndpointInputConfiguration struct { // The parameter you want to benchmark against. EnvironmentParameterRanges *EnvironmentParameterRanges // The inference specification name in the model package version. InferenceSpecificationName *string // The instance types to use for the load test. InstanceType ProductionVariantInstanceType // Specifies the serverless configuration for an endpoint variant. ServerlessConfig *ProductionVariantServerlessConfig noSmithyDocumentSerde } // The metadata of the endpoint. type EndpointMetadata struct { // The name of the endpoint. // // This member is required. EndpointName *string // The name of the endpoint configuration. EndpointConfigName *string // The status of the endpoint. For possible values of the status of an endpoint, // see EndpointSummary (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_EndpointSummary.html) // . EndpointStatus EndpointStatus // If the status of the endpoint is Failed , or the status is InService but update // operation fails, this provides the reason why it failed. FailureReason *string noSmithyDocumentSerde } // The endpoint configuration made by Inference Recommender during a // recommendation job. type EndpointOutputConfiguration struct { // The name of the endpoint made during a recommendation job. // // This member is required. EndpointName *string // The name of the production variant (deployed model) made during a // recommendation job. // // This member is required. VariantName *string // The number of instances recommended to launch initially. InitialInstanceCount *int32 // The instance type recommended by Amazon SageMaker Inference Recommender. InstanceType ProductionVariantInstanceType // Specifies the serverless configuration for an endpoint variant. ServerlessConfig *ProductionVariantServerlessConfig noSmithyDocumentSerde } // The performance results from running an Inference Recommender job on an // existing endpoint. type EndpointPerformance struct { // Details about a customer endpoint that was compared in an Inference Recommender // job. // // This member is required. EndpointInfo *EndpointInfo // The metrics for an existing endpoint. // // This member is required. Metrics *InferenceMetrics noSmithyDocumentSerde } // Provides summary information for an endpoint. type EndpointSummary struct { // A timestamp that shows when the endpoint was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the endpoint. // // This member is required. EndpointArn *string // The name of the endpoint. // // This member is required. EndpointName *string // The status of the endpoint. // - OutOfService : Endpoint is not available to take incoming requests. // - Creating : CreateEndpoint (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html) // is executing. // - Updating : UpdateEndpoint (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) // or UpdateEndpointWeightsAndCapacities (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpointWeightsAndCapacities.html) // is executing. // - SystemUpdating : Endpoint is undergoing maintenance and cannot be updated or // deleted or re-scaled until it has completed. This maintenance operation does not // change any customer-specified values such as VPC config, KMS encryption, model, // instance type, or instance count. // - RollingBack : Endpoint fails to scale up or down or change its variant // weight and is in the process of rolling back to its previous configuration. Once // the rollback completes, endpoint returns to an InService status. This // transitional status only applies to an endpoint that has autoscaling enabled and // is undergoing variant weight or capacity changes as part of an // UpdateEndpointWeightsAndCapacities (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpointWeightsAndCapacities.html) // call or when the UpdateEndpointWeightsAndCapacities (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpointWeightsAndCapacities.html) // operation is called explicitly. // - InService : Endpoint is available to process incoming requests. // - Deleting : DeleteEndpoint (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DeleteEndpoint.html) // is executing. // - Failed : Endpoint could not be created, updated, or re-scaled. Use // DescribeEndpointOutput$FailureReason for information about the failure. // DeleteEndpoint (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DeleteEndpoint.html) // is the only operation that can be performed on a failed endpoint. // To get a list of endpoints with a specified status, use the StatusEquals filter // with a call to ListEndpoints (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListEndpoints.html) // . // // This member is required. EndpointStatus EndpointStatus // A timestamp that shows when the endpoint was last modified. // // This member is required. LastModifiedTime *time.Time noSmithyDocumentSerde } // A list of environment parameters suggested by the Amazon SageMaker Inference // Recommender. type EnvironmentParameter struct { // The environment key suggested by the Amazon SageMaker Inference Recommender. // // This member is required. Key *string // The value suggested by the Amazon SageMaker Inference Recommender. // // This member is required. Value *string // The value type suggested by the Amazon SageMaker Inference Recommender. // // This member is required. ValueType *string noSmithyDocumentSerde } // Specifies the range of environment parameters type EnvironmentParameterRanges struct { // Specified a list of parameters for each category. CategoricalParameterRanges []CategoricalParameter noSmithyDocumentSerde } // The properties of an experiment as returned by the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) // API. type Experiment struct { // Who created the experiment. CreatedBy *UserContext // When the experiment was created. CreationTime *time.Time // The description of the experiment. Description *string // The name of the experiment as displayed. If DisplayName isn't specified, // ExperimentName is displayed. DisplayName *string // The Amazon Resource Name (ARN) of the experiment. ExperimentArn *string // The name of the experiment. ExperimentName *string // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. LastModifiedBy *UserContext // When the experiment was last modified. LastModifiedTime *time.Time // The source of the experiment. Source *ExperimentSource // The list of tags that are associated with the experiment. You can use Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) // API to search on the tags. Tags []Tag noSmithyDocumentSerde } // Associates a SageMaker job as a trial component with an experiment and trial. // Specified when you call the following APIs: // - CreateProcessingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html) // - CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) // - CreateTransformJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html) type ExperimentConfig struct { // The name of an existing experiment to associate with the trial component. ExperimentName *string // The name of the experiment run to associate with the trial component. RunName *string // The display name for the trial component. If this key isn't specified, the // display name is the trial component name. TrialComponentDisplayName *string // The name of an existing trial to associate the trial component with. If not // specified, a new trial is created. TrialName *string noSmithyDocumentSerde } // The source of the experiment. type ExperimentSource struct { // The Amazon Resource Name (ARN) of the source. // // This member is required. SourceArn *string // The source type. SourceType *string noSmithyDocumentSerde } // A summary of the properties of an experiment. To get the complete set of // properties, call the DescribeExperiment (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeExperiment.html) // API and provide the ExperimentName . type ExperimentSummary struct { // When the experiment was created. CreationTime *time.Time // The name of the experiment as displayed. If DisplayName isn't specified, // ExperimentName is displayed. DisplayName *string // The Amazon Resource Name (ARN) of the experiment. ExperimentArn *string // The name of the experiment. ExperimentName *string // The source of the experiment. ExperimentSource *ExperimentSource // When the experiment was last modified. LastModifiedTime *time.Time noSmithyDocumentSerde } // Contains explainability metrics for a model. type Explainability struct { // The explainability report for a model. Report *MetricsSource noSmithyDocumentSerde } // A parameter to activate explainers. type ExplainerConfig struct { // A member of ExplainerConfig that contains configuration parameters for the // SageMaker Clarify explainer. ClarifyExplainerConfig *ClarifyExplainerConfig noSmithyDocumentSerde } // The container for the metadata for Fail step. type FailStepMetadata struct { // A message that you define and then is processed and rendered by the Fail step // when the error occurs. ErrorMessage *string noSmithyDocumentSerde } // A list of features. You must include FeatureName and FeatureType . Valid feature // FeatureType s are Integral , Fractional and String . type FeatureDefinition struct { // The name of a feature. The type must be a string. FeatureName cannot be any of // the following: is_deleted , write_time , api_invocation_time . FeatureName *string // The value type of a feature. Valid values are Integral, Fractional, or String. FeatureType FeatureType noSmithyDocumentSerde } // Amazon SageMaker Feature Store stores features in a collection called Feature // Group. A Feature Group can be visualized as a table which has rows, with a // unique identifier for each row where each column in the table is a feature. In // principle, a Feature Group is composed of features and values per features. type FeatureGroup struct { // The time a FeatureGroup was created. CreationTime *time.Time // A free form description of a FeatureGroup . Description *string // The name of the feature that stores the EventTime of a Record in a FeatureGroup // . A EventTime is point in time when a new event occurs that corresponds to the // creation or update of a Record in FeatureGroup . All Records in the FeatureGroup // must have a corresponding EventTime . EventTimeFeatureName *string // The reason that the FeatureGroup failed to be replicated in the OfflineStore . // This is failure may be due to a failure to create a FeatureGroup in or delete a // FeatureGroup from the OfflineStore . FailureReason *string // A list of Feature s. Each Feature must include a FeatureName and a FeatureType . // Valid FeatureType s are Integral , Fractional and String . FeatureName s cannot // be any of the following: is_deleted , write_time , api_invocation_time . You can // create up to 2,500 FeatureDefinition s per FeatureGroup . FeatureDefinitions []FeatureDefinition // The Amazon Resource Name (ARN) of a FeatureGroup . FeatureGroupArn *string // The name of the FeatureGroup . FeatureGroupName *string // A FeatureGroup status. FeatureGroupStatus FeatureGroupStatus // A timestamp indicating the last time you updated the feature group. LastModifiedTime *time.Time // A value that indicates whether the feature group was updated successfully. LastUpdateStatus *LastUpdateStatus // The configuration of an OfflineStore . Provide an OfflineStoreConfig in a // request to CreateFeatureGroup to create an OfflineStore . To encrypt an // OfflineStore using at rest data encryption, specify Amazon Web Services Key // Management Service (KMS) key ID, or KMSKeyId , in S3StorageConfig . OfflineStoreConfig *OfflineStoreConfig // The status of OfflineStore . OfflineStoreStatus *OfflineStoreStatus // Use this to specify the Amazon Web Services Key Management Service (KMS) Key // ID, or KMSKeyId , for at rest data encryption. You can turn OnlineStore on or // off by specifying the EnableOnlineStore flag at General Assembly. The default // value is False . OnlineStoreConfig *OnlineStoreConfig // The name of the Feature whose value uniquely identifies a Record defined in the // FeatureGroup FeatureDefinitions . RecordIdentifierFeatureName *string // The Amazon Resource Name (ARN) of the IAM execution role used to create the // feature group. RoleArn *string // Tags used to define a FeatureGroup . Tags []Tag noSmithyDocumentSerde } // The name, ARN, CreationTime , FeatureGroup values, LastUpdatedTime and // EnableOnlineStorage status of a FeatureGroup . type FeatureGroupSummary struct { // A timestamp indicating the time of creation time of the FeatureGroup . // // This member is required. CreationTime *time.Time // Unique identifier for the FeatureGroup . // // This member is required. FeatureGroupArn *string // The name of FeatureGroup . // // This member is required. FeatureGroupName *string // The status of a FeatureGroup. The status can be any of the following: Creating , // Created , CreateFail , Deleting or DetailFail . FeatureGroupStatus FeatureGroupStatus // Notifies you if replicating data into the OfflineStore has failed. Returns // either: Active or Blocked . OfflineStoreStatus *OfflineStoreStatus noSmithyDocumentSerde } // The metadata for a feature. It can either be metadata that you specify, or // metadata that is updated automatically. type FeatureMetadata struct { // A timestamp indicating when the feature was created. CreationTime *time.Time // An optional description that you specify to better describe the feature. Description *string // The Amazon Resource Number (ARN) of the feature group. FeatureGroupArn *string // The name of the feature group containing the feature. FeatureGroupName *string // The name of feature. FeatureName *string // The data type of the feature. FeatureType FeatureType // A timestamp indicating when the feature was last modified. LastModifiedTime *time.Time // Optional key-value pairs that you specify to better describe the feature. Parameters []FeatureParameter noSmithyDocumentSerde } // A key-value pair that you specify to describe the feature. type FeatureParameter struct { // A key that must contain a value to describe the feature. Key *string // The value that belongs to a key. Value *string noSmithyDocumentSerde } // Contains details regarding the file source. type FileSource struct { // The Amazon S3 URI for the file source. // // This member is required. S3Uri *string // The digest of the file source. ContentDigest *string // The type of content stored in the file source. ContentType *string noSmithyDocumentSerde } // The Amazon Elastic File System (EFS) storage configuration for a SageMaker // image. type FileSystemConfig struct { // The default POSIX group ID (GID). If not specified, defaults to 100 . DefaultGid *int32 // The default POSIX user ID (UID). If not specified, defaults to 1000 . DefaultUid *int32 // The path within the image to mount the user's EFS home directory. The directory // should be empty. If not specified, defaults to /home/sagemaker-user. MountPath *string noSmithyDocumentSerde } // Specifies a file system data source for a channel. type FileSystemDataSource struct { // The full path to the directory to associate with the channel. // // This member is required. DirectoryPath *string // The access mode of the mount of the directory associated with the channel. A // directory can be mounted either in ro (read-only) or rw (read-write) mode. // // This member is required. FileSystemAccessMode FileSystemAccessMode // The file system id. // // This member is required. FileSystemId *string // The file system type. // // This member is required. FileSystemType FileSystemType noSmithyDocumentSerde } // A conditional statement for a search expression that includes a resource // property, a Boolean operator, and a value. Resources that match the statement // are returned in the results from the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) // API. If you specify a Value , but not an Operator , SageMaker uses the equals // operator. In search, there are several property types: Metrics To define a // metric filter, enter a value using the form "Metrics." , where is a metric // name. For example, the following filter searches for training jobs with an // // "accuracy" metric greater than "0.9" : { // "Name": "Metrics.accuracy", // // "Operator": "GreaterThan", // // "Value": "0.9" // } HyperParameters To define a hyperparameter filter, enter a value with the // // form "HyperParameters." . Decimal hyperparameter values are treated as a decimal // in a comparison if the specified Value is also a decimal value. If the // specified Value is an integer, the decimal hyperparameter values are treated as // integers. For example, the following filter is satisfied by training jobs with a // // "learning_rate" hyperparameter that is less than "0.5" : { // "Name": "HyperParameters.learning_rate", // // "Operator": "LessThan", // // "Value": "0.5" // } Tags To define a tag filter, enter a value with the form Tags. . type Filter struct { // A resource property name. For example, TrainingJobName . For valid property // names, see SearchRecord (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_SearchRecord.html) // . You must specify a valid property for the resource. // // This member is required. Name *string // A Boolean binary operator that is used to evaluate the filter. The operator // field contains one of the following values: Equals The value of Name equals // Value . NotEquals The value of Name doesn't equal Value . Exists The Name // property exists. NotExists The Name property does not exist. GreaterThan The // value of Name is greater than Value . Not supported for text properties. // GreaterThanOrEqualTo The value of Name is greater than or equal to Value . Not // supported for text properties. LessThan The value of Name is less than Value . // Not supported for text properties. LessThanOrEqualTo The value of Name is less // than or equal to Value . Not supported for text properties. In The value of Name // is one of the comma delimited strings in Value . Only supported for text // properties. Contains The value of Name contains the string Value . Only // supported for text properties. A SearchExpression can include the Contains // operator multiple times when the value of Name is one of the following: // - Experiment.DisplayName // - Experiment.ExperimentName // - Experiment.Tags // - Trial.DisplayName // - Trial.TrialName // - Trial.Tags // - TrialComponent.DisplayName // - TrialComponent.TrialComponentName // - TrialComponent.Tags // - TrialComponent.InputArtifacts // - TrialComponent.OutputArtifacts // A SearchExpression can include only one Contains operator for all other values // of Name . In these cases, if you include multiple Contains operators in the // SearchExpression , the result is the following error message: " 'CONTAINS' // operator usage limit of 1 exceeded. " Operator Operator // A value used with Name and Operator to determine which resources satisfy the // filter's condition. For numerical properties, Value must be an integer or // floating-point decimal. For timestamp properties, Value must be an ISO 8601 // date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS . Value *string noSmithyDocumentSerde } // The best candidate result from an AutoML training job. type FinalAutoMLJobObjectiveMetric struct { // The name of the metric with the best result. For a description of the possible // objective metrics, see AutoMLJobObjective$MetricName (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html) // . // // This member is required. MetricName AutoMLMetricEnum // The value of the metric with the best result. // // This member is required. Value float32 // The name of the standard metric. For a description of the standard metrics, see // Autopilot candidate metrics (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html#autopilot-metrics) // . StandardMetricName AutoMLMetricEnum // The type of metric with the best result. Type AutoMLJobObjectiveType noSmithyDocumentSerde } // Shows the latest objective metric emitted by a training job that was launched // by a hyperparameter tuning job. You define the objective metric in the // HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html) // . type FinalHyperParameterTuningJobObjectiveMetric struct { // The name of the objective metric. For SageMaker built-in algorithms, metrics // are defined per algorithm. See the metrics for XGBoost (https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost-tuning.html) // as an example. You can also use a custom algorithm for training and define your // own metrics. For more information, see Define metrics and environment variables (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html) // . // // This member is required. MetricName *string // The value of the objective metric. // // This member is required. Value float32 // Select if you want to minimize or maximize the objective metric during // hyperparameter tuning. Type HyperParameterTuningJobObjectiveType noSmithyDocumentSerde } // Contains information about where human output will be stored. type FlowDefinitionOutputConfig struct { // The Amazon S3 path where the object containing human output will be made // available. To learn more about the format of Amazon A2I output data, see Amazon // A2I Output Data (https://docs.aws.amazon.com/sagemaker/latest/dg/a2i-output-data.html) // . // // This member is required. S3OutputPath *string // The Amazon Key Management Service (KMS) key ID for server-side encryption. KmsKeyId *string noSmithyDocumentSerde } // Contains summary information about the flow definition. type FlowDefinitionSummary struct { // The timestamp when SageMaker created the flow definition. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the flow definition. // // This member is required. FlowDefinitionArn *string // The name of the flow definition. // // This member is required. FlowDefinitionName *string // The status of the flow definition. Valid values: // // This member is required. FlowDefinitionStatus FlowDefinitionStatus // The reason why the flow definition creation failed. A failure reason is // returned only when the flow definition status is Failed . FailureReason *string noSmithyDocumentSerde } // Specifies configuration details for a Git repository in your Amazon Web // Services account. type GitConfig struct { // The URL where the Git repository is located. // // This member is required. RepositoryUrl *string // The default branch for the Git repository. Branch *string // The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager // secret that contains the credentials used to access the git repository. The // secret must have a staging label of AWSCURRENT and must be in the following // format: {"username": UserName, "password": Password} SecretArn *string noSmithyDocumentSerde } // Specifies configuration details for a Git repository when the repository is // updated. type GitConfigForUpdate struct { // The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager // secret that contains the credentials used to access the git repository. The // secret must have a staging label of AWSCURRENT and must be in the following // format: {"username": UserName, "password": Password} SecretArn *string noSmithyDocumentSerde } // Any dependencies related to hub content, such as scripts, model artifacts, // datasets, or notebooks. type HubContentDependency struct { // The hub content dependency copy path. DependencyCopyPath *string // The hub content dependency origin path. DependencyOriginPath *string noSmithyDocumentSerde } // Information about hub content. type HubContentInfo struct { // The date and time that the hub content was created. // // This member is required. CreationTime *time.Time // The version of the hub content document schema. // // This member is required. DocumentSchemaVersion *string // The Amazon Resource Name (ARN) of the hub content. // // This member is required. HubContentArn *string // The name of the hub content. // // This member is required. HubContentName *string // The status of the hub content. // // This member is required. HubContentStatus HubContentStatus // The type of hub content. // // This member is required. HubContentType HubContentType // The version of the hub content. // // This member is required. HubContentVersion *string // A description of the hub content. HubContentDescription *string // The display name of the hub content. HubContentDisplayName *string // The searchable keywords for the hub content. HubContentSearchKeywords []string noSmithyDocumentSerde } // Information about a hub. type HubInfo struct { // The date and time that the hub was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the hub. // // This member is required. HubArn *string // The name of the hub. // // This member is required. HubName *string // The status of the hub. // // This member is required. HubStatus HubStatus // The date and time that the hub was last modified. // // This member is required. LastModifiedTime *time.Time // A description of the hub. HubDescription *string // The display name of the hub. HubDisplayName *string // The searchable keywords for the hub. HubSearchKeywords []string noSmithyDocumentSerde } // The Amazon S3 storage configuration of a hub. type HubS3StorageConfig struct { // The Amazon S3 bucket prefix for hosting hub content. S3OutputPath *string noSmithyDocumentSerde } // Defines under what conditions SageMaker creates a human loop. Used within // CreateFlowDefinition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateFlowDefinition.html) // . See HumanLoopActivationConditionsConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HumanLoopActivationConditionsConfig.html) // for the required format of activation conditions. type HumanLoopActivationConditionsConfig struct { // JSON expressing use-case specific conditions declaratively. If any condition is // matched, atomic tasks are created against the configured work team. The set of // conditions is different for Rekognition and Textract. For more information about // how to structure the JSON, see JSON Schema for Human Loop Activation Conditions // in Amazon Augmented AI (https://docs.aws.amazon.com/sagemaker/latest/dg/a2i-human-fallback-conditions-json-schema.html) // in the Amazon SageMaker Developer Guide. // // This value conforms to the media type: application/json // // This member is required. HumanLoopActivationConditions *string noSmithyDocumentSerde } // Provides information about how and under what conditions SageMaker creates a // human loop. If HumanLoopActivationConfig is not given, then all requests go to // humans. type HumanLoopActivationConfig struct { // Container structure for defining under what conditions SageMaker creates a // human loop. // // This member is required. HumanLoopActivationConditionsConfig *HumanLoopActivationConditionsConfig noSmithyDocumentSerde } // Describes the work to be performed by human workers. type HumanLoopConfig struct { // The Amazon Resource Name (ARN) of the human task user interface. You can use // standard HTML and Crowd HTML Elements to create a custom worker task template. // You use this template to create a human task UI. To learn how to create a custom // HTML template, see Create Custom Worker Task Template (https://docs.aws.amazon.com/sagemaker/latest/dg/a2i-custom-templates.html) // . To learn how to create a human task UI, which is a worker task template that // can be used in a flow definition, see Create and Delete a Worker Task Templates (https://docs.aws.amazon.com/sagemaker/latest/dg/a2i-worker-template-console.html) // . // // This member is required. HumanTaskUiArn *string // The number of distinct workers who will perform the same task on each object. // For example, if TaskCount is set to 3 for an image classification labeling job, // three workers will classify each input image. Increasing TaskCount can improve // label accuracy. // // This member is required. TaskCount *int32 // A description for the human worker task. // // This member is required. TaskDescription *string // A title for the human worker task. // // This member is required. TaskTitle *string // Amazon Resource Name (ARN) of a team of workers. To learn more about the types // of workforces and work teams you can create and use with Amazon A2I, see Create // and Manage Workforces (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-management.html) // . // // This member is required. WorkteamArn *string // Defines the amount of money paid to an Amazon Mechanical Turk worker for each // task performed. Use one of the following prices for bounding box tasks. Prices // are in US dollars and should be based on the complexity of the task; the longer // it takes in your initial testing, the more you should offer. // - 0.036 // - 0.048 // - 0.060 // - 0.072 // - 0.120 // - 0.240 // - 0.360 // - 0.480 // - 0.600 // - 0.720 // - 0.840 // - 0.960 // - 1.080 // - 1.200 // Use one of the following prices for image classification, text classification, // and custom tasks. Prices are in US dollars. // - 0.012 // - 0.024 // - 0.036 // - 0.048 // - 0.060 // - 0.072 // - 0.120 // - 0.240 // - 0.360 // - 0.480 // - 0.600 // - 0.720 // - 0.840 // - 0.960 // - 1.080 // - 1.200 // Use one of the following prices for semantic segmentation tasks. Prices are in // US dollars. // - 0.840 // - 0.960 // - 1.080 // - 1.200 // Use one of the following prices for Textract AnalyzeDocument Important Form Key // Amazon Augmented AI review tasks. Prices are in US dollars. // - 2.400 // - 2.280 // - 2.160 // - 2.040 // - 1.920 // - 1.800 // - 1.680 // - 1.560 // - 1.440 // - 1.320 // - 1.200 // - 1.080 // - 0.960 // - 0.840 // - 0.720 // - 0.600 // - 0.480 // - 0.360 // - 0.240 // - 0.120 // - 0.072 // - 0.060 // - 0.048 // - 0.036 // - 0.024 // - 0.012 // Use one of the following prices for Rekognition DetectModerationLabels Amazon // Augmented AI review tasks. Prices are in US dollars. // - 1.200 // - 1.080 // - 0.960 // - 0.840 // - 0.720 // - 0.600 // - 0.480 // - 0.360 // - 0.240 // - 0.120 // - 0.072 // - 0.060 // - 0.048 // - 0.036 // - 0.024 // - 0.012 // Use one of the following prices for Amazon Augmented AI custom human review // tasks. Prices are in US dollars. // - 1.200 // - 1.080 // - 0.960 // - 0.840 // - 0.720 // - 0.600 // - 0.480 // - 0.360 // - 0.240 // - 0.120 // - 0.072 // - 0.060 // - 0.048 // - 0.036 // - 0.024 // - 0.012 PublicWorkforceTaskPrice *PublicWorkforceTaskPrice // The length of time that a task remains available for review by human workers. TaskAvailabilityLifetimeInSeconds *int32 // Keywords used to describe the task so that workers can discover the task. TaskKeywords []string // The amount of time that a worker has to complete a task. The default value is // 3,600 seconds (1 hour). TaskTimeLimitInSeconds *int32 noSmithyDocumentSerde } // Container for configuring the source of human task requests. type HumanLoopRequestSource struct { // Specifies whether Amazon Rekognition or Amazon Textract are used as the // integration source. The default field settings and JSON parsing rules are // different based on the integration source. Valid values: // // This member is required. AwsManagedHumanLoopRequestSource AwsManagedHumanLoopRequestSource noSmithyDocumentSerde } // Information required for human workers to complete a labeling task. type HumanTaskConfig struct { // Configures how labels are consolidated across human workers. // // This member is required. AnnotationConsolidationConfig *AnnotationConsolidationConfig // The number of human workers that will label an object. // // This member is required. NumberOfHumanWorkersPerDataObject *int32 // The Amazon Resource Name (ARN) of a Lambda function that is run before a data // object is sent to a human worker. Use this function to provide input to a custom // labeling job. For built-in task types (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html) // , use one of the following Amazon SageMaker Ground Truth Lambda function ARNs // for PreHumanTaskLambdaArn . For custom labeling workflows, see Pre-annotation // Lambda (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates-step3.html#sms-custom-templates-step3-prelambda) // . Bounding box - Finds the most similar boxes from different workers based on // the Jaccard index of the boxes. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox // - arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox // - arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox // - arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox // - arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox // - arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox // - arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox // - arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox // - arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox // Image classification - Uses a variant of the Expectation Maximization approach // to estimate the true class of an image based on annotations from individual // workers. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass // - arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass // - arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass // - arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass // - arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass // - arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass // - arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass // - arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass // - arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass // Multi-label image classification - Uses a variant of the Expectation // Maximization approach to estimate the true classes of an image based on // annotations from individual workers. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel // - arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel // - arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel // Semantic segmentation - Treats each pixel in an image as a multi-class // classification and treats pixel annotations from workers as "votes" for the // correct label. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation // - arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation // - arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation // - arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation // - arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation // - arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation // - arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation // - arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation // - arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation // Text classification - Uses a variant of the Expectation Maximization approach // to estimate the true class of text based on annotations from individual workers. // // - arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass // - arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass // - arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass // - arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass // - arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass // - arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass // - arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass // - arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass // - arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass // Multi-label text classification - Uses a variant of the Expectation // Maximization approach to estimate the true classes of text based on annotations // from individual workers. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel // - arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel // - arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel // Named entity recognition - Groups similar selections and calculates aggregate // boundaries, resolving to most-assigned label. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition // - arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition // - arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition // - arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition // - arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition // Video Classification - Use this task type when you need workers to classify // videos using predefined labels that you specify. Workers are shown videos and // are asked to choose one label for each video. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass // - arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass // - arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass // - arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass // - arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass // - arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass // - arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass // - arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass // - arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass // Video Frame Object Detection - Use this task type to have workers identify and // locate objects in a sequence of video frames (images extracted from a video) // using bounding boxes. For example, you can use this task to ask workers to // identify and localize various objects in a series of video frames, such as cars, // bikes, and pedestrians. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection // - arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection // - arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection // - arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection // - arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection // - arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection // - arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection // - arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection // - arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection // Video Frame Object Tracking - Use this task type to have workers track the // movement of objects in a sequence of video frames (images extracted from a // video) using bounding boxes. For example, you can use this task to ask workers // to track the movement of objects, such as cars, bikes, and pedestrians. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking // - arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking // - arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking // - arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking // - arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking // - arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking // - arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking // - arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking // - arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking // 3D Point Cloud Modalities Use the following pre-annotation lambdas for 3D point // cloud labeling modality tasks. See 3D Point Cloud Task types (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-task-types.html) // to learn more. 3D Point Cloud Object Detection - Use this task type when you // want workers to classify objects in a 3D point cloud by drawing 3D cuboids // around objects. For example, you can use this task type to ask workers to // identify different types of objects in a point cloud, such as cars, bikes, and // pedestrians. // - // arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection // 3D Point Cloud Object Tracking - Use this task type when you want workers to // draw 3D cuboids around objects that appear in a sequence of 3D point cloud // frames. For example, you can use this task type to ask workers to track the // movement of vehicles across multiple point cloud frames. // - // arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking // 3D Point Cloud Semantic Segmentation - Use this task type when you want workers // to create a point-level semantic segmentation masks by painting objects in a 3D // point cloud using different colors where each color is assigned to one of the // classes you specify. // - // arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation // Use the following ARNs for Label Verification and Adjustment Jobs Use label // verification and adjustment jobs to review and adjust labels. To learn more, see // Verify and Adjust Labels (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html) // . Bounding box verification - Uses a variant of the Expectation Maximization // approach to estimate the true class of verification judgement for bounding box // labels based on annotations from individual workers. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBox // - arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBox // - arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBox // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBox // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBox // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBox // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBox // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBox // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBox // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBox // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBox // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBox // Bounding box adjustment - Finds the most similar boxes from different workers // based on the Jaccard index of the adjusted annotations. // - arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox // - arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox // - arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox // - arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox // - arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox // - arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox // - arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox // - arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox // Semantic segmentation verification - Uses a variant of the Expectation // Maximization approach to estimate the true class of verification judgment for // semantic segmentation labels based on annotations from individual workers. // - // arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation // Semantic segmentation adjustment - Treats each pixel in an image as a // multi-class classification and treats pixel adjusted annotations from workers as // "votes" for the correct label. // - // arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation // Video Frame Object Detection Adjustment - Use this task type when you want // workers to adjust bounding boxes that workers have added to video frames to // classify and localize objects in a sequence of video frames. // - // arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection // Video Frame Object Tracking Adjustment - Use this task type when you want // workers to adjust bounding boxes that workers have added to video frames to // track object movement across a sequence of video frames. // - // arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking // 3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud // frame. // - // arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection // 3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence // of point cloud frames. // - // arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking // 3D point cloud semantic segmentation adjustment - Adjust semantic segmentation // masks in a 3D point cloud. // - // arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation // - // arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation // // This member is required. PreHumanTaskLambdaArn *string // A description of the task for your human workers. // // This member is required. TaskDescription *string // The amount of time that a worker has to complete a task. If you create a custom // labeling job, the maximum value for this parameter is 8 hours (28,800 seconds). // If you create a labeling job using a built-in task type (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html) // the maximum for this parameter depends on the task type you use: // - For image (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-label-images.html) // and text (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-label-text.html) // labeling jobs, the maximum is 8 hours (28,800 seconds). // - For 3D point cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud.html) // and video frame (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-video.html) // labeling jobs, the maximum is 30 days (2952,000 seconds) for non-AL mode. For // most users, the maximum is also 30 days. // // This member is required. TaskTimeLimitInSeconds *int32 // A title for the task for your human workers. // // This member is required. TaskTitle *string // Information about the user interface that workers use to complete the labeling // task. // // This member is required. UiConfig *UiConfig // The Amazon Resource Name (ARN) of the work team assigned to complete the tasks. // // This member is required. WorkteamArn *string // Defines the maximum number of data objects that can be labeled by human workers // at the same time. Also referred to as batch size. Each object may have more than // one worker at one time. The default value is 1000 objects. To increase the // maximum value to 5000 objects, contact Amazon Web Services Support. MaxConcurrentTaskCount *int32 // The price that you pay for each task performed by an Amazon Mechanical Turk // worker. PublicWorkforceTaskPrice *PublicWorkforceTaskPrice // The length of time that a task remains available for labeling by human workers. // The default and maximum values for this parameter depend on the type of // workforce you use. // - If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours // (43,200 seconds). The default is 6 hours (21,600 seconds). // - If you choose a private or vendor workforce, the default value is 30 days // (2592,000 seconds) for non-AL mode. For most users, the maximum is also 30 days. TaskAvailabilityLifetimeInSeconds *int32 // Keywords used to describe the task so that workers on Amazon Mechanical Turk // can discover the task. TaskKeywords []string noSmithyDocumentSerde } // Container for human task user interface information. type HumanTaskUiSummary struct { // A timestamp when SageMaker created the human task user interface. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the human task user interface. // // This member is required. HumanTaskUiArn *string // The name of the human task user interface. // // This member is required. HumanTaskUiName *string noSmithyDocumentSerde } // The configuration for Hyperband , a multi-fidelity based hyperparameter tuning // strategy. Hyperband uses the final and intermediate results of a training job // to dynamically allocate resources to utilized hyperparameter configurations // while automatically stopping under-performing configurations. This parameter // should be provided only if Hyperband is selected as the StrategyConfig under // the HyperParameterTuningJobConfig API. type HyperbandStrategyConfig struct { // The maximum number of resources (such as epochs) that can be used by a training // job launched by a hyperparameter tuning job. Once a job reaches the MaxResource // value, it is stopped. If a value for MaxResource is not provided, and Hyperband // is selected as the hyperparameter tuning strategy, HyperbandTrainingJ attempts // to infer MaxResource from the following keys (if present) in // StaticsHyperParameters (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html#sagemaker-Type-HyperParameterTrainingJobDefinition-StaticHyperParameters) // : // - epochs // - numepochs // - n-epochs // - n_epochs // - num_epochs // If HyperbandStrategyConfig is unable to infer a value for MaxResource , it // generates a validation error. The maximum value is 20,000 epochs. All metrics // that correspond to an objective metric are used to derive early stopping // decisions (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html) // . For distributive (https://docs.aws.amazon.com/sagemaker/latest/dg/distributed-training.html) // training jobs, ensure that duplicate metrics are not printed in the logs across // the individual nodes in a training job. If multiple nodes are publishing // duplicate or incorrect metrics, training jobs may make an incorrect stopping // decision and stop the job prematurely. MaxResource *int32 // The minimum number of resources (such as epochs) that can be used by a training // job launched by a hyperparameter tuning job. If the value for MinResource has // not been reached, the training job is not stopped by Hyperband . MinResource *int32 noSmithyDocumentSerde } // Specifies which training algorithm to use for training jobs that a // hyperparameter tuning job launches and the metrics to monitor. type HyperParameterAlgorithmSpecification 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 resource algorithm to use for the hyperparameter tuning job. If // you specify a value for this parameter, do not specify a value for TrainingImage // . AlgorithmName *string // An array of MetricDefinition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_MetricDefinition.html) // objects that specify the metrics that the algorithm emits. MetricDefinitions []MetricDefinition // The registry path of the Docker image that contains the training algorithm. For // information about Docker registry paths for built-in algorithms, see Algorithms // Provided by Amazon SageMaker: Common Parameters (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html) // . SageMaker supports both registry/repository[:tag] and // registry/repository[@digest] image path formats. For more information, see // Using Your Own Algorithms with Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html) // . TrainingImage *string noSmithyDocumentSerde } // Defines a hyperparameter to be used by an algorithm. type HyperParameterSpecification struct { // The name of this hyperparameter. The name must be unique. // // This member is required. Name *string // The type of this hyperparameter. The valid types are Integer , Continuous , // Categorical , and FreeText . // // This member is required. Type ParameterType // The default value for this hyperparameter. If a default value is specified, a // hyperparameter cannot be required. DefaultValue *string // A brief description of the hyperparameter. Description *string // Indicates whether this hyperparameter is required. IsRequired bool // Indicates whether this hyperparameter is tunable in a hyperparameter tuning job. IsTunable bool // The allowed range for this hyperparameter. Range *ParameterRange noSmithyDocumentSerde } // Defines the training jobs launched by a hyperparameter tuning job. type HyperParameterTrainingJobDefinition struct { // The HyperParameterAlgorithmSpecification (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterAlgorithmSpecification.html) // object that specifies the resource algorithm to use for the training jobs that // the tuning job launches. // // This member is required. AlgorithmSpecification *HyperParameterAlgorithmSpecification // Specifies the path to the Amazon S3 bucket where you store model artifacts from // the training jobs that the tuning job launches. // // This member is required. OutputDataConfig *OutputDataConfig // The Amazon Resource Name (ARN) of the IAM role associated with the training // jobs that the tuning job launches. // // This member is required. RoleArn *string // Specifies a limit to how long a model hyperparameter training job can run. It // also specifies how long a managed spot training job has to complete. When the // job reaches the time limit, SageMaker ends the training job. Use this API to cap // model training costs. // // This member is required. StoppingCondition *StoppingCondition // Contains information about the output location for managed spot training // checkpoint data. CheckpointConfig *CheckpointConfig // The job definition name. DefinitionName *string // To encrypt all communications between ML compute instances in distributed // training, choose True . Encryption provides greater security for distributed // training, but training might take longer. How long it takes depends on the // amount of communication between compute instances, especially if you use a deep // learning algorithm in distributed training. EnableInterContainerTrafficEncryption bool // A Boolean indicating whether managed spot training is enabled ( True ) or not ( // False ). EnableManagedSpotTraining bool // Isolates the training container. No inbound or outbound network calls can be // made, except for calls between peers within a training cluster for distributed // training. If network isolation is used for training jobs that are configured to // use a VPC, SageMaker downloads and uploads customer data and model artifacts // through the specified VPC, but the training container does not have network // access. EnableNetworkIsolation bool // An environment variable that you can pass into the SageMaker CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) // API. You can use an existing environment variable from the training container (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html#sagemaker-CreateTrainingJob-request-Environment) // or use your own. See Define metrics and variables (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html) // for more information. The maximum number of items specified for Map Entries // refers to the maximum number of environment variables for each // TrainingJobDefinition and also the maximum for the hyperparameter tuning job // itself. That is, the sum of the number of environment variables for all the // training job definitions can't exceed the maximum number specified. Environment map[string]string // Specifies ranges of integer, continuous, and categorical hyperparameters that a // hyperparameter tuning job searches. The hyperparameter tuning job launches // training jobs with hyperparameter values within these ranges to find the // combination of values that result in the training job with the best performance // as measured by the objective metric of the hyperparameter tuning job. The // maximum number of items specified for Array Members refers to the maximum // number of hyperparameters for each range and also the maximum for the // hyperparameter tuning job itself. That is, the sum of the number of // hyperparameters for all the ranges can't exceed the maximum number specified. HyperParameterRanges *ParameterRanges // The configuration for the hyperparameter tuning resources, including the // compute instances and storage volumes, used for training jobs launched by the // tuning job. By default, storage volumes hold model artifacts and incremental // states. Choose File for TrainingInputMode in the AlgorithmSpecification // parameter to additionally store training data in the storage volume (optional). HyperParameterTuningResourceConfig *HyperParameterTuningResourceConfig // An array of Channel (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Channel.html) // objects that specify the input for the training jobs that the tuning job // launches. InputDataConfig []Channel // The resources, including the compute instances and storage volumes, to use for // the training jobs that the tuning job launches. Storage volumes store model // artifacts and incremental states. Training algorithms might also use storage // volumes for scratch space. If you want SageMaker to use the storage volume to // store the training data, choose File as the TrainingInputMode in the algorithm // specification. For distributed training algorithms, specify an instance count // greater than 1. If you want to use hyperparameter optimization with instance // type flexibility, use HyperParameterTuningResourceConfig instead. ResourceConfig *ResourceConfig // The number of times to retry the job when the job fails due to an // InternalServerError . RetryStrategy *RetryStrategy // Specifies the values of hyperparameters that do not change for the tuning job. StaticHyperParameters map[string]string // Defines the objective metric for a hyperparameter tuning job. Hyperparameter // tuning uses the value of this metric to evaluate the training jobs it launches, // and returns the training job that results in either the highest or lowest value // for this metric, depending on the value you specify for the Type parameter. TuningObjective *HyperParameterTuningJobObjective // The VpcConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html) // object that specifies the VPC that you want the training jobs that this // hyperparameter tuning job launches to connect to. Control access to and from // your training container by configuring the VPC. For more information, see // Protect Training Jobs by Using an Amazon Virtual Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html) // . VpcConfig *VpcConfig noSmithyDocumentSerde } // The container for the summary information about a training job. type HyperParameterTrainingJobSummary struct { // The date and time that the training job was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the training job. // // This member is required. TrainingJobArn *string // The name of the training job. // // This member is required. TrainingJobName *string // The status of the training job. // // This member is required. TrainingJobStatus TrainingJobStatus // A list of the hyperparameters for which you specified ranges to search. // // This member is required. TunedHyperParameters map[string]string // The reason that the training job failed. FailureReason *string // The FinalHyperParameterTuningJobObjectiveMetric (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_FinalHyperParameterTuningJobObjectiveMetric.html) // object that specifies the value of the objective metric of the tuning job that // launched this training job. FinalHyperParameterTuningJobObjectiveMetric *FinalHyperParameterTuningJobObjectiveMetric // The status of the objective metric for the training job: // - Succeeded: The final objective metric for the training job was evaluated by // the hyperparameter tuning job and used in the hyperparameter tuning process. // // - Pending: The training job is in progress and evaluation of its final // objective metric is pending. // // - Failed: The final objective metric for the training job was not evaluated, // and was not used in the hyperparameter tuning process. This typically occurs // when the training job failed or did not emit an objective metric. ObjectiveStatus ObjectiveStatus // Specifies the time when the training job ends on training instances. You are // billed for the time interval between the value of TrainingStartTime and this // time. For successful jobs and stopped jobs, this is the time after model // artifacts are uploaded. For failed jobs, this is the time when SageMaker detects // a job failure. TrainingEndTime *time.Time // The training job definition name. TrainingJobDefinitionName *string // The date and time that the training job started. TrainingStartTime *time.Time // The HyperParameter tuning job that launched the training job. TuningJobName *string noSmithyDocumentSerde } // The configuration for hyperparameter tuning resources for use in training jobs // launched by the tuning job. These resources include compute instances and // storage volumes. Specify one or more compute instance configurations and // allocation strategies to select resources (optional). type HyperParameterTuningInstanceConfig struct { // The number of instances of the type specified by InstanceType . Choose an // instance count larger than 1 for distributed training algorithms. See Step 2: // Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK (https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html) // for more information. // // This member is required. InstanceCount int32 // The instance type used for processing of hyperparameter optimization jobs. // Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, // ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: // ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see // instance type descriptions (https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-instance-types.html) // . // // This member is required. InstanceType TrainingInstanceType // The volume size in GB of the data to be processed for hyperparameter // optimization (optional). // // This member is required. VolumeSizeInGB int32 noSmithyDocumentSerde } // A structure that contains runtime information about both current and completed // hyperparameter tuning jobs. type HyperParameterTuningJobCompletionDetails struct { // The time in timestamp format that AMT detected model convergence, as defined by // a lack of significant improvement over time based on criteria developed over a // wide range of diverse benchmarking tests. ConvergenceDetectedTime *time.Time // The number of training jobs launched by a tuning job that are not improving (1% // or less) as measured by model performance evaluated against an objective // function. NumberOfTrainingJobsObjectiveNotImproving int32 noSmithyDocumentSerde } // Configures a hyperparameter tuning job. type HyperParameterTuningJobConfig struct { // The ResourceLimits (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceLimits.html) // object that specifies the maximum number of training and parallel training jobs // that can be used for this hyperparameter tuning job. // // This member is required. ResourceLimits *ResourceLimits // Specifies how hyperparameter tuning chooses the combinations of hyperparameter // values to use for the training job it launches. For information about search // strategies, see How Hyperparameter Tuning Works (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html) // . // // This member is required. Strategy HyperParameterTuningJobStrategyType // The HyperParameterTuningJobObjective (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobObjective.html) // specifies the objective metric used to evaluate the performance of training jobs // launched by this tuning job. HyperParameterTuningJobObjective *HyperParameterTuningJobObjective // The ParameterRanges (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ParameterRanges.html) // object that specifies the ranges of hyperparameters that this tuning job // searches over to find the optimal configuration for the highest model // performance against your chosen objective metric. ParameterRanges *ParameterRanges // A value used to initialize a pseudo-random number generator. Setting a random // seed and using the same seed later for the same tuning job will allow // hyperparameter optimization to find more a consistent hyperparameter // configuration between the two runs. RandomSeed *int32 // The configuration for the Hyperband optimization strategy. This parameter // should be provided only if Hyperband is selected as the strategy for // HyperParameterTuningJobConfig . StrategyConfig *HyperParameterTuningJobStrategyConfig // Specifies whether to use early stopping for training jobs launched by the // hyperparameter tuning job. Because the Hyperband strategy has its own advanced // internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to // use Hyperband . This parameter can take on one of the following values (the // default value is OFF ): OFF Training jobs launched by the hyperparameter tuning // job do not use early stopping. AUTO SageMaker stops training jobs launched by // the hyperparameter tuning job when they are unlikely to perform better than // previously completed training jobs. For more information, see Stop Training // Jobs Early (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html) // . TrainingJobEarlyStoppingType TrainingJobEarlyStoppingType // The tuning job's completion criteria. TuningJobCompletionCriteria *TuningJobCompletionCriteria noSmithyDocumentSerde } // The total resources consumed by your hyperparameter tuning job. type HyperParameterTuningJobConsumedResources struct { // The wall clock runtime in seconds used by your hyperparameter tuning job. RuntimeInSeconds int32 noSmithyDocumentSerde } // Defines the objective metric for a hyperparameter tuning job. Hyperparameter // tuning uses the value of this metric to evaluate the training jobs it launches, // and returns the training job that results in either the highest or lowest value // for this metric, depending on the value you specify for the Type parameter. type HyperParameterTuningJobObjective struct { // The name of the metric to use for the objective metric. // // This member is required. MetricName *string // Whether to minimize or maximize the objective metric. // // This member is required. Type HyperParameterTuningJobObjectiveType noSmithyDocumentSerde } // An entity returned by the SearchRecord (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_SearchRecord.html) // API containing the properties of a hyperparameter tuning job. type HyperParameterTuningJobSearchEntity struct { // The container for the summary information about a training job. BestTrainingJob *HyperParameterTrainingJobSummary // The total amount of resources consumed by a hyperparameter tuning job. ConsumedResources *HyperParameterTuningJobConsumedResources // The time that a hyperparameter tuning job was created. CreationTime *time.Time // The error that was created when a hyperparameter tuning job failed. FailureReason *string // The time that a hyperparameter tuning job ended. HyperParameterTuningEndTime *time.Time // The Amazon Resource Name (ARN) of a hyperparameter tuning job. HyperParameterTuningJobArn *string // Configures a hyperparameter tuning job. HyperParameterTuningJobConfig *HyperParameterTuningJobConfig // The name of a hyperparameter tuning job. HyperParameterTuningJobName *string // The status of a hyperparameter tuning job. HyperParameterTuningJobStatus HyperParameterTuningJobStatus // The time that a hyperparameter tuning job was last modified. LastModifiedTime *time.Time // Specifies the number of training jobs that this hyperparameter tuning job // launched, categorized by the status of their objective metric. The objective // metric status shows whether the final objective metric for the training job has // been evaluated by the tuning job and used in the hyperparameter tuning process. ObjectiveStatusCounters *ObjectiveStatusCounters // The container for the summary information about a training job. OverallBestTrainingJob *HyperParameterTrainingJobSummary // The tags associated with a hyperparameter tuning job. For more information see // Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html) // . Tags []Tag // Defines the training jobs launched by a hyperparameter tuning job. TrainingJobDefinition *HyperParameterTrainingJobDefinition // The job definitions included in a hyperparameter tuning job. TrainingJobDefinitions []HyperParameterTrainingJobDefinition // The numbers of training jobs launched by a hyperparameter tuning job, // categorized by status. TrainingJobStatusCounters *TrainingJobStatusCounters // Information about either a current or completed hyperparameter tuning job. TuningJobCompletionDetails *HyperParameterTuningJobCompletionDetails // Specifies the configuration for a hyperparameter tuning job that uses one or // more previous hyperparameter tuning jobs as a starting point. The results of // previous tuning jobs are used to inform which combinations of hyperparameters to // search over in the new tuning job. All training jobs launched by the new // hyperparameter tuning job are evaluated by using the objective metric, and the // training job that performs the best is compared to the best training jobs from // the parent tuning jobs. From these, the training job that performs the best as // measured by the objective metric is returned as the overall best training job. // All training jobs launched by parent hyperparameter tuning jobs and the new // hyperparameter tuning jobs count against the limit of training jobs for the // tuning job. WarmStartConfig *HyperParameterTuningJobWarmStartConfig noSmithyDocumentSerde } // The configuration for a training job launched by a hyperparameter tuning job. // Choose Bayesian for Bayesian optimization, and Random for random search // optimization. For more advanced use cases, use Hyperband , which evaluates // objective metrics for training jobs after every epoch. For more information // about strategies, see How Hyperparameter Tuning Works (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html) // . type HyperParameterTuningJobStrategyConfig struct { // The configuration for the object that specifies the Hyperband strategy. This // parameter is only supported for the Hyperband selection for Strategy within the // HyperParameterTuningJobConfig API. HyperbandStrategyConfig *HyperbandStrategyConfig noSmithyDocumentSerde } // Provides summary information about a hyperparameter tuning job. type HyperParameterTuningJobSummary struct { // The date and time that the tuning job was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the tuning job. // // This member is required. HyperParameterTuningJobArn *string // The name of the tuning job. // // This member is required. HyperParameterTuningJobName *string // The status of the tuning job. // // This member is required. HyperParameterTuningJobStatus HyperParameterTuningJobStatus // The ObjectiveStatusCounters (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ObjectiveStatusCounters.html) // object that specifies the numbers of training jobs, categorized by objective // metric status, that this tuning job launched. // // This member is required. ObjectiveStatusCounters *ObjectiveStatusCounters // Specifies the search strategy hyperparameter tuning uses to choose which // hyperparameters to evaluate at each iteration. // // This member is required. Strategy HyperParameterTuningJobStrategyType // The TrainingJobStatusCounters (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingJobStatusCounters.html) // object that specifies the numbers of training jobs, categorized by status, that // this tuning job launched. // // This member is required. TrainingJobStatusCounters *TrainingJobStatusCounters // The date and time that the tuning job ended. HyperParameterTuningEndTime *time.Time // The date and time that the tuning job was modified. LastModifiedTime *time.Time // The ResourceLimits (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceLimits.html) // object that specifies the maximum number of training jobs and parallel training // jobs allowed for this tuning job. ResourceLimits *ResourceLimits noSmithyDocumentSerde } // Specifies the configuration for a hyperparameter tuning job that uses one or // more previous hyperparameter tuning jobs as a starting point. The results of // previous tuning jobs are used to inform which combinations of hyperparameters to // search over in the new tuning job. All training jobs launched by the new // hyperparameter tuning job are evaluated by using the objective metric, and the // training job that performs the best is compared to the best training jobs from // the parent tuning jobs. From these, the training job that performs the best as // measured by the objective metric is returned as the overall best training job. // All training jobs launched by parent hyperparameter tuning jobs and the new // hyperparameter tuning jobs count against the limit of training jobs for the // tuning job. type HyperParameterTuningJobWarmStartConfig struct { // An array of hyperparameter tuning jobs that are used as the starting point for // the new hyperparameter tuning job. For more information about warm starting a // hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a // Starting Point (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html) // . Hyperparameter tuning jobs created before October 1, 2018 cannot be used as // parent jobs for warm start tuning jobs. // // This member is required. ParentHyperParameterTuningJobs []ParentHyperParameterTuningJob // Specifies one of the following: IDENTICAL_DATA_AND_ALGORITHM The new // hyperparameter tuning job uses the same input data and training image as the // parent tuning jobs. You can change the hyperparameter ranges to search and the // maximum number of training jobs that the hyperparameter tuning job launches. You // cannot use a new version of the training algorithm, unless the changes in the // new version do not affect the algorithm itself. For example, changes that // improve logging or adding support for a different data format are allowed. You // can also change hyperparameters from tunable to static, and from static to // tunable, but the total number of static plus tunable hyperparameters must remain // the same as it is in all parent jobs. The objective metric for the new tuning // job must be the same as for all parent jobs. TRANSFER_LEARNING The new // hyperparameter tuning job can include input data, hyperparameter ranges, maximum // number of concurrent training jobs, and maximum number of training jobs that are // different than those of its parent hyperparameter tuning jobs. The training // image can also be a different version from the version used in the parent // hyperparameter tuning job. You can also change hyperparameters from tunable to // static, and from static to tunable, but the total number of static plus tunable // hyperparameters must remain the same as it is in all parent jobs. The objective // metric for the new tuning job must be the same as for all parent jobs. // // This member is required. WarmStartType HyperParameterTuningJobWarmStartType noSmithyDocumentSerde } // The configuration of resources, including compute instances and storage volumes // for use in training jobs launched by hyperparameter tuning jobs. // HyperParameterTuningResourceConfig is similar to ResourceConfig , but has the // additional InstanceConfigs and AllocationStrategy fields to allow for flexible // instance management. Specify one or more instance types, count, and the // allocation strategy for instance selection. HyperParameterTuningResourceConfig // supports the capabilities of ResourceConfig with the exception of // KeepAlivePeriodInSeconds . Hyperparameter tuning jobs use warm pools by default, // which reuse clusters between training jobs. type HyperParameterTuningResourceConfig struct { // The strategy that determines the order of preference for resources specified in // InstanceConfigs used in hyperparameter optimization. AllocationStrategy HyperParameterTuningAllocationStrategy // A list containing the configuration(s) for one or more resources for processing // hyperparameter jobs. These resources include compute instances and storage // volumes to use in model training jobs launched by hyperparameter tuning jobs. // The AllocationStrategy controls the order in which multiple configurations // provided in InstanceConfigs are used. If you only want to use a single instance // configuration inside the HyperParameterTuningResourceConfig API, do not provide // a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and // InstanceCount . If you use InstanceConfigs , do not provide values for // InstanceType , VolumeSizeInGB or InstanceCount . InstanceConfigs []HyperParameterTuningInstanceConfig // The number of compute instances of type InstanceType to use. For distributed // training (https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html) // , select a value greater than 1. InstanceCount int32 // The instance type used to run hyperparameter optimization tuning jobs. See // descriptions of instance types (https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-instance-types.html) // for more information. InstanceType TrainingInstanceType // A key used by Amazon Web Services Key Management Service to encrypt data on the // storage volume attached to the compute instances used to run the training job. // You can use either of the following formats to specify a key. KMS Key ID: // "1234abcd-12ab-34cd-56ef-1234567890ab" Amazon Resource Name (ARN) of a KMS key: // "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab" // Some instances use local storage, which use a hardware module to encrypt (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html) // storage volumes. If you choose one of these instance types, you cannot request a // VolumeKmsKeyId . For a list of instance types that use local storage, see // instance store volumes (http://aws.amazon.com/releasenotes/host-instance-storage-volumes-table/) // . For more information about Amazon Web Services Key Management Service, see // KMS encryption (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-security-kms-permissions.html) // for more information. VolumeKmsKeyId *string // The volume size in GB for the storage volume to be used in processing // hyperparameter optimization jobs (optional). These volumes store model // artifacts, incremental states and optionally, scratch space for training // algorithms. Do not provide a value for this parameter if a value for // InstanceConfigs is also specified. Some instance types have a fixed total local // storage size. If you select one of these instances for training, VolumeSizeInGB // cannot be greater than this total size. For a list of instance types with local // instance storage and their sizes, see instance store volumes (http://aws.amazon.com/releasenotes/host-instance-storage-volumes-table/) // . SageMaker supports only the General Purpose SSD (gp2) (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-volume-types.html) // storage volume type. VolumeSizeInGB int32 noSmithyDocumentSerde } // The IAM Identity details associated with the user. These details are associated // with model package groups, model packages and project entities only. type IamIdentity struct { // The Amazon Resource Name (ARN) of the IAM identity. Arn *string // The ID of the principal that assumes the IAM identity. PrincipalId *string // The person or application which assumes the IAM identity. SourceIdentity *string noSmithyDocumentSerde } // A SageMaker image. A SageMaker image represents a set of container images that // are derived from a common base container image. Each of these container images // is represented by a SageMaker ImageVersion . type Image struct { // When the image was created. // // This member is required. CreationTime *time.Time // The ARN of the image. // // This member is required. ImageArn *string // The name of the image. // // This member is required. ImageName *string // The status of the image. // // This member is required. ImageStatus ImageStatus // When the image was last modified. // // This member is required. LastModifiedTime *time.Time // The description of the image. Description *string // The name of the image as displayed. DisplayName *string // When a create, update, or delete operation fails, the reason for the failure. FailureReason *string noSmithyDocumentSerde } // Stores the configuration information for the image classification problem of an // AutoML job V2. type ImageClassificationJobConfig struct { // How long a job is allowed to run, or how many candidates a job is allowed to // generate. CompletionCriteria *AutoMLJobCompletionCriteria noSmithyDocumentSerde } // Specifies whether the model container is in Amazon ECR or a private Docker // registry accessible from your Amazon Virtual Private Cloud (VPC). type ImageConfig struct { // Set this to one of the following values: // - Platform - The model image is hosted in Amazon ECR. // - Vpc - The model image is hosted in a private Docker registry in your VPC. // // This member is required. RepositoryAccessMode RepositoryAccessMode // (Optional) Specifies an authentication configuration for the private docker // registry where your model image is hosted. Specify a value for this property // only if you specified Vpc as the value for the RepositoryAccessMode field, and // the private Docker registry where the model image is hosted requires // authentication. RepositoryAuthConfig *RepositoryAuthConfig noSmithyDocumentSerde } // A version of a SageMaker Image . A version represents an existing container // image. type ImageVersion struct { // When the version was created. // // This member is required. CreationTime *time.Time // The ARN of the image the version is based on. // // This member is required. ImageArn *string // The ARN of the version. // // This member is required. ImageVersionArn *string // The status of the version. // // This member is required. ImageVersionStatus ImageVersionStatus // When the version was last modified. // // This member is required. LastModifiedTime *time.Time // The version number. // // This member is required. Version *int32 // When a create or delete operation fails, the reason for the failure. FailureReason *string noSmithyDocumentSerde } // Specifies details about how containers in a multi-container endpoint are run. type InferenceExecutionConfig struct { // How containers in a multi-container are run. The following values are valid. // - SERIAL - Containers run as a serial pipeline. // - DIRECT - Only the individual container that you specify is run. // // This member is required. Mode InferenceExecutionMode noSmithyDocumentSerde } // The Amazon S3 location and configuration for storing inference request and // response data. type InferenceExperimentDataStorageConfig struct { // The Amazon S3 bucket where the inference request and response data is stored. // // This member is required. Destination *string // Configuration specifying how to treat different headers. If no headers are // specified SageMaker will by default base64 encode when capturing the data. ContentType *CaptureContentTypeHeader // The Amazon Web Services Key Management Service key that Amazon SageMaker uses // to encrypt captured data at rest using Amazon S3 server-side encryption. KmsKey *string noSmithyDocumentSerde } // The start and end times of an inference experiment. The maximum duration that // you can set for an inference experiment is 30 days. type InferenceExperimentSchedule struct { // The timestamp at which the inference experiment ended or will end. EndTime *time.Time // The timestamp at which the inference experiment started or will start. StartTime *time.Time noSmithyDocumentSerde } // Lists a summary of properties of an inference experiment. type InferenceExperimentSummary struct { // The timestamp at which the inference experiment was created. // // This member is required. CreationTime *time.Time // The timestamp when you last modified the inference experiment. // // This member is required. LastModifiedTime *time.Time // The name of the inference experiment. // // This member is required. Name *string // The status of the inference experiment. // // This member is required. Status InferenceExperimentStatus // The type of the inference experiment. // // This member is required. Type InferenceExperimentType // The timestamp at which the inference experiment was completed. CompletionTime *time.Time // The description of the inference experiment. Description *string // The ARN of the IAM role that Amazon SageMaker can assume to access model // artifacts and container images, and manage Amazon SageMaker Inference endpoints // for model deployment. RoleArn *string // The duration for which the inference experiment ran or will run. The maximum // duration that you can set for an inference experiment is 30 days. Schedule *InferenceExperimentSchedule // The error message for the inference experiment status result. StatusReason *string noSmithyDocumentSerde } // The metrics for an existing endpoint compared in an Inference Recommender job. type InferenceMetrics struct { // The expected maximum number of requests per minute for the instance. // // This member is required. MaxInvocations int32 // The expected model latency at maximum invocations per minute for the instance. // // This member is required. ModelLatency int32 noSmithyDocumentSerde } // A list of recommendations made by Amazon SageMaker Inference Recommender. type InferenceRecommendation struct { // Defines the endpoint configuration parameters. // // This member is required. EndpointConfiguration *EndpointOutputConfiguration // The metrics used to decide what recommendation to make. // // This member is required. Metrics *RecommendationMetrics // Defines the model configuration. // // This member is required. ModelConfiguration *ModelConfiguration // A timestamp that shows when the benchmark completed. InvocationEndTime *time.Time // A timestamp that shows when the benchmark started. InvocationStartTime *time.Time // The recommendation ID which uniquely identifies each recommendation. RecommendationId *string noSmithyDocumentSerde } // A structure that contains a list of recommendation jobs. type InferenceRecommendationsJob struct { // A timestamp that shows when the job was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the recommendation job. // // This member is required. JobArn *string // The job description. // // This member is required. JobDescription *string // The name of the job. // // This member is required. JobName *string // The recommendation job type. // // This member is required. JobType RecommendationJobType // A timestamp that shows when the job was last modified. // // This member is required. LastModifiedTime *time.Time // The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to // perform tasks on your behalf. // // This member is required. RoleArn *string // The status of the job. // // This member is required. Status RecommendationJobStatus // A timestamp that shows when the job completed. CompletionTime *time.Time // If the job fails, provides information why the job failed. FailureReason *string // The name of the created model. ModelName *string // The Amazon Resource Name (ARN) of a versioned model package. ModelPackageVersionArn *string // The Amazon Simple Storage Service (Amazon S3) path where the sample payload is // stored. This path must point to a single gzip compressed tar archive (.tar.gz // suffix). SamplePayloadUrl *string noSmithyDocumentSerde } // A returned array object for the Steps response field in the // ListInferenceRecommendationsJobSteps (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListInferenceRecommendationsJobSteps.html) // API command. type InferenceRecommendationsJobStep struct { // The name of the Inference Recommender job. // // This member is required. JobName *string // The current status of the benchmark. // // This member is required. Status RecommendationJobStatus // The type of the subtask. BENCHMARK : Evaluate the performance of your model on // different instance types. // // This member is required. StepType RecommendationStepType // The details for a specific benchmark. InferenceBenchmark *RecommendationJobInferenceBenchmark noSmithyDocumentSerde } // Defines how to perform inference generation after a training job is run. type InferenceSpecification struct { // The Amazon ECR registry path of the Docker image that contains the inference // code. // // This member is required. Containers []ModelPackageContainerDefinition // The supported MIME types for the input data. // // This member is required. SupportedContentTypes []string // The supported MIME types for the output data. // // This member is required. SupportedResponseMIMETypes []string // A list of the instance types that are used to generate inferences in real-time. // This parameter is required for unversioned models, and optional for versioned // models. SupportedRealtimeInferenceInstanceTypes []ProductionVariantInstanceType // A list of the instance types on which a transformation job can be run or on // which an endpoint can be deployed. This parameter is required for unversioned // models, and optional for versioned models. SupportedTransformInstanceTypes []TransformInstanceType noSmithyDocumentSerde } // Contains information about the location of input model artifacts, the name and // shape of the expected data inputs, and the framework in which the model was // trained. type InputConfig struct { // Specifies the name and shape of the expected data inputs for your trained model // with a JSON dictionary form. The data inputs are Framework specific. // - TensorFlow : You must specify the name and shape (NHWC format) of the // expected data inputs using a dictionary format for your trained model. The // dictionary formats required for the console and CLI are different. // - Examples for one input: // - If using the console, {"input":[1,1024,1024,3]} // - If using the CLI, {\"input\":[1,1024,1024,3]} // - Examples for two inputs: // - If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]} // - If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]} // - KERAS : You must specify the name and shape (NCHW format) of expected data // inputs using a dictionary format for your trained model. Note that while Keras // model artifacts should be uploaded in NHWC (channel-last) format, // DataInputConfig should be specified in NCHW (channel-first) format. The // dictionary formats required for the console and CLI are different. // - Examples for one input: // - If using the console, {"input_1":[1,3,224,224]} // - If using the CLI, {\"input_1\":[1,3,224,224]} // - Examples for two inputs: // - If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]} // - If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]} // - MXNET/ONNX/DARKNET : You must specify the name and shape (NCHW format) of // the expected data inputs in order using a dictionary format for your trained // model. The dictionary formats required for the console and CLI are different. // - Examples for one input: // - If using the console, {"data":[1,3,1024,1024]} // - If using the CLI, {\"data\":[1,3,1024,1024]} // - Examples for two inputs: // - If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]} // - If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]} // - PyTorch : You can either specify the name and shape (NCHW format) of // expected data inputs in order using a dictionary format for your trained model // or you can specify the shape only using a list format. The dictionary formats // required for the console and CLI are different. The list formats for the console // and CLI are the same. // - Examples for one input in dictionary format: // - If using the console, {"input0":[1,3,224,224]} // - If using the CLI, {\"input0\":[1,3,224,224]} // - Example for one input in list format: [[1,3,224,224]] // - Examples for two inputs in dictionary format: // - If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]} // - If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]} // - Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]] // - XGBOOST : input data name and shape are not needed. // DataInputConfig supports the following parameters for CoreML TargetDevice (ML // Model format): // - shape : Input shape, for example {"input_1": {"shape": [1,224,224,3]}} . In // addition to static input shapes, CoreML converter supports Flexible input // shapes: // - Range Dimension. You can use the Range Dimension feature if you know the // input shape will be within some specific interval in that dimension, for // example: {"input_1": {"shape": ["1..10", 224, 224, 3]}} // - Enumerated shapes. Sometimes, the models are trained to work only on a // select set of inputs. You can enumerate all supported input shapes, for example: // {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}} // - default_shape : Default input shape. You can set a default shape during // conversion for both Range Dimension and Enumerated Shapes. For example // {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, // 3]}} // - type : Input type. Allowed values: Image and Tensor . By default, the // converter generates an ML Model with inputs of type Tensor (MultiArray). User // can set input type to be Image. Image input type requires additional input // parameters such as bias and scale . // - bias : If the input type is an Image, you need to provide the bias vector. // - scale : If the input type is an Image, you need to provide a scale factor. // CoreML ClassifierConfig parameters can be specified using OutputConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html) // CompilerOptions . CoreML converter supports Tensorflow and PyTorch models. // CoreML conversion examples: // - Tensor type input: // - "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], // "default_shape": [1,224,224,3]}} // - Tensor type input without input name (PyTorch): // - "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], // "default_shape": [1,3,224,224]}] // - Image type input: // - "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], // "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": // 0.007843137255}} // - "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} // - Image type input without input name (PyTorch): // - "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], // "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": // 0.007843137255}] // - "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"} // Depending on the model format, DataInputConfig requires the following // parameters for ml_eia2 OutputConfig:TargetDevice (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html#sagemaker-Type-OutputConfig-TargetDevice) // . // - For TensorFlow models saved in the SavedModel format, specify the input // names from signature_def_key and the input model shapes for DataInputConfig . // Specify the signature_def_key in OutputConfig:CompilerOptions (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html#sagemaker-Type-OutputConfig-CompilerOptions) // if the model does not use TensorFlow's default signature def key. For example: // - "DataInputConfig": {"inputs": [1, 224, 224, 3]} // - "CompilerOptions": {"signature_def_key": "serving_custom"} // - For TensorFlow models saved as a frozen graph, specify the input tensor // names and shapes in DataInputConfig and the output tensor names for // output_names in OutputConfig:CompilerOptions (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html#sagemaker-Type-OutputConfig-CompilerOptions) // . For example: // - "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]} // - "CompilerOptions": {"output_names": ["output_tensor:0"]} // // This member is required. DataInputConfig *string // Identifies the framework in which the model was trained. For example: // TENSORFLOW. // // This member is required. Framework Framework // The S3 path where the model artifacts, which result from model training, are // stored. This path must point to a single gzip compressed tar archive (.tar.gz // suffix). // // This member is required. S3Uri *string // Specifies the framework version to use. This API field is only supported for // the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks. For information // about framework versions supported for cloud targets and edge devices, see // Cloud Supported Instance Types and Frameworks (https://docs.aws.amazon.com/sagemaker/latest/dg/neo-supported-cloud.html) // and Edge Supported Frameworks (https://docs.aws.amazon.com/sagemaker/latest/dg/neo-supported-devices-edge-frameworks.html) // . FrameworkVersion *string noSmithyDocumentSerde } // Defines an instance group for heterogeneous cluster training. When requesting a // training job using the CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) // API, you can configure multiple instance groups . type InstanceGroup struct { // Specifies the number of instances of the instance group. // // 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 TrainingInstanceType noSmithyDocumentSerde } // Information on the IMDS configuration of the notebook instance type InstanceMetadataServiceConfiguration struct { // Indicates the minimum IMDS version that the notebook instance supports. When // passed as part of CreateNotebookInstance , if no value is selected, then it // defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If // passed as part of UpdateNotebookInstance , there is no default. // // This member is required. MinimumInstanceMetadataServiceVersion *string noSmithyDocumentSerde } // For a hyperparameter of the integer type, specifies the range that a // hyperparameter tuning job searches. type IntegerParameterRange struct { // The maximum value of the hyperparameter to search. // // This member is required. MaxValue *string // The minimum value of the hyperparameter to search. // // This member is required. MinValue *string // The name of the hyperparameter to search. // // This member is required. Name *string // The scale that hyperparameter tuning uses to search the hyperparameter range. // For information about choosing a hyperparameter scale, see Hyperparameter // Scaling (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type) // . One of the following values: Auto SageMaker hyperparameter tuning chooses the // best scale for the hyperparameter. Linear Hyperparameter tuning searches the // values in the hyperparameter range by using a linear scale. Logarithmic // Hyperparameter tuning searches the values in the hyperparameter range by using a // logarithmic scale. Logarithmic scaling works only for ranges that have only // values greater than 0. ScalingType HyperParameterScalingType noSmithyDocumentSerde } // Defines the possible values for an integer hyperparameter. type IntegerParameterRangeSpecification struct { // The maximum integer value allowed. // // This member is required. MaxValue *string // The minimum integer value allowed. // // This member is required. MinValue *string noSmithyDocumentSerde } // The JupyterServer app settings. type JupyterServerAppSettings struct { // A list of Git repositories that SageMaker automatically displays to users for // cloning in the JupyterServer application. CodeRepositories []CodeRepository // The default instance type and the Amazon Resource Name (ARN) of the default // SageMaker image used by the JupyterServer app. If you use the // LifecycleConfigArns parameter, then this parameter is also required. DefaultResourceSpec *ResourceSpec // The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the // JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter // is also required. To remove a Lifecycle Config, you must set LifecycleConfigArns // to an empty list. LifecycleConfigArns []string noSmithyDocumentSerde } // The KernelGateway app settings. type KernelGatewayAppSettings struct { // A list of custom SageMaker images that are configured to run as a KernelGateway // app. CustomImages []CustomImage // The default instance type and the Amazon Resource Name (ARN) of the default // SageMaker image used by the KernelGateway app. The Amazon SageMaker Studio UI // does not use the default instance type value set here. The default instance type // set here is used when Apps are created using the Amazon Web Services Command // Line Interface or Amazon Web Services CloudFormation and the instance type // parameter value is not passed. DefaultResourceSpec *ResourceSpec // The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the // the user profile or domain. To remove a Lifecycle Config, you must set // LifecycleConfigArns to an empty list. LifecycleConfigArns []string noSmithyDocumentSerde } // The configuration for the file system and kernels in a SageMaker image running // as a KernelGateway app. type KernelGatewayImageConfig struct { // The specification of the Jupyter kernels in the image. // // This member is required. KernelSpecs []KernelSpec // The Amazon Elastic File System (EFS) storage configuration for a SageMaker // image. FileSystemConfig *FileSystemConfig noSmithyDocumentSerde } // The specification of a Jupyter kernel. type KernelSpec struct { // The name of the Jupyter kernel in the image. This value is case sensitive. // // This member is required. Name *string // The display name of the kernel. DisplayName *string noSmithyDocumentSerde } // Provides a breakdown of the number of objects labeled. type LabelCounters struct { // The total number of objects that could not be labeled due to an error. FailedNonRetryableError int32 // The total number of objects labeled by a human worker. HumanLabeled int32 // The total number of objects labeled by automated data labeling. MachineLabeled int32 // The total number of objects labeled. TotalLabeled int32 // The total number of objects not yet labeled. Unlabeled int32 noSmithyDocumentSerde } // Provides counts for human-labeled tasks in the labeling job. type LabelCountersForWorkteam struct { // The total number of data objects labeled by a human worker. HumanLabeled int32 // The total number of data objects that need to be labeled by a human worker. PendingHuman int32 // The total number of tasks in the labeling job. Total int32 noSmithyDocumentSerde } // Provides configuration information for auto-labeling of your data objects. A // LabelingJobAlgorithmsConfig object must be supplied in order to use // auto-labeling. type LabelingJobAlgorithmsConfig struct { // Specifies the Amazon Resource Name (ARN) of the algorithm used for // auto-labeling. You must select one of the following ARNs: // - Image classification // arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification // - Text classification // arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification // - Object detection // arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection // - Semantic Segmentation // arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation // // This member is required. LabelingJobAlgorithmSpecificationArn *string // At the end of an auto-label job Ground Truth sends the Amazon Resource Name // (ARN) of the final model used for auto-labeling. You can use this model as the // starting point for subsequent similar jobs by providing the ARN of the model // here. InitialActiveLearningModelArn *string // Provides configuration information for a labeling job. LabelingJobResourceConfig *LabelingJobResourceConfig noSmithyDocumentSerde } // Attributes of the data specified by the customer. Use these to describe the // data to be labeled. type LabelingJobDataAttributes struct { // Declares that your content is free of personally identifiable information or // adult content. SageMaker may restrict the Amazon Mechanical Turk workers that // can view your task based on this information. ContentClassifiers []ContentClassifier noSmithyDocumentSerde } // Provides information about the location of input data. You must specify at // least one of the following: S3DataSource or SnsDataSource . Use SnsDataSource // to specify an SNS input topic for a streaming labeling job. If you do not // specify and SNS input topic ARN, Ground Truth will create a one-time labeling // job. Use S3DataSource to specify an input manifest file for both streaming and // one-time labeling jobs. Adding an S3DataSource is optional if you use // SnsDataSource to create a streaming labeling job. type LabelingJobDataSource struct { // The Amazon S3 location of the input data objects. S3DataSource *LabelingJobS3DataSource // An Amazon SNS data source used for streaming labeling jobs. To learn more, see // Send Data to a Streaming Labeling Job (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-labeling-job.html#sms-streaming-how-it-works-send-data) // . SnsDataSource *LabelingJobSnsDataSource noSmithyDocumentSerde } // Provides summary information for a work team. type LabelingJobForWorkteamSummary struct { // The date and time that the labeling job was created. // // This member is required. CreationTime *time.Time // A unique identifier for a labeling job. You can use this to refer to a specific // labeling job. // // This member is required. JobReferenceCode *string // The Amazon Web Services account ID of the account used to start the labeling // job. // // This member is required. WorkRequesterAccountId *string // Provides information about the progress of a labeling job. LabelCounters *LabelCountersForWorkteam // The name of the labeling job that the work team is assigned to. LabelingJobName *string // The configured number of workers per data object. NumberOfHumanWorkersPerDataObject *int32 noSmithyDocumentSerde } // Input configuration information for a labeling job. type LabelingJobInputConfig struct { // The location of the input data. // // This member is required. DataSource *LabelingJobDataSource // Attributes of the data specified by the customer. DataAttributes *LabelingJobDataAttributes noSmithyDocumentSerde } // Specifies the location of the output produced by the labeling job. type LabelingJobOutput struct { // The Amazon S3 bucket location of the manifest file for labeled data. // // This member is required. OutputDatasetS3Uri *string // The Amazon Resource Name (ARN) for the most recent SageMaker model trained as // part of automated data labeling. FinalActiveLearningModelArn *string noSmithyDocumentSerde } // Output configuration information for a labeling job. type LabelingJobOutputConfig struct { // The Amazon S3 location to write output data. // // This member is required. S3OutputPath *string // The Amazon Web Services Key Management Service ID of the key used to encrypt // the output data, if any. If you provide your own KMS key ID, you must add the // required permissions to your KMS key described in Encrypt Output Data and // Storage Volume with Amazon Web Services KMS (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-security-permission.html#sms-security-kms-permissions) // . If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon // Web Services KMS key for Amazon S3 for your role's account to encrypt your // output data. If you use a bucket policy with an s3:PutObject permission that // only allows objects with server-side encryption, set the condition key of // s3:x-amz-server-side-encryption to "aws:kms" . For more information, see // KMS-Managed Encryption Keys (https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html) // in the Amazon Simple Storage Service Developer Guide. KmsKeyId *string // An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a // SnsTopicArn if you want to do real time chaining to another streaming job and // receive an Amazon SNS notifications each time a data object is submitted by a // worker. If you provide an SnsTopicArn in OutputConfig , when workers complete // labeling tasks, Ground Truth will send labeling task output data to the SNS // output topic you specify here. To learn more, see Receive Output Data from a // Streaming Labeling Job (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-labeling-job.html#sms-streaming-how-it-works-output-data) // . SnsTopicArn *string noSmithyDocumentSerde } // Configure encryption on the storage volume attached to the ML compute instance // used to run automated data labeling model training and inference. type LabelingJobResourceConfig struct { // The Amazon Web Services Key Management Service (Amazon Web Services KMS) key // that Amazon SageMaker uses to encrypt data on the storage volume attached to the // ML compute instance(s) that run the training and inference jobs used for // automated data labeling. You can only specify a VolumeKmsKeyId when you create // a labeling job with automated data labeling enabled using the API operation // CreateLabelingJob . You cannot specify an Amazon Web Services KMS key to encrypt // the storage volume used for automated data labeling model training and inference // when you create a labeling job using the console. To learn more, see Output // Data and Storage Volume Encryption (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-security.html) // . The VolumeKmsKeyId can be any of the following formats: // - KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab" // - Amazon Resource Name (ARN) of a KMS Key // "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab" VolumeKmsKeyId *string // Specifies a VPC that your training jobs and hosted models have access to. // Control access to and from your training and model containers by configuring the // VPC. For more information, see Protect Endpoints by Using an Amazon Virtual // Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html) // and Protect Training Jobs by Using an Amazon Virtual Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html) // . VpcConfig *VpcConfig noSmithyDocumentSerde } // The Amazon S3 location of the input data objects. type LabelingJobS3DataSource struct { // The Amazon S3 location of the manifest file that describes the input data // objects. The input manifest file referenced in ManifestS3Uri must contain one // of the following keys: source-ref or source . The value of the keys are // interpreted as follows: // - source-ref : The source of the object is the Amazon S3 object specified in // the value. Use this value when the object is a binary object, such as an image. // - source : The source of the object is the value. Use this value when the // object is a text value. // If you are a new user of Ground Truth, it is recommended you review Use an // Input Manifest File (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-input-data-input-manifest.html) // in the Amazon SageMaker Developer Guide to learn how to create an input manifest // file. // // This member is required. ManifestS3Uri *string noSmithyDocumentSerde } // An Amazon SNS data source used for streaming labeling jobs. type LabelingJobSnsDataSource struct { // The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the // input topic you will use to send new data objects to a streaming labeling job. // // This member is required. SnsTopicArn *string noSmithyDocumentSerde } // A set of conditions for stopping a labeling job. If any of the conditions are // met, the job is automatically stopped. You can use these conditions to control // the cost of data labeling. Labeling jobs fail after 30 days with an appropriate // client error message. type LabelingJobStoppingConditions struct { // The maximum number of objects that can be labeled by human workers. MaxHumanLabeledObjectCount *int32 // The maximum number of input data objects that should be labeled. MaxPercentageOfInputDatasetLabeled *int32 noSmithyDocumentSerde } // Provides summary information about a labeling job. type LabelingJobSummary struct { // The date and time that the job was created (timestamp). // // This member is required. CreationTime *time.Time // Counts showing the progress of the labeling job. // // This member is required. LabelCounters *LabelCounters // The Amazon Resource Name (ARN) assigned to the labeling job when it was created. // // This member is required. LabelingJobArn *string // The name of the labeling job. // // This member is required. LabelingJobName *string // The current status of the labeling job. // // This member is required. LabelingJobStatus LabelingJobStatus // The date and time that the job was last modified (timestamp). // // This member is required. LastModifiedTime *time.Time // The Amazon Resource Name (ARN) of a Lambda function. The function is run before // each data object is sent to a worker. // // This member is required. PreHumanTaskLambdaArn *string // The Amazon Resource Name (ARN) of the work team assigned to the job. // // This member is required. WorkteamArn *string // The Amazon Resource Name (ARN) of the Lambda function used to consolidate the // annotations from individual workers into a label for a data object. For more // information, see Annotation Consolidation (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-annotation-consolidation.html) // . AnnotationConsolidationLambdaArn *string // If the LabelingJobStatus field is Failed , this field contains a description of // the error. FailureReason *string // Input configuration for the labeling job. InputConfig *LabelingJobInputConfig // The location of the output produced by the labeling job. LabelingJobOutput *LabelingJobOutput noSmithyDocumentSerde } // Metadata for a Lambda step. type LambdaStepMetadata struct { // The Amazon Resource Name (ARN) of the Lambda function that was run by this step // execution. Arn *string // A list of the output parameters of the Lambda step. OutputParameters []OutputParameter noSmithyDocumentSerde } // A value that indicates whether the update was successful. type LastUpdateStatus struct { // A value that indicates whether the update was made successful. // // This member is required. Status LastUpdateStatusValue // If the update wasn't successful, indicates the reason why it failed. FailureReason *string noSmithyDocumentSerde } // Lists a summary of the properties of a lineage group. A lineage group provides // a group of shareable lineage entity resources. type LineageGroupSummary struct { // The creation time of the lineage group summary. CreationTime *time.Time // The display name of the lineage group summary. DisplayName *string // The last modified time of the lineage group summary. LastModifiedTime *time.Time // The Amazon Resource Name (ARN) of the lineage group resource. LineageGroupArn *string // The name or Amazon Resource Name (ARN) of the lineage group. LineageGroupName *string noSmithyDocumentSerde } // Defines an Amazon Cognito or your own OIDC IdP user group that is part of a // work team. type MemberDefinition struct { // The Amazon Cognito user group that is part of the work team. CognitoMemberDefinition *CognitoMemberDefinition // A list user groups that exist in your OIDC Identity Provider (IdP). One to ten // groups can be used to create a single private work team. When you add a user // group to the list of Groups , you can add that user group to one or more private // work teams. If you add a user group to a private work team, all workers in that // user group are added to the work team. OidcMemberDefinition *OidcMemberDefinition noSmithyDocumentSerde } // Metadata properties of the tracking entity, trial, or trial component. type MetadataProperties struct { // The commit ID. CommitId *string // The entity this entity was generated by. GeneratedBy *string // The project ID. ProjectId *string // The repository. Repository *string noSmithyDocumentSerde } // The name, value, and date and time of a metric that was emitted to Amazon // CloudWatch. type MetricData struct { // The name of the metric. MetricName *string // The date and time that the algorithm emitted the metric. Timestamp *time.Time // The value of the metric. Value float32 noSmithyDocumentSerde } // Information about the metric for a candidate produced by an AutoML job. type MetricDatum struct { // The name of the metric. MetricName AutoMLMetricEnum // The dataset split from which the AutoML job produced the metric. Set MetricSetSource // The name of the standard metric. For definitions of the standard metrics, see // Autopilot candidate metrics (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-metrics) // . StandardMetricName AutoMLMetricExtendedEnum // The value of the metric. Value float32 noSmithyDocumentSerde } // Specifies a metric that the training algorithm writes to stderr or stdout . You // can view these logs to understand how your training job performs and check for // any errors encountered during training. SageMaker hyperparameter tuning captures // all defined metrics. Specify one of the defined metrics to use as an objective // metric using the TuningObjective (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html#sagemaker-Type-HyperParameterTrainingJobDefinition-TuningObjective) // parameter in the HyperParameterTrainingJobDefinition API to evaluate job // performance during hyperparameter tuning. type MetricDefinition struct { // The name of the metric. // // This member is required. Name *string // A regular expression that searches the output of a training job and gets the // value of the metric. For more information about using regular expressions to // define metrics, see Defining metrics and environment variables (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html) // . // // This member is required. Regex *string noSmithyDocumentSerde } // Details about the metrics source. type MetricsSource struct { // The metric source content type. // // This member is required. ContentType *string // The S3 URI for the metrics source. // // This member is required. S3Uri *string // The hash key used for the metrics source. ContentDigest *string noSmithyDocumentSerde } // The properties of a model as returned by the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) // API. type Model struct { // The containers in the inference pipeline. Containers []ContainerDefinition // A timestamp that indicates when the model was created. CreationTime *time.Time // A set of recommended deployment configurations for the model. DeploymentRecommendation *DeploymentRecommendation // Isolates the model container. No inbound or outbound network calls can be made // to or from the model container. EnableNetworkIsolation bool // The Amazon Resource Name (ARN) of the IAM role that you specified for the model. ExecutionRoleArn *string // Specifies details about how containers in a multi-container endpoint are run. InferenceExecutionConfig *InferenceExecutionConfig // The Amazon Resource Name (ARN) of the model. ModelArn *string // The name of the model. ModelName *string // Describes the container, as part of model definition. PrimaryContainer *ContainerDefinition // A list of key-value pairs associated with the model. For more information, see // Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html) // in the Amazon Web Services General Reference Guide. Tags []Tag // Specifies a VPC that your training jobs and hosted models have access to. // Control access to and from your training and model containers by configuring the // VPC. For more information, see Protect Endpoints by Using an Amazon Virtual // Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html) // and Protect Training Jobs by Using an Amazon Virtual Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html) // . VpcConfig *VpcConfig noSmithyDocumentSerde } // Provides information about the location that is configured for storing model // artifacts. Model artifacts are the output that results from training a model, // and typically consist of trained parameters, a model definition that describes // how to compute inferences, and other metadata. type ModelArtifacts struct { // The path of the S3 object that contains the model artifacts. For example, // s3://bucket-name/keynameprefix/model.tar.gz . // // This member is required. S3ModelArtifacts *string noSmithyDocumentSerde } // Docker container image configuration object for the model bias job. type ModelBiasAppSpecification struct { // JSON formatted S3 file that defines bias parameters. For more information on // this JSON configuration file, see Configure bias parameters (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-config-json-monitor-bias-parameters.html) // . // // This member is required. ConfigUri *string // The container image to be run by the model bias job. // // This member is required. ImageUri *string // Sets the environment variables in the Docker container. Environment map[string]string noSmithyDocumentSerde } // The configuration for a baseline model bias job. type ModelBiasBaselineConfig struct { // The name of the baseline model bias job. BaseliningJobName *string // The constraints resource for a monitoring job. ConstraintsResource *MonitoringConstraintsResource noSmithyDocumentSerde } // Inputs for the model bias job. type ModelBiasJobInput struct { // Location of ground truth labels to use in model bias job. // // This member is required. GroundTruthS3Input *MonitoringGroundTruthS3Input // Input object for the batch transform job. BatchTransformInput *BatchTransformInput // Input object for the endpoint EndpointInput *EndpointInput noSmithyDocumentSerde } // An Amazon SageMaker Model Card. type ModelCard struct { // The content of the model card. Content uses the model card JSON schema (https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html#model-cards-json-schema) // and provided as a string. Content *string // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. CreatedBy *UserContext // The date and time that the model card was created. CreationTime *time.Time // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. LastModifiedBy *UserContext // The date and time that the model card was last modified. LastModifiedTime *time.Time // The Amazon Resource Name (ARN) of the model card. ModelCardArn *string // The unique name of the model card. ModelCardName *string // The approval status of the model card within your organization. Different // organizations might have different criteria for model card review and approval. // - Draft : The model card is a work in progress. // - PendingReview : The model card is pending review. // - Approved : The model card is approved. // - Archived : The model card is archived. No more updates should be made to the // model card, but it can still be exported. ModelCardStatus ModelCardStatus // The version of the model card. ModelCardVersion int32 // The unique name (ID) of the model. ModelId *string // The model package group that contains the model package. Only relevant for // model cards created for model packages in the Amazon SageMaker Model Registry. ModelPackageGroupName *string // The risk rating of the model. Different organizations might have different // criteria for model card risk ratings. For more information, see Risk ratings (https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards-risk-rating.html) // . RiskRating *string // The security configuration used to protect model card data. SecurityConfig *ModelCardSecurityConfig // Key-value pairs used to manage metadata for the model card. Tags []Tag noSmithyDocumentSerde } // The artifacts of the model card export job. type ModelCardExportArtifacts struct { // The Amazon S3 URI of the exported model artifacts. // // This member is required. S3ExportArtifacts *string noSmithyDocumentSerde } // The summary of the Amazon SageMaker Model Card export job. type ModelCardExportJobSummary struct { // The date and time that the model card export job was created. // // This member is required. CreatedAt *time.Time // The date and time that the model card export job was last modified.. // // This member is required. LastModifiedAt *time.Time // The Amazon Resource Name (ARN) of the model card export job. // // This member is required. ModelCardExportJobArn *string // The name of the model card export job. // // This member is required. ModelCardExportJobName *string // The name of the model card that the export job exports. // // This member is required. ModelCardName *string // The version of the model card that the export job exports. // // This member is required. ModelCardVersion int32 // The completion status of the model card export job. // // This member is required. Status ModelCardExportJobStatus noSmithyDocumentSerde } // Configure the export output details for an Amazon SageMaker Model Card. type ModelCardExportOutputConfig struct { // The Amazon S3 output path to export your model card PDF. // // This member is required. S3OutputPath *string noSmithyDocumentSerde } // Configure the security settings to protect model card data. type ModelCardSecurityConfig struct { // A Key Management Service key ID (https://docs.aws.amazon.com/kms/latest/developerguide/concepts.html#key-id-key-id) // to use for encrypting a model card. KmsKeyId *string noSmithyDocumentSerde } // A summary of the model card. type ModelCardSummary struct { // The date and time that the model card was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the model card. // // This member is required. ModelCardArn *string // The name of the model card. // // This member is required. ModelCardName *string // The approval status of the model card within your organization. Different // organizations might have different criteria for model card review and approval. // - Draft : The model card is a work in progress. // - PendingReview : The model card is pending review. // - Approved : The model card is approved. // - Archived : The model card is archived. No more updates should be made to the // model card, but it can still be exported. // // This member is required. ModelCardStatus ModelCardStatus // The date and time that the model card was last modified. LastModifiedTime *time.Time noSmithyDocumentSerde } // A summary of a specific version of the model card. type ModelCardVersionSummary struct { // The date and time that the model card version was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the model card. // // This member is required. ModelCardArn *string // The name of the model card. // // This member is required. ModelCardName *string // The approval status of the model card version within your organization. // Different organizations might have different criteria for model card review and // approval. // - Draft : The model card is a work in progress. // - PendingReview : The model card is pending review. // - Approved : The model card is approved. // - Archived : The model card is archived. No more updates should be made to the // model card, but it can still be exported. // // This member is required. ModelCardStatus ModelCardStatus // A version of the model card. // // This member is required. ModelCardVersion int32 // The time date and time that the model card version was last modified. LastModifiedTime *time.Time noSmithyDocumentSerde } // Configures the timeout and maximum number of retries for processing a transform // job invocation. type ModelClientConfig struct { // The maximum number of retries when invocation requests are failing. The default // value is 3. InvocationsMaxRetries *int32 // The timeout value in seconds for an invocation request. The default value is // 600. InvocationsTimeoutInSeconds *int32 noSmithyDocumentSerde } // Defines the model configuration. Includes the specification name and // environment parameters. type ModelConfiguration struct { // The name of the compilation job used to create the recommended model artifacts. CompilationJobName *string // Defines the environment parameters that includes key, value types, and values. EnvironmentParameters []EnvironmentParameter // The inference specification name in the model package version. InferenceSpecificationName *string noSmithyDocumentSerde } // An endpoint that hosts a model displayed in the Amazon SageMaker Model // Dashboard. type ModelDashboardEndpoint struct { // A timestamp that indicates when the endpoint was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the endpoint. // // This member is required. EndpointArn *string // The endpoint name. // // This member is required. EndpointName *string // The endpoint status. // // This member is required. EndpointStatus EndpointStatus // The last time the endpoint was modified. // // This member is required. LastModifiedTime *time.Time noSmithyDocumentSerde } // An alert action taken to light up an icon on the Amazon SageMaker Model // Dashboard when an alert goes into InAlert status. type ModelDashboardIndicatorAction struct { // Indicates whether the alert action is turned on. Enabled bool noSmithyDocumentSerde } // A model displayed in the Amazon SageMaker Model Dashboard. type ModelDashboardModel struct { // The endpoints that host a model. Endpoints []ModelDashboardEndpoint // A batch transform job. For information about SageMaker batch transform, see Use // Batch Transform (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html) // . LastBatchTransformJob *TransformJob // A model displayed in the Model Dashboard. Model *Model // The model card for a model. ModelCard *ModelDashboardModelCard // The monitoring schedules for a model. MonitoringSchedules []ModelDashboardMonitoringSchedule noSmithyDocumentSerde } // The model card for a model displayed in the Amazon SageMaker Model Dashboard. type ModelDashboardModelCard struct { // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. CreatedBy *UserContext // A timestamp that indicates when the model card was created. CreationTime *time.Time // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. LastModifiedBy *UserContext // A timestamp that indicates when the model card was last updated. LastModifiedTime *time.Time // The Amazon Resource Name (ARN) for a model card. ModelCardArn *string // The name of a model card. ModelCardName *string // The model card status. ModelCardStatus ModelCardStatus // The model card version. ModelCardVersion int32 // For models created in SageMaker, this is the model ARN. For models created // outside of SageMaker, this is a user-customized string. ModelId *string // A model card's risk rating. Can be low, medium, or high. RiskRating *string // The KMS Key ID ( KMSKeyId ) for encryption of model card information. SecurityConfig *ModelCardSecurityConfig // The tags associated with a model card. Tags []Tag noSmithyDocumentSerde } // A monitoring schedule for a model displayed in the Amazon SageMaker Model // Dashboard. type ModelDashboardMonitoringSchedule struct { // A timestamp that indicates when the monitoring schedule was created. CreationTime *time.Time // The endpoint which is monitored. EndpointName *string // If a monitoring job failed, provides the reason. FailureReason *string // A timestamp that indicates when the monitoring schedule was last updated. LastModifiedTime *time.Time // Summary of information about the last monitoring job to run. LastMonitoringExecutionSummary *MonitoringExecutionSummary // A JSON array where each element is a summary for a monitoring alert. MonitoringAlertSummaries []MonitoringAlertSummary // The Amazon Resource Name (ARN) of a monitoring schedule. MonitoringScheduleArn *string // Configures the monitoring schedule and defines the monitoring job. MonitoringScheduleConfig *MonitoringScheduleConfig // The name of a monitoring schedule. MonitoringScheduleName *string // The status of the monitoring schedule. MonitoringScheduleStatus ScheduleStatus // The monitor type of a model monitor. MonitoringType MonitoringType noSmithyDocumentSerde } // Data quality constraints and statistics for a model. type ModelDataQuality struct { // Data quality constraints for a model. Constraints *MetricsSource // Data quality statistics for a model. Statistics *MetricsSource noSmithyDocumentSerde } // Specifies the location of ML model data to deploy. If specified, you must // specify one and only one of the available data sources. type ModelDataSource struct { // Specifies the S3 location of ML model data to deploy. // // This member is required. S3DataSource *S3ModelDataSource noSmithyDocumentSerde } // Specifies how to generate the endpoint name for an automatic one-click // Autopilot model deployment. type ModelDeployConfig struct { // Set to True to automatically generate an endpoint name for a one-click // Autopilot model deployment; set to False otherwise. The default value is False . // If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; // otherwise a 400 error is thrown. AutoGenerateEndpointName bool // Specifies the endpoint name to use for a one-click Autopilot model deployment // if the endpoint name is not generated automatically. Specify the EndpointName // if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error // is thrown. EndpointName *string noSmithyDocumentSerde } // Provides information about the endpoint of the model deployment. type ModelDeployResult struct { // The name of the endpoint to which the model has been deployed. If model // deployment fails, this field is omitted from the response. EndpointName *string noSmithyDocumentSerde } // Provides information to verify the integrity of stored model artifacts. type ModelDigests struct { // Provides a hash value that uniquely identifies the stored model artifacts. ArtifactDigest *string noSmithyDocumentSerde } // Docker container image configuration object for the model explainability job. type ModelExplainabilityAppSpecification struct { // JSON formatted S3 file that defines explainability parameters. For more // information on this JSON configuration file, see Configure model explainability // parameters (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-config-json-monitor-model-explainability-parameters.html) // . // // This member is required. ConfigUri *string // The container image to be run by the model explainability job. // // This member is required. ImageUri *string // Sets the environment variables in the Docker container. Environment map[string]string noSmithyDocumentSerde } // The configuration for a baseline model explainability job. type ModelExplainabilityBaselineConfig struct { // The name of the baseline model explainability job. BaseliningJobName *string // The constraints resource for a monitoring job. ConstraintsResource *MonitoringConstraintsResource noSmithyDocumentSerde } // Inputs for the model explainability job. type ModelExplainabilityJobInput struct { // Input object for the batch transform job. BatchTransformInput *BatchTransformInput // Input object for the endpoint EndpointInput *EndpointInput noSmithyDocumentSerde } // The configuration for the infrastructure that the model will be deployed to. type ModelInfrastructureConfig struct { // The inference option to which to deploy your model. Possible values are the // following: // - RealTime : Deploy to real-time inference. // // This member is required. InfrastructureType ModelInfrastructureType // The infrastructure configuration for deploying the model to real-time inference. // // This member is required. RealTimeInferenceConfig *RealTimeInferenceConfig noSmithyDocumentSerde } // Input object for the model. type ModelInput struct { // The input configuration object for the model. // // This member is required. DataInputConfig *string noSmithyDocumentSerde } // The model latency threshold. type ModelLatencyThreshold struct { // The model latency percentile threshold. Percentile *string // The model latency percentile value in milliseconds. ValueInMilliseconds int32 noSmithyDocumentSerde } // Part of the search expression. You can specify the name and value (domain, // task, framework, framework version, task, and model). type ModelMetadataFilter struct { // The name of the of the model to filter by. // // This member is required. Name ModelMetadataFilterType // The value to filter the model metadata. // // This member is required. Value *string noSmithyDocumentSerde } // One or more filters that searches for the specified resource or resources in a // search. All resource objects that satisfy the expression's condition are // included in the search results type ModelMetadataSearchExpression struct { // A list of filter objects. Filters []ModelMetadataFilter noSmithyDocumentSerde } // A summary of the model metadata. type ModelMetadataSummary struct { // The machine learning domain of the model. // // This member is required. Domain *string // The machine learning framework of the model. // // This member is required. Framework *string // The framework version of the model. // // This member is required. FrameworkVersion *string // The name of the model. // // This member is required. Model *string // The machine learning task of the model. // // This member is required. Task *string noSmithyDocumentSerde } // Contains metrics captured from a model. type ModelMetrics struct { // Metrics that measure bais in a model. Bias *Bias // Metrics that help explain a model. Explainability *Explainability // Metrics that measure the quality of the input data for a model. ModelDataQuality *ModelDataQuality // Metrics that measure the quality of a model. ModelQuality *ModelQuality noSmithyDocumentSerde } // A versioned model that can be deployed for SageMaker inference. type ModelPackage struct { // An array of additional Inference Specification objects. AdditionalInferenceSpecifications []AdditionalInferenceSpecificationDefinition // A description provided when the model approval is set. ApprovalDescription *string // Whether the model package is to be certified to be listed on Amazon Web // Services Marketplace. For information about listing model packages on Amazon Web // Services Marketplace, see List Your Algorithm or Model Package on Amazon Web // Services Marketplace (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-list.html) // . CertifyForMarketplace bool // Information about the user who created or modified an experiment, trial, trial // component, lineage group, or project. CreatedBy *UserContext // The time that the model package was created. CreationTime *time.Time // The metadata properties for the model package. CustomerMetadataProperties map[string]string // The machine learning domain of your model package and its components. Common // machine learning domains include computer vision and natural language // processing. Domain *string // Represents the drift check baselines that can be used when the model monitor is // set using the model package. DriftCheckBaselines *DriftCheckBaselines // Defines how to perform inference generation after a training job is run. InferenceSpecification *InferenceSpecification // Information about the user who created or modified an experiment, trial, trial // component, lineage group, or project. LastModifiedBy *UserContext // The last time the model package was modified. LastModifiedTime *time.Time // Metadata properties of the tracking entity, trial, or trial component. MetadataProperties *MetadataProperties // The approval status of the model. This can be one of the following values. // - APPROVED - The model is approved // - REJECTED - The model is rejected. // - PENDING_MANUAL_APPROVAL - The model is waiting for manual approval. ModelApprovalStatus ModelApprovalStatus // Metrics for the model. ModelMetrics *ModelMetrics // The Amazon Resource Name (ARN) of the model package. ModelPackageArn *string // The description of the model package. ModelPackageDescription *string // The model group to which the model belongs. ModelPackageGroupName *string // The name of the model. ModelPackageName *string // The status of the model package. This can be one of the following values. // - PENDING - The model package is pending being created. // - IN_PROGRESS - The model package is in the process of being created. // - COMPLETED - The model package was successfully created. // - FAILED - The model package failed. // - DELETING - The model package is in the process of being deleted. ModelPackageStatus ModelPackageStatus // Specifies the validation and image scan statuses of the model package. ModelPackageStatusDetails *ModelPackageStatusDetails // The version number of a versioned model. ModelPackageVersion *int32 // The Amazon Simple Storage Service path where the sample payload are stored. // This path must point to a single gzip compressed tar archive (.tar.gz suffix). SamplePayloadUrl *string // A list of algorithms that were used to create a model package. SourceAlgorithmSpecification *SourceAlgorithmSpecification // A list of the tags associated with the model package. For more information, see // Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html) // in the Amazon Web Services General Reference Guide. Tags []Tag // The machine learning task your model package accomplishes. Common machine // learning tasks include object detection and image classification. Task *string // Specifies batch transform jobs that SageMaker runs to validate your model // package. ValidationSpecification *ModelPackageValidationSpecification noSmithyDocumentSerde } // Describes the Docker container for the model package. type ModelPackageContainerDefinition struct { // The Amazon EC2 Container Registry (Amazon ECR) path where inference code is // stored. If you are using your own custom algorithm instead of an algorithm // provided by SageMaker, the inference code must meet SageMaker requirements. // SageMaker supports both registry/repository[:tag] and // registry/repository[@digest] image path formats. For more information, see // Using Your Own Algorithms with Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html) // . // // This member is required. Image *string // The DNS host name for the Docker container. ContainerHostname *string // The environment variables to set in the Docker container. Each key and value in // the Environment string to string map can have length of up to 1024. We support // up to 16 entries in the map. Environment map[string]string // The machine learning framework of the model package container image. Framework *string // The framework version of the Model Package Container Image. FrameworkVersion *string // An MD5 hash of the training algorithm that identifies the Docker image used for // training. ImageDigest *string // The Amazon S3 path where the model artifacts, which result from model training, // are stored. This path must point to a single gzip compressed tar archive ( // .tar.gz suffix). The model artifacts must be in an S3 bucket that is in the same // region as the model package. ModelDataUrl *string // A structure with Model Input details. ModelInput *ModelInput // The name of a pre-trained machine learning benchmarked by Amazon SageMaker // Inference Recommender model that matches your model. You can find a list of // benchmarked models by calling ListModelMetadata . NearestModelName *string // The Amazon Web Services Marketplace product ID of the model package. ProductId *string noSmithyDocumentSerde } // A group of versioned models in the model registry. type ModelPackageGroup struct { // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. CreatedBy *UserContext // The time that the model group was created. CreationTime *time.Time // The Amazon Resource Name (ARN) of the model group. ModelPackageGroupArn *string // The description for the model group. ModelPackageGroupDescription *string // The name of the model group. ModelPackageGroupName *string // The status of the model group. This can be one of the following values. // - PENDING - The model group is pending being created. // - IN_PROGRESS - The model group is in the process of being created. // - COMPLETED - The model group was successfully created. // - FAILED - The model group failed. // - DELETING - The model group is in the process of being deleted. // - DELETE_FAILED - SageMaker failed to delete the model group. ModelPackageGroupStatus ModelPackageGroupStatus // A list of the tags associated with the model group. For more information, see // Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html) // in the Amazon Web Services General Reference Guide. Tags []Tag noSmithyDocumentSerde } // Summary information about a model group. type ModelPackageGroupSummary struct { // The time that the model group was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the model group. // // This member is required. ModelPackageGroupArn *string // The name of the model group. // // This member is required. ModelPackageGroupName *string // The status of the model group. // // This member is required. ModelPackageGroupStatus ModelPackageGroupStatus // A description of the model group. ModelPackageGroupDescription *string noSmithyDocumentSerde } // Specifies the validation and image scan statuses of the model package. type ModelPackageStatusDetails struct { // The validation status of the model package. // // This member is required. ValidationStatuses []ModelPackageStatusItem // The status of the scan of the Docker image container for the model package. ImageScanStatuses []ModelPackageStatusItem noSmithyDocumentSerde } // Represents the overall status of a model package. type ModelPackageStatusItem struct { // The name of the model package for which the overall status is being reported. // // This member is required. Name *string // The current status. // // This member is required. Status DetailedModelPackageStatus // if the overall status is Failed , the reason for the failure. FailureReason *string noSmithyDocumentSerde } // Provides summary information about a model package. type ModelPackageSummary struct { // A timestamp that shows when the model package was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the model package. // // This member is required. ModelPackageArn *string // The name of the model package. // // This member is required. ModelPackageName *string // The overall status of the model package. // // This member is required. ModelPackageStatus ModelPackageStatus // The approval status of the model. This can be one of the following values. // - APPROVED - The model is approved // - REJECTED - The model is rejected. // - PENDING_MANUAL_APPROVAL - The model is waiting for manual approval. ModelApprovalStatus ModelApprovalStatus // A brief description of the model package. ModelPackageDescription *string // If the model package is a versioned model, the model group that the versioned // model belongs to. ModelPackageGroupName *string // If the model package is a versioned model, the version of the model. ModelPackageVersion *int32 noSmithyDocumentSerde } // Contains data, such as the inputs and targeted instance types that are used in // the process of validating the model package. The data provided in the validation // profile is made available to your buyers on Amazon Web Services Marketplace. type ModelPackageValidationProfile struct { // The name of the profile for the model package. // // This member is required. ProfileName *string // The TransformJobDefinition object that describes the transform job used for the // validation of the model package. // // This member is required. TransformJobDefinition *TransformJobDefinition noSmithyDocumentSerde } // Specifies batch transform jobs that SageMaker runs to validate your model // package. type ModelPackageValidationSpecification struct { // An array of ModelPackageValidationProfile objects, each of which specifies a // batch transform job that SageMaker runs to validate your model package. // // This member is required. ValidationProfiles []ModelPackageValidationProfile // The IAM roles to be used for the validation of the model package. // // This member is required. ValidationRole *string noSmithyDocumentSerde } // Model quality statistics and constraints. type ModelQuality struct { // Model quality constraints. Constraints *MetricsSource // Model quality statistics. Statistics *MetricsSource noSmithyDocumentSerde } // Container image configuration object for the monitoring job. type ModelQualityAppSpecification struct { // The address of the container image that the monitoring job runs. // // This member is required. ImageUri *string // An array of arguments for the container used to run the monitoring job. ContainerArguments []string // Specifies the entrypoint for a container that the monitoring job runs. ContainerEntrypoint []string // Sets the environment variables in the container that the monitoring job runs. Environment map[string]string // An Amazon S3 URI to a script that is called after analysis has been performed. // Applicable only for the built-in (first party) containers. PostAnalyticsProcessorSourceUri *string // The machine learning problem type of the model that the monitoring job monitors. ProblemType MonitoringProblemType // An Amazon S3 URI to a script that is called per row prior to running analysis. // It can base64 decode the payload and convert it into a flatted json so that the // built-in container can use the converted data. Applicable only for the built-in // (first party) containers. RecordPreprocessorSourceUri *string noSmithyDocumentSerde } // Configuration for monitoring constraints and monitoring statistics. These // baseline resources are compared against the results of the current job from the // series of jobs scheduled to collect data periodically. type ModelQualityBaselineConfig struct { // The name of the job that performs baselining for the monitoring job. BaseliningJobName *string // The constraints resource for a monitoring job. ConstraintsResource *MonitoringConstraintsResource noSmithyDocumentSerde } // The input for the model quality monitoring job. Currently endponts are // supported for input for model quality monitoring jobs. type ModelQualityJobInput struct { // The ground truth label provided for the model. // // This member is required. GroundTruthS3Input *MonitoringGroundTruthS3Input // Input object for the batch transform job. BatchTransformInput *BatchTransformInput // Input object for the endpoint EndpointInput *EndpointInput noSmithyDocumentSerde } // The model registry settings for the SageMaker Canvas application. type ModelRegisterSettings struct { // The Amazon Resource Name (ARN) of the SageMaker model registry account. // Required only to register model versions created by a different SageMaker Canvas // Amazon Web Services account than the Amazon Web Services account in which // SageMaker model registry is set up. CrossAccountModelRegisterRoleArn *string // Describes whether the integration to the model registry is enabled or disabled // in the Canvas application. Status FeatureStatus noSmithyDocumentSerde } // Metadata for Model steps. type ModelStepMetadata struct { // The Amazon Resource Name (ARN) of the created model. Arn *string noSmithyDocumentSerde } // Provides summary information about a model. type ModelSummary struct { // A timestamp that indicates when the model was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the model. // // This member is required. ModelArn *string // The name of the model that you want a summary for. // // This member is required. ModelName *string noSmithyDocumentSerde } // Contains information about the deployment options of a model. type ModelVariantConfig struct { // The configuration for the infrastructure that the model will be deployed to. // // This member is required. InfrastructureConfig *ModelInfrastructureConfig // The name of the Amazon SageMaker Model entity. // // This member is required. ModelName *string // The name of the variant. // // This member is required. VariantName *string noSmithyDocumentSerde } // Summary of the deployment configuration of a model. type ModelVariantConfigSummary struct { // The configuration of the infrastructure that the model has been deployed to. // // This member is required. InfrastructureConfig *ModelInfrastructureConfig // The name of the Amazon SageMaker Model entity. // // This member is required. ModelName *string // The status of deployment for the model variant on the hosted inference // endpoint. // - Creating - Amazon SageMaker is preparing the model variant on the hosted // inference endpoint. // - InService - The model variant is running on the hosted inference endpoint. // - Updating - Amazon SageMaker is updating the model variant on the hosted // inference endpoint. // - Deleting - Amazon SageMaker is deleting the model variant on the hosted // inference endpoint. // - Deleted - The model variant has been deleted on the hosted inference // endpoint. This can only happen after stopping the experiment. // // This member is required. Status ModelVariantStatus // The name of the variant. // // This member is required. VariantName *string noSmithyDocumentSerde } // A list of alert actions taken in response to an alert going into InAlert status. type MonitoringAlertActions struct { // An alert action taken to light up an icon on the Model Dashboard when an alert // goes into InAlert status. ModelDashboardIndicator *ModelDashboardIndicatorAction noSmithyDocumentSerde } // Provides summary information of an alert's history. type MonitoringAlertHistorySummary struct { // The current alert status of an alert. // // This member is required. AlertStatus MonitoringAlertStatus // A timestamp that indicates when the first alert transition occurred in an alert // history. An alert transition can be from status InAlert to OK , or from OK to // InAlert . // // This member is required. CreationTime *time.Time // The name of a monitoring alert. // // This member is required. MonitoringAlertName *string // The name of a monitoring schedule. // // This member is required. MonitoringScheduleName *string noSmithyDocumentSerde } // Provides summary information about a monitor alert. type MonitoringAlertSummary struct { // A list of alert actions taken in response to an alert going into InAlert status. // // This member is required. Actions *MonitoringAlertActions // The current status of an alert. // // This member is required. AlertStatus MonitoringAlertStatus // A timestamp that indicates when a monitor alert was created. // // This member is required. CreationTime *time.Time // Within EvaluationPeriod , how many execution failures will raise an alert. // // This member is required. DatapointsToAlert *int32 // The number of most recent monitoring executions to consider when evaluating // alert status. // // This member is required. EvaluationPeriod *int32 // A timestamp that indicates when a monitor alert was last updated. // // This member is required. LastModifiedTime *time.Time // The name of a monitoring alert. // // This member is required. MonitoringAlertName *string noSmithyDocumentSerde } // Container image configuration object for the monitoring job. type MonitoringAppSpecification struct { // The container image to be run by the monitoring job. // // This member is required. ImageUri *string // An array of arguments for the container used to run the monitoring job. ContainerArguments []string // Specifies the entrypoint for a container used to run the monitoring job. ContainerEntrypoint []string // An Amazon S3 URI to a script that is called after analysis has been performed. // Applicable only for the built-in (first party) containers. PostAnalyticsProcessorSourceUri *string // An Amazon S3 URI to a script that is called per row prior to running analysis. // It can base64 decode the payload and convert it into a flatted json so that the // built-in container can use the converted data. Applicable only for the built-in // (first party) containers. RecordPreprocessorSourceUri *string noSmithyDocumentSerde } // Configuration for monitoring constraints and monitoring statistics. These // baseline resources are compared against the results of the current job from the // series of jobs scheduled to collect data periodically. type MonitoringBaselineConfig struct { // The name of the job that performs baselining for the monitoring job. BaseliningJobName *string // The baseline constraint file in Amazon S3 that the current monitoring job // should validated against. ConstraintsResource *MonitoringConstraintsResource // The baseline statistics file in Amazon S3 that the current monitoring job // should be validated against. StatisticsResource *MonitoringStatisticsResource noSmithyDocumentSerde } // Configuration for the cluster used to run model monitoring jobs. type MonitoringClusterConfig struct { // The number of ML compute instances to use in the model monitoring job. For // distributed processing jobs, specify a value greater than 1. The default value // is 1. // // This member is required. InstanceCount *int32 // The ML compute instance type for the processing job. // // This member is required. InstanceType ProcessingInstanceType // The size of the ML storage volume, in gigabytes, that you want to provision. // You must specify sufficient ML storage for your scenario. // // This member is required. VolumeSizeInGB *int32 // The Amazon Web Services Key Management Service (Amazon Web Services KMS) key // that Amazon SageMaker uses to encrypt data on the storage volume attached to the // ML compute instance(s) that run the model monitoring job. VolumeKmsKeyId *string noSmithyDocumentSerde } // The constraints resource for a monitoring job. type MonitoringConstraintsResource struct { // The Amazon S3 URI for the constraints resource. S3Uri *string noSmithyDocumentSerde } // Represents the CSV dataset format used when running a monitoring job. type MonitoringCsvDatasetFormat struct { // Indicates if the CSV data has a header. Header bool noSmithyDocumentSerde } // Represents the dataset format used when running a monitoring job. type MonitoringDatasetFormat struct { // The CSV dataset used in the monitoring job. Csv *MonitoringCsvDatasetFormat // The JSON dataset used in the monitoring job Json *MonitoringJsonDatasetFormat // The Parquet dataset used in the monitoring job Parquet *MonitoringParquetDatasetFormat noSmithyDocumentSerde } // Summary of information about the last monitoring job to run. type MonitoringExecutionSummary struct { // The time at which the monitoring job was created. // // This member is required. CreationTime *time.Time // A timestamp that indicates the last time the monitoring job was modified. // // This member is required. LastModifiedTime *time.Time // The status of the monitoring job. // // This member is required. MonitoringExecutionStatus ExecutionStatus // The name of the monitoring schedule. // // This member is required. MonitoringScheduleName *string // The time the monitoring job was scheduled. // // This member is required. ScheduledTime *time.Time // The name of the endpoint used to run the monitoring job. EndpointName *string // Contains the reason a monitoring job failed, if it failed. FailureReason *string // The name of the monitoring job. MonitoringJobDefinitionName *string // The type of the monitoring job. MonitoringType MonitoringType // The Amazon Resource Name (ARN) of the monitoring job. ProcessingJobArn *string noSmithyDocumentSerde } // The ground truth labels for the dataset used for the monitoring job. type MonitoringGroundTruthS3Input struct { // The address of the Amazon S3 location of the ground truth labels. S3Uri *string noSmithyDocumentSerde } // The inputs for a monitoring job. type MonitoringInput struct { // Input object for the batch transform job. BatchTransformInput *BatchTransformInput // The endpoint for a monitoring job. EndpointInput *EndpointInput noSmithyDocumentSerde } // Defines the monitoring job. type MonitoringJobDefinition struct { // Configures the monitoring job to run a specified Docker container image. // // This member is required. MonitoringAppSpecification *MonitoringAppSpecification // The array of inputs for the monitoring job. Currently we support monitoring an // Amazon SageMaker Endpoint. // // This member is required. MonitoringInputs []MonitoringInput // The array of outputs from the monitoring job to be uploaded to Amazon Simple // Storage Service (Amazon S3). // // This member is required. MonitoringOutputConfig *MonitoringOutputConfig // Identifies the resources, ML compute instances, and ML storage volumes to // deploy for a monitoring job. In distributed processing, you specify more than // one instance. // // This member is required. MonitoringResources *MonitoringResources // The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume // to perform tasks on your behalf. // // This member is required. RoleArn *string // Baseline configuration used to validate that the data conforms to the specified // constraints and statistics BaselineConfig *MonitoringBaselineConfig // Sets the environment variables in the Docker container. Environment map[string]string // Specifies networking options for an monitoring job. NetworkConfig *NetworkConfig // Specifies a time limit for how long the monitoring job is allowed to run. StoppingCondition *MonitoringStoppingCondition noSmithyDocumentSerde } // Summary information about a monitoring job. type MonitoringJobDefinitionSummary struct { // The time that the monitoring job was created. // // This member is required. CreationTime *time.Time // The name of the endpoint that the job monitors. // // This member is required. EndpointName *string // The Amazon Resource Name (ARN) of the monitoring job. // // This member is required. MonitoringJobDefinitionArn *string // The name of the monitoring job. // // This member is required. MonitoringJobDefinitionName *string noSmithyDocumentSerde } // Represents the JSON dataset format used when running a monitoring job. type MonitoringJsonDatasetFormat struct { // Indicates if the file should be read as a json object per line. Line bool noSmithyDocumentSerde } // The networking configuration for the monitoring job. type MonitoringNetworkConfig struct { // Whether to encrypt all communications between the instances used for the // monitoring jobs. Choose True to encrypt communications. Encryption provides // greater security for distributed jobs, but the processing might take longer. EnableInterContainerTrafficEncryption bool // Whether to allow inbound and outbound network calls to and from the containers // used for the monitoring job. EnableNetworkIsolation bool // Specifies a VPC that your training jobs and hosted models have access to. // Control access to and from your training and model containers by configuring the // VPC. For more information, see Protect Endpoints by Using an Amazon Virtual // Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html) // and Protect Training Jobs by Using an Amazon Virtual Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html) // . VpcConfig *VpcConfig noSmithyDocumentSerde } // The output object for a monitoring job. type MonitoringOutput struct { // The Amazon S3 storage location where the results of a monitoring job are saved. // // This member is required. S3Output *MonitoringS3Output noSmithyDocumentSerde } // The output configuration for monitoring jobs. type MonitoringOutputConfig struct { // Monitoring outputs for monitoring jobs. This is where the output of the // periodic monitoring jobs is uploaded. // // This member is required. MonitoringOutputs []MonitoringOutput // The Amazon Web Services Key Management Service (Amazon Web Services KMS) key // that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon // S3 server-side encryption. KmsKeyId *string noSmithyDocumentSerde } // Represents the Parquet dataset format used when running a monitoring job. type MonitoringParquetDatasetFormat struct { noSmithyDocumentSerde } // Identifies the resources to deploy for a monitoring job. type MonitoringResources struct { // The configuration for the cluster resources used to run the processing job. // // This member is required. ClusterConfig *MonitoringClusterConfig noSmithyDocumentSerde } // Information about where and how you want to store the results of a monitoring // job. type MonitoringS3Output struct { // The local path to the Amazon S3 storage location where Amazon SageMaker saves // the results of a monitoring job. LocalPath is an absolute path for the output // data. // // This member is required. LocalPath *string // A URI that identifies the Amazon S3 storage location where Amazon SageMaker // saves the results of a monitoring job. // // This member is required. S3Uri *string // Whether to upload the results of the monitoring job continuously or after the // job completes. S3UploadMode ProcessingS3UploadMode noSmithyDocumentSerde } // A schedule for a model monitoring job. For information about model monitor, see // Amazon SageMaker Model Monitor (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) // . type MonitoringSchedule struct { // The time that the monitoring schedule was created. CreationTime *time.Time // The endpoint that hosts the model being monitored. EndpointName *string // If the monitoring schedule failed, the reason it failed. FailureReason *string // The last time the monitoring schedule was changed. LastModifiedTime *time.Time // Summary of information about the last monitoring job to run. LastMonitoringExecutionSummary *MonitoringExecutionSummary // The Amazon Resource Name (ARN) of the monitoring schedule. MonitoringScheduleArn *string // Configures the monitoring schedule and defines the monitoring job. MonitoringScheduleConfig *MonitoringScheduleConfig // The name of the monitoring schedule. MonitoringScheduleName *string // The status of the monitoring schedule. This can be one of the following values. // - PENDING - The schedule is pending being created. // - FAILED - The schedule failed. // - SCHEDULED - The schedule was successfully created. // - STOPPED - The schedule was stopped. MonitoringScheduleStatus ScheduleStatus // The type of the monitoring job definition to schedule. MonitoringType MonitoringType // A list of the tags associated with the monitoring schedlue. For more // information, see Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html) // in the Amazon Web Services General Reference Guide. Tags []Tag noSmithyDocumentSerde } // Configures the monitoring schedule and defines the monitoring job. type MonitoringScheduleConfig struct { // Defines the monitoring job. MonitoringJobDefinition *MonitoringJobDefinition // The name of the monitoring job definition to schedule. MonitoringJobDefinitionName *string // The type of the monitoring job definition to schedule. MonitoringType MonitoringType // Configures the monitoring schedule. ScheduleConfig *ScheduleConfig noSmithyDocumentSerde } // Summarizes the monitoring schedule. type MonitoringScheduleSummary struct { // The creation time of the monitoring schedule. // // This member is required. CreationTime *time.Time // The last time the monitoring schedule was modified. // // This member is required. LastModifiedTime *time.Time // The Amazon Resource Name (ARN) of the monitoring schedule. // // This member is required. MonitoringScheduleArn *string // The name of the monitoring schedule. // // This member is required. MonitoringScheduleName *string // The status of the monitoring schedule. // // This member is required. MonitoringScheduleStatus ScheduleStatus // The name of the endpoint using the monitoring schedule. EndpointName *string // The name of the monitoring job definition that the schedule is for. MonitoringJobDefinitionName *string // The type of the monitoring job definition that the schedule is for. MonitoringType MonitoringType noSmithyDocumentSerde } // The statistics resource for a monitoring job. type MonitoringStatisticsResource struct { // The Amazon S3 URI for the statistics resource. S3Uri *string noSmithyDocumentSerde } // A time limit for how long the monitoring job is allowed to run before stopping. type MonitoringStoppingCondition struct { // The maximum runtime allowed in seconds. The MaxRuntimeInSeconds cannot exceed // the frequency of the job. For data quality and model explainability, this can be // up to 3600 seconds for an hourly schedule. For model bias and model quality // hourly schedules, this can be up to 1800 seconds. // // This member is required. MaxRuntimeInSeconds int32 noSmithyDocumentSerde } // Specifies additional configuration for hosting multi-model endpoints. type MultiModelConfig struct { // Whether to cache models for a multi-model endpoint. By default, multi-model // endpoints cache models so that a model does not have to be loaded into memory // each time it is invoked. Some use cases do not benefit from model caching. For // example, if an endpoint hosts a large number of models that are each invoked // infrequently, the endpoint might perform better if you disable model caching. To // disable model caching, set the value of this parameter to Disabled . ModelCacheSetting ModelCacheSetting noSmithyDocumentSerde } // The VpcConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html) // configuration object that specifies the VPC that you want the compilation jobs // to connect to. For more information on controlling access to your Amazon S3 // buckets used for compilation job, see Give Amazon SageMaker Compilation Jobs // Access to Resources in Your Amazon VPC (https://docs.aws.amazon.com/sagemaker/latest/dg/neo-vpc.html) // . type NeoVpcConfig struct { // The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the // security groups for the VPC that is specified in the Subnets field. // // This member is required. SecurityGroupIds []string // The ID of the subnets in the VPC that you want to connect the compilation job // to for accessing the model in Amazon S3. // // This member is required. Subnets []string noSmithyDocumentSerde } // A list of nested Filter (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Filter.html) // objects. A resource must satisfy the conditions of all filters to be included in // the results returned from the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) // API. For example, to filter on a training job's InputDataConfig property with a // specific channel name and S3Uri prefix, define the following filters: // - '{Name:"InputDataConfig.ChannelName", "Operator":"Equals", // "Value":"train"}', // - '{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", // "Operator":"Contains", "Value":"mybucket/catdata"}' type NestedFilters struct { // A list of filters. Each filter acts on a property. Filters must contain at // least one Filters value. For example, a NestedFilters call might include a // filter on the PropertyName parameter of the InputDataConfig property: // InputDataConfig.DataSource.S3DataSource.S3Uri . // // This member is required. Filters []Filter // The name of the property to use in the nested filters. The value must match a // listed property name, such as InputDataConfig . // // This member is required. NestedPropertyName *string noSmithyDocumentSerde } // Networking options for a job, such as network traffic encryption between // containers, whether to allow inbound and outbound network calls to and from // containers, and the VPC subnets and security groups to use for VPC-enabled jobs. type NetworkConfig struct { // Whether to encrypt all communications between distributed processing jobs. // Choose True to encrypt communications. Encryption provides greater security for // distributed processing jobs, but the processing might take longer. EnableInterContainerTrafficEncryption bool // Whether to allow inbound and outbound network calls to and from the containers // used for the processing job. EnableNetworkIsolation bool // Specifies a VPC that your training jobs and hosted models have access to. // Control access to and from your training and model containers by configuring the // VPC. For more information, see Protect Endpoints by Using an Amazon Virtual // Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html) // and Protect Training Jobs by Using an Amazon Virtual Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html) // . VpcConfig *VpcConfig noSmithyDocumentSerde } // Provides a summary of a notebook instance lifecycle configuration. type NotebookInstanceLifecycleConfigSummary struct { // The Amazon Resource Name (ARN) of the lifecycle configuration. // // This member is required. NotebookInstanceLifecycleConfigArn *string // The name of the lifecycle configuration. // // This member is required. NotebookInstanceLifecycleConfigName *string // A timestamp that tells when the lifecycle configuration was created. CreationTime *time.Time // A timestamp that tells when the lifecycle configuration was last modified. LastModifiedTime *time.Time noSmithyDocumentSerde } // Contains the notebook instance lifecycle configuration script. Each lifecycle // configuration script has a limit of 16384 characters. The value of the $PATH // environment variable that is available to both scripts is // /sbin:bin:/usr/sbin:/usr/bin . View CloudWatch Logs for notebook instance // lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log // stream [notebook-instance-name]/[LifecycleConfigHook] . Lifecycle configuration // scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 // minutes, it fails and the notebook instance is not created or started. For // information about notebook instance lifestyle configurations, see Step 2.1: // (Optional) Customize a Notebook Instance (https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html) // . type NotebookInstanceLifecycleHook struct { // A base64-encoded string that contains a shell script for a notebook instance // lifecycle configuration. Content *string noSmithyDocumentSerde } // Provides summary information for an SageMaker notebook instance. type NotebookInstanceSummary struct { // The Amazon Resource Name (ARN) of the notebook instance. // // This member is required. NotebookInstanceArn *string // The name of the notebook instance that you want a summary for. // // This member is required. NotebookInstanceName *string // An array of up to three Git repositories associated with the notebook instance. // These can be either the names of Git repositories stored as resources in your // account, or the URL of Git repositories in Amazon Web Services CodeCommit (https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html) // or in any other Git repository. These repositories are cloned at the same level // as the default repository of your notebook instance. For more information, see // Associating Git Repositories with SageMaker Notebook Instances (https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html) // . AdditionalCodeRepositories []string // A timestamp that shows when the notebook instance was created. CreationTime *time.Time // The Git repository associated with the notebook instance as its default code // repository. This can be either the name of a Git repository stored as a resource // in your account, or the URL of a Git repository in Amazon Web Services // CodeCommit (https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html) // or in any other Git repository. When you open a notebook instance, it opens in // the directory that contains this repository. For more information, see // Associating Git Repositories with SageMaker Notebook Instances (https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html) // . DefaultCodeRepository *string // The type of ML compute instance that the notebook instance is running on. InstanceType InstanceType // A timestamp that shows when the notebook instance was last modified. LastModifiedTime *time.Time // The name of a notebook instance lifecycle configuration associated with this // notebook instance. For information about notebook instance lifestyle // configurations, see Step 2.1: (Optional) Customize a Notebook Instance (https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html) // . NotebookInstanceLifecycleConfigName *string // The status of the notebook instance. NotebookInstanceStatus NotebookInstanceStatus // The URL that you use to connect to the Jupyter notebook running in your // notebook instance. Url *string noSmithyDocumentSerde } // Configures Amazon SNS notifications of available or expiring work items for // work teams. type NotificationConfiguration struct { // The ARN for the Amazon SNS topic to which notifications should be published. NotificationTopicArn *string noSmithyDocumentSerde } // Specifies the number of training jobs that this hyperparameter tuning job // launched, categorized by the status of their objective metric. The objective // metric status shows whether the final objective metric for the training job has // been evaluated by the tuning job and used in the hyperparameter tuning process. type ObjectiveStatusCounters struct { // The number of training jobs whose final objective metric was not evaluated and // used in the hyperparameter tuning process. This typically occurs when the // training job failed or did not emit an objective metric. Failed int32 // The number of training jobs that are in progress and pending evaluation of // their final objective metric. Pending int32 // The number of training jobs whose final objective metric was evaluated by the // hyperparameter tuning job and used in the hyperparameter tuning process. Succeeded int32 noSmithyDocumentSerde } // The configuration of an OfflineStore . Provide an OfflineStoreConfig in a // request to CreateFeatureGroup to create an OfflineStore . To encrypt an // OfflineStore using at rest data encryption, specify Amazon Web Services Key // Management Service (KMS) key ID, or KMSKeyId , in S3StorageConfig . type OfflineStoreConfig struct { // The Amazon Simple Storage (Amazon S3) location of OfflineStore . // // This member is required. S3StorageConfig *S3StorageConfig // The meta data of the Glue table that is autogenerated when an OfflineStore is // created. DataCatalogConfig *DataCatalogConfig // Set to True to disable the automatic creation of an Amazon Web Services Glue // table when configuring an OfflineStore . If set to False , Feature Store will // name the OfflineStore Glue table following Athena's naming recommendations (https://docs.aws.amazon.com/athena/latest/ug/tables-databases-columns-names.html) // . The default value is False . DisableGlueTableCreation bool // Format for the offline store table. Supported formats are Glue (Default) and // Apache Iceberg (https://iceberg.apache.org/) . TableFormat TableFormat noSmithyDocumentSerde } // The status of OfflineStore . type OfflineStoreStatus struct { // An OfflineStore status. // // This member is required. Status OfflineStoreStatusValue // The justification for why the OfflineStoreStatus is Blocked (if applicable). BlockedReason *string noSmithyDocumentSerde } // Use this parameter to configure your OIDC Identity Provider (IdP). type OidcConfig struct { // The OIDC IdP authorization endpoint used to configure your private workforce. // // This member is required. AuthorizationEndpoint *string // The OIDC IdP client ID used to configure your private workforce. // // This member is required. ClientId *string // The OIDC IdP client secret used to configure your private workforce. // // This member is required. ClientSecret *string // The OIDC IdP issuer used to configure your private workforce. // // This member is required. Issuer *string // The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private // workforce. // // This member is required. JwksUri *string // The OIDC IdP logout endpoint used to configure your private workforce. // // This member is required. LogoutEndpoint *string // The OIDC IdP token endpoint used to configure your private workforce. // // This member is required. TokenEndpoint *string // The OIDC IdP user information endpoint used to configure your private workforce. // // This member is required. UserInfoEndpoint *string noSmithyDocumentSerde } // Your OIDC IdP workforce configuration. type OidcConfigForResponse struct { // The OIDC IdP authorization endpoint used to configure your private workforce. AuthorizationEndpoint *string // The OIDC IdP client ID used to configure your private workforce. ClientId *string // The OIDC IdP issuer used to configure your private workforce. Issuer *string // The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private // workforce. JwksUri *string // The OIDC IdP logout endpoint used to configure your private workforce. LogoutEndpoint *string // The OIDC IdP token endpoint used to configure your private workforce. TokenEndpoint *string // The OIDC IdP user information endpoint used to configure your private workforce. UserInfoEndpoint *string noSmithyDocumentSerde } // A list of user groups that exist in your OIDC Identity Provider (IdP). One to // ten groups can be used to create a single private work team. When you add a user // group to the list of Groups , you can add that user group to one or more private // work teams. If you add a user group to a private work team, all workers in that // user group are added to the work team. type OidcMemberDefinition struct { // A list of comma seperated strings that identifies user groups in your OIDC IdP. // Each user group is made up of a group of private workers. // // This member is required. Groups []string noSmithyDocumentSerde } // Use this to specify the Amazon Web Services Key Management Service (KMS) Key // ID, or KMSKeyId , for at rest data encryption. You can turn OnlineStore on or // off by specifying the EnableOnlineStore flag at General Assembly. The default // value is False . type OnlineStoreConfig struct { // Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn // OnlineStore on by specifying True for the EnableOnlineStore flag. The default // value is False . EnableOnlineStore bool // Use to specify KMS Key ID ( KMSKeyId ) for at-rest encryption of your // OnlineStore . SecurityConfig *OnlineStoreSecurityConfig // Time to live duration, where the record is hard deleted after the expiration // time is reached; ExpiresAt = EventTime + TtlDuration . For information on // HardDelete, see the DeleteRecord (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_feature_store_DeleteRecord.html) // API in the Amazon SageMaker API Reference guide. TtlDuration *TtlDuration noSmithyDocumentSerde } // Updates the feature group online store configuration. type OnlineStoreConfigUpdate struct { // Time to live duration, where the record is hard deleted after the expiration // time is reached; ExpiresAt = EventTime + TtlDuration . For information on // HardDelete, see the DeleteRecord (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_feature_store_DeleteRecord.html) // API in the Amazon SageMaker API Reference guide. TtlDuration *TtlDuration noSmithyDocumentSerde } // The security configuration for OnlineStore . type OnlineStoreSecurityConfig struct { // The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker // Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 // server-side encryption. The caller (either user or IAM role) of // CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId : // - "kms:Encrypt" // - "kms:Decrypt" // - "kms:DescribeKey" // - "kms:CreateGrant" // - "kms:RetireGrant" // - "kms:ReEncryptFrom" // - "kms:ReEncryptTo" // - "kms:GenerateDataKey" // - "kms:ListAliases" // - "kms:ListGrants" // - "kms:RevokeGrant" // The caller (either user or IAM role) to all DataPlane operations ( PutRecord , // GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId : // - "kms:Decrypt" KmsKeyId *string noSmithyDocumentSerde } // Contains information about the output location for the compiled model and the // target device that the model runs on. TargetDevice and TargetPlatform are // mutually exclusive, so you need to choose one between the two to specify your // target device or platform. If you cannot find your device you want to use from // the TargetDevice list, use TargetPlatform to describe the platform of your edge // device and CompilerOptions if there are specific settings that are required or // recommended to use for particular TargetPlatform. type OutputConfig struct { // Identifies the S3 bucket where you want Amazon SageMaker to store the model // artifacts. For example, s3://bucket-name/key-name-prefix . // // This member is required. S3OutputLocation *string // Specifies additional parameters for compiler options in JSON format. The // compiler options are TargetPlatform specific. It is required for NVIDIA // accelerators and highly recommended for CPU compilations. For any other cases, // it is optional to specify CompilerOptions. // - DTYPE : Specifies the data type for the input. When compiling for ml_* // (except for ml_inf ) instances using PyTorch framework, provide the data type // (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. // Options for data type are: // - float32: Use either "float" or "float32" . // - int64: Use either "int64" or "long" . For example, {"dtype" : "float32"} . // - CPU : Compilation for CPU supports the following compiler options. // - mcpu : CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'} // - mattr : CPU flags. For example, {'mattr': ['+neon', '+vfpv4']} // - ARM : Details of ARM CPU compilations. // - NEON : NEON is an implementation of the Advanced SIMD extension used in // ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler // options if compiling for ARM 32-bit platform with the NEON support. // - NVIDIA : Compilation for NVIDIA GPU supports the following compiler options. // - gpu_code : Specifies the targeted architecture. // - trt-ver : Specifies the TensorRT versions in x.y.z. format. // - cuda-ver : Specifies the CUDA version in x.y format. For example, // {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'} // - ANDROID : Compilation for the Android OS supports the following compiler // options: // - ANDROID_PLATFORM : Specifies the Android API levels. Available levels range // from 21 to 29. For example, {'ANDROID_PLATFORM': 28} . // - mattr : Add {'mattr': ['+neon']} to compiler options if compiling for ARM // 32-bit platform with NEON support. // - INFERENTIA : Compilation for target ml_inf1 uses compiler options passed in // as a JSON string. For example, "CompilerOptions": "\"--verbose 1 // --num-neuroncores 2 -O2\"" . For information about supported compiler options, // see Neuron Compiler CLI (https://github.com/aws/aws-neuron-sdk/blob/master/docs/neuron-cc/command-line-reference.md) // . // - CoreML : Compilation for the CoreML OutputConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html) // TargetDevice supports the following compiler options: // - class_labels : Specifies the classification labels file name inside input // tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"} . // Labels inside the txt file should be separated by newlines. // - EIA : Compilation for the Elastic Inference Accelerator supports the // following compiler options: // - precision_mode : Specifies the precision of compiled artifacts. Supported // values are "FP16" and "FP32" . Default is "FP32" . // - signature_def_key : Specifies the signature to use for models in SavedModel // format. Defaults is TensorFlow's default signature def key. // - output_names : Specifies a list of output tensor names for models in // FrozenGraph format. Set at most one API field, either: signature_def_key or // output_names . For example: {"precision_mode": "FP32", "output_names": // ["output:0"]} CompilerOptions *string // The Amazon Web Services Key Management Service key (Amazon Web Services KMS) // that Amazon SageMaker uses to encrypt your output models with Amazon S3 // server-side encryption after compilation job. If you don't provide a KMS key ID, // Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. // For more information, see KMS-Managed Encryption Keys (https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html) // in the Amazon Simple Storage Service Developer Guide. 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 // Identifies the target device or the machine learning instance that you want to // run your model on after the compilation has completed. Alternatively, you can // specify OS, architecture, and accelerator using TargetPlatform (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TargetPlatform.html) // fields. It can be used instead of TargetPlatform . Currently ml_trn1 is // available only in US East (N. Virginia) Region, and ml_inf2 is available only // in US East (Ohio) Region. TargetDevice TargetDevice // Contains information about a target platform that you want your model to run // on, such as OS, architecture, and accelerators. It is an alternative of // TargetDevice . The following examples show how to configure the TargetPlatform // and CompilerOptions JSON strings for popular target platforms: // - Raspberry Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": // "ARM_EABIHF"}, "CompilerOptions": {'mattr': ['+neon']} // - Jetson TX2 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", // "Accelerator": "NVIDIA"}, "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': // '6.0.1', 'cuda-ver': '10.0'} // - EC2 m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": // "X86_64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': // 'skylake-avx512'} // - RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": // "MALI"} // - ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}, // "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']} // - ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}, // "CompilerOptions": {'ANDROID_PLATFORM': 29} TargetPlatform *TargetPlatform noSmithyDocumentSerde } // Provides information about how to store model training results (model // artifacts). type OutputDataConfig struct { // Identifies the S3 path where you want SageMaker to store the model artifacts. // For example, s3://bucket-name/key-name-prefix . // // This member is required. S3OutputPath *string // The model output compression type. Select None to output an uncompressed model, // recommended for large model outputs. Defaults to gzip. CompressionType OutputCompressionType // The Amazon Web Services Key Management Service (Amazon Web Services KMS) key // that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 // server-side encryption. The KmsKeyId can be any of the following formats: // - // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab" // - // Amazon Resource Name (ARN) of a KMS Key // "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab" // - // KMS Key Alias "alias/ExampleAlias" // - // Amazon Resource Name (ARN) of a KMS Key Alias // "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias" // If you use a KMS key ID or an alias of your KMS key, the SageMaker execution // role must include permissions to call kms:Encrypt . If you don't provide a KMS // key ID, SageMaker uses the default KMS key for Amazon S3 for your role's // account. SageMaker uses server-side encryption with KMS-managed keys for // OutputDataConfig . If you use a bucket policy with an s3:PutObject permission // that only allows objects with server-side encryption, set the condition key of // s3:x-amz-server-side-encryption to "aws:kms" . For more information, see // KMS-Managed Encryption Keys (https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html) // in the Amazon Simple Storage Service Developer Guide. The KMS key policy must // grant permission to the IAM role that you specify in your CreateTrainingJob , // CreateTransformJob , or CreateHyperParameterTuningJob requests. For more // information, see Using Key Policies in Amazon Web Services KMS (https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html) // in the Amazon Web Services Key Management Service Developer Guide. KmsKeyId *string noSmithyDocumentSerde } // An output parameter of a pipeline step. type OutputParameter struct { // The name of the output parameter. // // This member is required. Name *string // The value of the output parameter. // // This member is required. Value *string noSmithyDocumentSerde } // Configuration that controls the parallelism of the pipeline. By default, the // parallelism configuration specified applies to all executions of the pipeline // unless overridden. type ParallelismConfiguration struct { // The max number of steps that can be executed in parallel. // // This member is required. MaxParallelExecutionSteps int32 noSmithyDocumentSerde } // Assigns a value to a named Pipeline parameter. type Parameter struct { // The name of the parameter to assign a value to. This parameter name must match // a named parameter in the pipeline definition. // // This member is required. Name *string // The literal value for the parameter. // // This member is required. Value *string noSmithyDocumentSerde } // Defines the possible values for categorical, continuous, and integer // hyperparameters to be used by an algorithm. type ParameterRange struct { // A CategoricalParameterRangeSpecification object that defines the possible // values for a categorical hyperparameter. CategoricalParameterRangeSpecification *CategoricalParameterRangeSpecification // A ContinuousParameterRangeSpecification object that defines the possible values // for a continuous hyperparameter. ContinuousParameterRangeSpecification *ContinuousParameterRangeSpecification // A IntegerParameterRangeSpecification object that defines the possible values // for an integer hyperparameter. IntegerParameterRangeSpecification *IntegerParameterRangeSpecification noSmithyDocumentSerde } // Specifies ranges of integer, continuous, and categorical hyperparameters that a // hyperparameter tuning job searches. The hyperparameter tuning job launches // training jobs with hyperparameter values within these ranges to find the // combination of values that result in the training job with the best performance // as measured by the objective metric of the hyperparameter tuning job. The // maximum number of items specified for Array Members refers to the maximum // number of hyperparameters for each range and also the maximum for the // hyperparameter tuning job itself. That is, the sum of the number of // hyperparameters for all the ranges can't exceed the maximum number specified. type ParameterRanges struct { // A list containing hyperparameter names and example values to be used by // Autotune to determine optimal ranges for your tuning job. AutoParameters []AutoParameter // The array of CategoricalParameterRange (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CategoricalParameterRange.html) // objects that specify ranges of categorical hyperparameters that a hyperparameter // tuning job searches. CategoricalParameterRanges []CategoricalParameterRange // The array of ContinuousParameterRange (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContinuousParameterRange.html) // objects that specify ranges of continuous hyperparameters that a hyperparameter // tuning job searches. ContinuousParameterRanges []ContinuousParameterRange // The array of IntegerParameterRange (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_IntegerParameterRange.html) // objects that specify ranges of integer hyperparameters that a hyperparameter // tuning job searches. IntegerParameterRanges []IntegerParameterRange noSmithyDocumentSerde } // The trial that a trial component is associated with and the experiment the // trial is part of. A component might not be associated with a trial. A component // can be associated with multiple trials. type Parent struct { // The name of the experiment. ExperimentName *string // The name of the trial. TrialName *string noSmithyDocumentSerde } // A previously completed or stopped hyperparameter tuning job to be used as a // starting point for a new hyperparameter tuning job. type ParentHyperParameterTuningJob struct { // The name of the hyperparameter tuning job to be used as a starting point for a // new hyperparameter tuning job. HyperParameterTuningJobName *string noSmithyDocumentSerde } // The summary of an in-progress deployment when an endpoint is creating or // updating with a new endpoint configuration. type PendingDeploymentSummary struct { // The name of the endpoint configuration used in the deployment. // // This member is required. EndpointConfigName *string // An array of PendingProductionVariantSummary (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_PendingProductionVariantSummary.html) // objects, one for each model hosted behind this endpoint for the in-progress // deployment. ProductionVariants []PendingProductionVariantSummary // An array of PendingProductionVariantSummary (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_PendingProductionVariantSummary.html) // objects, one for each model hosted behind this endpoint in shadow mode with // production traffic replicated from the model specified on ProductionVariants // for the in-progress deployment. ShadowProductionVariants []PendingProductionVariantSummary // The start time of the deployment. StartTime *time.Time noSmithyDocumentSerde } // The production variant summary for a deployment when an endpoint is creating or // updating with the CreateEndpoint (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html) // or UpdateEndpoint (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) // operations. Describes the VariantStatus , weight and capacity for a production // variant associated with an endpoint. type PendingProductionVariantSummary struct { // The name of the variant. // // This member is required. VariantName *string // The size of the Elastic Inference (EI) instance to use for the production // variant. EI instances provide on-demand GPU computing for inference. For more // information, see Using Elastic Inference in Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html) // . AcceleratorType ProductionVariantAcceleratorType // The number of instances associated with the variant. CurrentInstanceCount *int32 // The serverless configuration for the endpoint. CurrentServerlessConfig *ProductionVariantServerlessConfig // The weight associated with the variant. CurrentWeight *float32 // An array of DeployedImage objects that specify the Amazon EC2 Container // Registry paths of the inference images deployed on instances of this // ProductionVariant . DeployedImages []DeployedImage // The number of instances requested in this deployment, as specified in the // endpoint configuration for the endpoint. The value is taken from the request to // the CreateEndpointConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html) // operation. DesiredInstanceCount *int32 // The serverless configuration requested for this deployment, as specified in the // endpoint configuration for the endpoint. DesiredServerlessConfig *ProductionVariantServerlessConfig // The requested weight for the variant in this deployment, as specified in the // endpoint configuration for the endpoint. The value is taken from the request to // the CreateEndpointConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html) // operation. DesiredWeight *float32 // The type of instances associated with the variant. InstanceType ProductionVariantInstanceType // The endpoint variant status which describes the current deployment stage status // or operational status. VariantStatus []ProductionVariantStatus noSmithyDocumentSerde } // Defines the traffic pattern. type Phase struct { // Specifies how long traffic phase should be. DurationInSeconds *int32 // Specifies how many concurrent users to start with. InitialNumberOfUsers *int32 // Specified how many new users to spawn in a minute. SpawnRate *int32 noSmithyDocumentSerde } // A SageMaker Model Building Pipeline instance. type Pipeline struct { // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. CreatedBy *UserContext // The creation time of the pipeline. CreationTime *time.Time // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. LastModifiedBy *UserContext // The time that the pipeline was last modified. LastModifiedTime *time.Time // The time when the pipeline was last run. LastRunTime *time.Time // The parallelism configuration applied to the pipeline. ParallelismConfiguration *ParallelismConfiguration // The Amazon Resource Name (ARN) of the pipeline. PipelineArn *string // The description of the pipeline. PipelineDescription *string // The display name of the pipeline. PipelineDisplayName *string // The name of the pipeline. PipelineName *string // The status of the pipeline. PipelineStatus PipelineStatus // The Amazon Resource Name (ARN) of the role that created the pipeline. RoleArn *string // A list of tags that apply to the pipeline. Tags []Tag noSmithyDocumentSerde } // The location of the pipeline definition stored in Amazon S3. type PipelineDefinitionS3Location struct { // Name of the S3 bucket. // // This member is required. Bucket *string // The object key (or key name) uniquely identifies the object in an S3 bucket. // // This member is required. ObjectKey *string // Version Id of the pipeline definition file. If not specified, Amazon SageMaker // will retrieve the latest version. VersionId *string noSmithyDocumentSerde } // An execution of a pipeline. type PipelineExecution struct { // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. CreatedBy *UserContext // The creation time of the pipeline execution. CreationTime *time.Time // If the execution failed, a message describing why. FailureReason *string // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. LastModifiedBy *UserContext // The time that the pipeline execution was last modified. LastModifiedTime *time.Time // The parallelism configuration applied to the pipeline execution. ParallelismConfiguration *ParallelismConfiguration // The Amazon Resource Name (ARN) of the pipeline that was executed. PipelineArn *string // The Amazon Resource Name (ARN) of the pipeline execution. PipelineExecutionArn *string // The description of the pipeline execution. PipelineExecutionDescription *string // The display name of the pipeline execution. PipelineExecutionDisplayName *string // The status of the pipeline status. PipelineExecutionStatus PipelineExecutionStatus // Specifies the names of the experiment and trial created by a pipeline. PipelineExperimentConfig *PipelineExperimentConfig // Contains a list of pipeline parameters. This list can be empty. PipelineParameters []Parameter // The selective execution configuration applied to the pipeline run. SelectiveExecutionConfig *SelectiveExecutionConfig noSmithyDocumentSerde } // An execution of a step in a pipeline. type PipelineExecutionStep struct { // The current attempt of the execution step. For more information, see Retry // Policy for SageMaker Pipelines steps (https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-retry-policy.html) // . AttemptCount int32 // If this pipeline execution step was cached, details on the cache hit. CacheHitResult *CacheHitResult // The time that the step stopped executing. EndTime *time.Time // The reason why the step failed execution. This is only returned if the step // failed its execution. FailureReason *string // Metadata to run the pipeline step. Metadata *PipelineExecutionStepMetadata // The ARN from an execution of the current pipeline from which results are reused // for this step. SelectiveExecutionResult *SelectiveExecutionResult // The time that the step started executing. StartTime *time.Time // The description of the step. StepDescription *string // The display name of the step. StepDisplayName *string // The name of the step that is executed. StepName *string // The status of the step execution. StepStatus StepStatus noSmithyDocumentSerde } // Metadata for a step execution. type PipelineExecutionStepMetadata struct { // The Amazon Resource Name (ARN) of the AutoML job that was run by this step. AutoMLJob *AutoMLJobStepMetadata // The URL of the Amazon SQS queue used by this step execution, the pipeline // generated token, and a list of output parameters. Callback *CallbackStepMetadata // Container for the metadata for a Clarify check step. The configurations and // outcomes of the check step execution. This includes: // - The type of the check conducted, // - The Amazon S3 URIs of baseline constraints and statistics files to be used // for the drift check. // - The Amazon S3 URIs of newly calculated baseline constraints and statistics. // - The model package group name provided. // - The Amazon S3 URI of the violation report if violations detected. // - The Amazon Resource Name (ARN) of check processing job initiated by the // step execution. // - The boolean flags indicating if the drift check is skipped. // - If step property BaselineUsedForDriftCheck is set the same as // CalculatedBaseline . ClarifyCheck *ClarifyCheckStepMetadata // The outcome of the condition evaluation that was run by this step execution. Condition *ConditionStepMetadata // The configurations and outcomes of an Amazon EMR step execution. EMR *EMRStepMetadata // The configurations and outcomes of a Fail step execution. Fail *FailStepMetadata // The Amazon Resource Name (ARN) of the Lambda function that was run by this step // execution and a list of output parameters. Lambda *LambdaStepMetadata // The Amazon Resource Name (ARN) of the model that was created by this step // execution. Model *ModelStepMetadata // The Amazon Resource Name (ARN) of the processing job that was run by this step // execution. ProcessingJob *ProcessingJobStepMetadata // The configurations and outcomes of the check step execution. This includes: // - The type of the check conducted. // - The Amazon S3 URIs of baseline constraints and statistics files to be used // for the drift check. // - The Amazon S3 URIs of newly calculated baseline constraints and statistics. // - The model package group name provided. // - The Amazon S3 URI of the violation report if violations detected. // - The Amazon Resource Name (ARN) of check processing job initiated by the // step execution. // - The Boolean flags indicating if the drift check is skipped. // - If step property BaselineUsedForDriftCheck is set the same as // CalculatedBaseline . QualityCheck *QualityCheckStepMetadata // The Amazon Resource Name (ARN) of the model package that the model was // registered to by this step execution. RegisterModel *RegisterModelStepMetadata // The Amazon Resource Name (ARN) of the training job that was run by this step // execution. TrainingJob *TrainingJobStepMetadata // The Amazon Resource Name (ARN) of the transform job that was run by this step // execution. TransformJob *TransformJobStepMetadata // The Amazon Resource Name (ARN) of the tuning job that was run by this step // execution. TuningJob *TuningJobStepMetaData noSmithyDocumentSerde } // A pipeline execution summary. type PipelineExecutionSummary struct { // The Amazon Resource Name (ARN) of the pipeline execution. PipelineExecutionArn *string // The description of the pipeline execution. PipelineExecutionDescription *string // The display name of the pipeline execution. PipelineExecutionDisplayName *string // A message generated by SageMaker Pipelines describing why the pipeline // execution failed. PipelineExecutionFailureReason *string // The status of the pipeline execution. PipelineExecutionStatus PipelineExecutionStatus // The start time of the pipeline execution. StartTime *time.Time noSmithyDocumentSerde } // Specifies the names of the experiment and trial created by a pipeline. type PipelineExperimentConfig struct { // The name of the experiment. ExperimentName *string // The name of the trial. TrialName *string noSmithyDocumentSerde } // A summary of a pipeline. type PipelineSummary struct { // The creation time of the pipeline. CreationTime *time.Time // The last time that a pipeline execution began. LastExecutionTime *time.Time // The time that the pipeline was last modified. LastModifiedTime *time.Time // The Amazon Resource Name (ARN) of the pipeline. PipelineArn *string // The description of the pipeline. PipelineDescription *string // The display name of the pipeline. PipelineDisplayName *string // The name of the pipeline. PipelineName *string // The Amazon Resource Name (ARN) that the pipeline used to execute. RoleArn *string noSmithyDocumentSerde } // Configuration for the cluster used to run a processing job. type ProcessingClusterConfig struct { // The number of ML compute instances to use in the processing job. For // distributed processing jobs, specify a value greater than 1. The default value // is 1. // // This member is required. InstanceCount *int32 // The ML compute instance type for the processing job. // // This member is required. InstanceType ProcessingInstanceType // The size of the ML storage volume in gigabytes that you want to provision. You // must specify sufficient ML storage for your scenario. Certain Nitro-based // instances include local storage with a fixed total size, dependent on the // instance type. When using these instances for processing, Amazon SageMaker // mounts the local instance storage instead of Amazon EBS gp2 storage. You can't // request a VolumeSizeInGB greater than the total size of the local instance // storage. For a list of instance types that support local instance storage, // including the total size per instance type, see Instance Store Volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes) // . // // This member is required. VolumeSizeInGB *int32 // The Amazon Web Services Key Management Service (Amazon Web Services KMS) key // that Amazon SageMaker uses to encrypt data on the storage volume attached to the // ML compute instance(s) that run the processing job. Certain Nitro-based // instances include local storage, dependent on the instance type. Local storage // volumes are encrypted using a hardware module on the instance. You can't request // a VolumeKmsKeyId when using an instance type with local storage. For a list of // instance types that support local instance storage, see Instance Store Volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes) // . For more information about local instance storage encryption, see SSD // Instance Store Volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html) // . VolumeKmsKeyId *string noSmithyDocumentSerde } // Configuration for processing job outputs in Amazon SageMaker Feature Store. type ProcessingFeatureStoreOutput struct { // The name of the Amazon SageMaker FeatureGroup to use as the destination for // processing job output. Note that your processing script is responsible for // putting records into your Feature Store. // // This member is required. FeatureGroupName *string noSmithyDocumentSerde } // The inputs for a processing job. The processing input must specify exactly one // of either S3Input or DatasetDefinition types. type ProcessingInput struct { // The name for the processing job input. // // This member is required. InputName *string // When True , input operations such as data download are managed natively by the // processing job application. When False (default), input operations are managed // by Amazon SageMaker. AppManaged bool // Configuration for a Dataset Definition input. DatasetDefinition *DatasetDefinition // Configuration for downloading input data from Amazon S3 into the processing // container. S3Input *ProcessingS3Input noSmithyDocumentSerde } // An Amazon SageMaker processing job that is used to analyze data and evaluate // models. For more information, see Process Data and Evaluate Models (https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html) // . type ProcessingJob struct { // Configuration to run a processing job in a specified container image. AppSpecification *AppSpecification // The Amazon Resource Name (ARN) of the AutoML job associated with this // processing job. AutoMLJobArn *string // The time the processing job was created. CreationTime *time.Time // Sets the environment variables in the Docker container. Environment map[string]string // A string, up to one KB in size, that contains metadata from the processing // container when the processing job exits. ExitMessage *string // Associates a SageMaker job as a trial component with an experiment and trial. // Specified when you call the following APIs: // - CreateProcessingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html) // - CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) // - CreateTransformJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html) ExperimentConfig *ExperimentConfig // A string, up to one KB in size, that contains the reason a processing job // failed, if it failed. FailureReason *string // The time the processing job was last modified. LastModifiedTime *time.Time // The ARN of a monitoring schedule for an endpoint associated with this // processing job. MonitoringScheduleArn *string // Networking options for a job, such as network traffic encryption between // containers, whether to allow inbound and outbound network calls to and from // containers, and the VPC subnets and security groups to use for VPC-enabled jobs. NetworkConfig *NetworkConfig // The time that the processing job ended. ProcessingEndTime *time.Time // List of input configurations for the processing job. ProcessingInputs []ProcessingInput // The ARN of the processing job. ProcessingJobArn *string // The name of the processing job. ProcessingJobName *string // The status of the processing job. ProcessingJobStatus ProcessingJobStatus // Configuration for uploading output from the processing container. ProcessingOutputConfig *ProcessingOutputConfig // Identifies the resources, ML compute instances, and ML storage volumes to // deploy for a processing job. In distributed training, you specify more than one // instance. ProcessingResources *ProcessingResources // The time that the processing job started. ProcessingStartTime *time.Time // The ARN of the role used to create the processing job. RoleArn *string // Configures conditions under which the processing job should be stopped, such as // how long the processing job has been running. After the condition is met, the // processing job is stopped. StoppingCondition *ProcessingStoppingCondition // An array of key-value pairs. For more information, see Using Cost Allocation // Tags (https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL) // in the Amazon Web Services Billing and Cost Management User Guide. Tags []Tag // The ARN of the training job associated with this processing job. TrainingJobArn *string noSmithyDocumentSerde } // Metadata for a processing job step. type ProcessingJobStepMetadata struct { // The Amazon Resource Name (ARN) of the processing job. Arn *string noSmithyDocumentSerde } // Summary of information about a processing job. type ProcessingJobSummary struct { // The time at which the processing job was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the processing job.. // // This member is required. ProcessingJobArn *string // The name of the processing job. // // This member is required. ProcessingJobName *string // The status of the processing job. // // This member is required. ProcessingJobStatus ProcessingJobStatus // An optional string, up to one KB in size, that contains metadata from the // processing container when the processing job exits. ExitMessage *string // A string, up to one KB in size, that contains the reason a processing job // failed, if it failed. FailureReason *string // A timestamp that indicates the last time the processing job was modified. LastModifiedTime *time.Time // The time at which the processing job completed. ProcessingEndTime *time.Time noSmithyDocumentSerde } // Describes the results of a processing job. The processing output must specify // exactly one of either S3Output or FeatureStoreOutput types. type ProcessingOutput struct { // The name for the processing job output. // // This member is required. OutputName *string // When True , output operations such as data upload are managed natively by the // processing job application. When False (default), output operations are managed // by Amazon SageMaker. AppManaged bool // Configuration for processing job outputs in Amazon SageMaker Feature Store. // This processing output type is only supported when AppManaged is specified. FeatureStoreOutput *ProcessingFeatureStoreOutput // Configuration for processing job outputs in Amazon S3. S3Output *ProcessingS3Output noSmithyDocumentSerde } // Configuration for uploading output from the processing container. type ProcessingOutputConfig struct { // An array of outputs configuring the data to upload from the processing // container. // // This member is required. Outputs []ProcessingOutput // The Amazon Web Services Key Management Service (Amazon Web Services KMS) key // that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can // be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS // key. The KmsKeyId is applied to all outputs. KmsKeyId *string noSmithyDocumentSerde } // Identifies the resources, ML compute instances, and ML storage volumes to // deploy for a processing job. In distributed training, you specify more than one // instance. type ProcessingResources struct { // The configuration for the resources in a cluster used to run the processing job. // // This member is required. ClusterConfig *ProcessingClusterConfig noSmithyDocumentSerde } // Configuration for downloading input data from Amazon S3 into the processing // container. type ProcessingS3Input struct { // Whether you use an S3Prefix or a ManifestFile for the data type. If you choose // S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all // objects with the specified key name prefix for the processing job. If you choose // ManifestFile , S3Uri identifies an object that is a manifest file containing a // list of object keys that you want Amazon SageMaker to use for the processing // job. // // This member is required. S3DataType ProcessingS3DataType // The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run // a processing job. // // This member is required. S3Uri *string // The local path in your container where you want Amazon SageMaker to write input // data to. LocalPath is an absolute path to the input data and must begin with // /opt/ml/processing/ . LocalPath is a required parameter when AppManaged is False // (default). LocalPath *string // Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the // processing container. Gzip can only be used when Pipe mode is specified as the // S3InputMode . In Pipe mode, Amazon SageMaker streams input data from the source // directly to your container without using the EBS volume. S3CompressionType ProcessingS3CompressionType // Whether to distribute the data from Amazon S3 to all processing instances with // FullyReplicated , or whether the data from Amazon S3 is shared by Amazon S3 key, // downloading one shard of data to each processing instance. S3DataDistributionType ProcessingS3DataDistributionType // Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies // the data from the input source onto the local ML storage volume before starting // your processing container. This is the most commonly used input mode. In Pipe // mode, Amazon SageMaker streams input data from the source directly to your // processing container into named pipes without using the ML storage volume. S3InputMode ProcessingS3InputMode noSmithyDocumentSerde } // Configuration for uploading output data to Amazon S3 from the processing // container. type ProcessingS3Output struct { // The local path of a directory where you want Amazon SageMaker to upload its // contents to Amazon S3. LocalPath is an absolute path to a directory containing // output files. This directory will be created by the platform and exist when your // container's entrypoint is invoked. // // This member is required. LocalPath *string // Whether to upload the results of the processing job continuously or after the // job completes. // // This member is required. S3UploadMode ProcessingS3UploadMode // A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to // save the results of a processing job. // // This member is required. S3Uri *string noSmithyDocumentSerde } // Configures conditions under which the processing job should be stopped, such as // how long the processing job has been running. After the condition is met, the // processing job is stopped. type ProcessingStoppingCondition struct { // Specifies the maximum runtime in seconds. // // This member is required. MaxRuntimeInSeconds int32 noSmithyDocumentSerde } // Identifies a model that you want to host and the resources chosen to deploy for // hosting it. If you are deploying multiple models, tell SageMaker how to // distribute traffic among the models by specifying variant weights. For more // information on production variants, check Production variants (https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html) // . type ProductionVariant struct { // The name of the model that you want to host. This is the name that you // specified when creating the model. // // This member is required. ModelName *string // The name of the production variant. // // This member is required. VariantName *string // The size of the Elastic Inference (EI) instance to use for the production // variant. EI instances provide on-demand GPU computing for inference. For more // information, see Using Elastic Inference in Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html) // . AcceleratorType ProductionVariantAcceleratorType // The timeout value, in seconds, for your inference container to pass health // check by SageMaker Hosting. For more information about health check, see How // Your Container Should Respond to Health Check (Ping) Requests (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests) // . ContainerStartupHealthCheckTimeoutInSeconds *int32 // Specifies configuration for a core dump from the model container when the // process crashes. CoreDumpConfig *ProductionVariantCoreDumpConfig // You can use this parameter to turn on native Amazon Web Services Systems // Manager (SSM) access for a production variant behind an endpoint. By default, // SSM access is disabled for all production variants behind an endpoint. You can // turn on or turn off SSM access for a production variant behind an existing // endpoint by creating a new endpoint configuration and calling UpdateEndpoint . EnableSSMAccess *bool // Number of instances to launch initially. InitialInstanceCount *int32 // Determines initial traffic distribution among all of the models that you // specify in the endpoint configuration. The traffic to a production variant is // determined by the ratio of the VariantWeight to the sum of all VariantWeight // values across all ProductionVariants. If unspecified, it defaults to 1.0. InitialVariantWeight *float32 // The ML compute instance type. InstanceType ProductionVariantInstanceType // The timeout value, in seconds, to download and extract the model that you want // to host from Amazon S3 to the individual inference instance associated with this // production variant. ModelDataDownloadTimeoutInSeconds *int32 // The serverless configuration for an endpoint. Specifies a serverless endpoint // configuration instead of an instance-based endpoint configuration. ServerlessConfig *ProductionVariantServerlessConfig // The size, in GB, of the ML storage volume attached to individual inference // instance associated with the production variant. Currently only Amazon EBS gp2 // storage volumes are supported. VolumeSizeInGB *int32 noSmithyDocumentSerde } // Specifies configuration for a core dump from the model container when the // process crashes. type ProductionVariantCoreDumpConfig struct { // The Amazon S3 bucket to send the core dump to. // // This member is required. DestinationS3Uri *string // The Amazon Web Services Key Management Service (Amazon Web Services KMS) key // that SageMaker uses to encrypt the core dump data at rest using Amazon S3 // server-side encryption. The KmsKeyId can be any of the following formats: // - // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab" // - // Amazon Resource Name (ARN) of a KMS Key // "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab" // - // KMS Key Alias "alias/ExampleAlias" // - // Amazon Resource Name (ARN) of a KMS Key Alias // "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias" // If you use a KMS key ID or an alias of your KMS key, the SageMaker execution // role must include permissions to call kms:Encrypt . If you don't provide a KMS // key ID, SageMaker uses the default KMS key for Amazon S3 for your role's // account. SageMaker uses server-side encryption with KMS-managed keys for // OutputDataConfig . If you use a bucket policy with an s3:PutObject permission // that only allows objects with server-side encryption, set the condition key of // s3:x-amz-server-side-encryption to "aws:kms" . For more information, see // KMS-Managed Encryption Keys (https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html) // in the Amazon Simple Storage Service Developer Guide. The KMS key policy must // grant permission to the IAM role that you specify in your CreateEndpoint and // UpdateEndpoint requests. For more information, see Using Key Policies in Amazon // Web Services KMS (https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html) // in the Amazon Web Services Key Management Service Developer Guide. KmsKeyId *string noSmithyDocumentSerde } // Specifies the serverless configuration for an endpoint variant. type ProductionVariantServerlessConfig struct { // The maximum number of concurrent invocations your serverless endpoint can // process. // // This member is required. MaxConcurrency *int32 // The memory size of your serverless endpoint. Valid values are in 1 GB // increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB. // // This member is required. MemorySizeInMB *int32 // The amount of provisioned concurrency to allocate for the serverless endpoint. // Should be less than or equal to MaxConcurrency . This field is not supported for // serverless endpoint recommendations for Inference Recommender jobs. For more // information about creating an Inference Recommender job, see // CreateInferenceRecommendationsJobs (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateInferenceRecommendationsJob.html) // . ProvisionedConcurrency *int32 noSmithyDocumentSerde } // Specifies the serverless update concurrency configuration for an endpoint // variant. type ProductionVariantServerlessUpdateConfig struct { // The updated maximum number of concurrent invocations your serverless endpoint // can process. MaxConcurrency *int32 // The updated amount of provisioned concurrency to allocate for the serverless // endpoint. Should be less than or equal to MaxConcurrency . ProvisionedConcurrency *int32 noSmithyDocumentSerde } // Describes the status of the production variant. type ProductionVariantStatus struct { // The endpoint variant status which describes the current deployment stage status // or operational status. // - Creating : Creating inference resources for the production variant. // - Deleting : Terminating inference resources for the production variant. // - Updating : Updating capacity for the production variant. // - ActivatingTraffic : Turning on traffic for the production variant. // - Baking : Waiting period to monitor the CloudWatch alarms in the automatic // rollback configuration. // // This member is required. Status VariantStatus // The start time of the current status change. StartTime *time.Time // A message that describes the status of the production variant. StatusMessage *string noSmithyDocumentSerde } // Describes weight and capacities for a production variant associated with an // endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API // and the endpoint status is Updating , you get different desired and current // values. type ProductionVariantSummary struct { // The name of the variant. // // This member is required. VariantName *string // The number of instances associated with the variant. CurrentInstanceCount *int32 // The serverless configuration for the endpoint. CurrentServerlessConfig *ProductionVariantServerlessConfig // The weight associated with the variant. CurrentWeight *float32 // An array of DeployedImage objects that specify the Amazon EC2 Container // Registry paths of the inference images deployed on instances of this // ProductionVariant . DeployedImages []DeployedImage // The number of instances requested in the UpdateEndpointWeightsAndCapacities // request. DesiredInstanceCount *int32 // The serverless configuration requested for the endpoint update. DesiredServerlessConfig *ProductionVariantServerlessConfig // The requested weight, as specified in the UpdateEndpointWeightsAndCapacities // request. DesiredWeight *float32 // The endpoint variant status which describes the current deployment stage status // or operational status. VariantStatus []ProductionVariantStatus noSmithyDocumentSerde } // Configuration information for Amazon SageMaker Debugger system monitoring, // framework profiling, and storage paths. type ProfilerConfig struct { // Configuration to turn off Amazon SageMaker Debugger's system monitoring and // profiling functionality. To turn it off, set to True . DisableProfiler bool // A time interval for capturing system metrics in milliseconds. Available values // are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) // milliseconds. The default value is 500 milliseconds. ProfilingIntervalInMilliseconds *int64 // Configuration information for capturing framework metrics. Available key // strings for different profiling options are DetailedProfilingConfig , // PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are // configuration structures for the ProfilingParameters parameter. To learn more // about how to configure the ProfilingParameters parameter, see Use the SageMaker // and Debugger Configuration API Operations to Create, Update, and Debug Your // Training Job (https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html) // . ProfilingParameters map[string]string // Path to Amazon S3 storage location for system and framework metrics. S3OutputPath *string noSmithyDocumentSerde } // Configuration information for updating the Amazon SageMaker Debugger profile // parameters, system and framework metrics configurations, and storage paths. type ProfilerConfigForUpdate struct { // To turn off Amazon SageMaker Debugger monitoring and profiling while a training // job is in progress, set to True . DisableProfiler bool // A time interval for capturing system metrics in milliseconds. Available values // are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) // milliseconds. The default value is 500 milliseconds. ProfilingIntervalInMilliseconds *int64 // Configuration information for capturing framework metrics. Available key // strings for different profiling options are DetailedProfilingConfig , // PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are // configuration structures for the ProfilingParameters parameter. To learn more // about how to configure the ProfilingParameters parameter, see Use the SageMaker // and Debugger Configuration API Operations to Create, Update, and Debug Your // Training Job (https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html) // . ProfilingParameters map[string]string // Path to Amazon S3 storage location for system and framework metrics. S3OutputPath *string noSmithyDocumentSerde } // Configuration information for profiling rules. type ProfilerRuleConfiguration struct { // The name of the rule configuration. It must be unique relative to other rule // configuration names. // // This member is required. RuleConfigurationName *string // The Amazon Elastic Container Registry Image for the managed rule evaluation. // // This member is required. RuleEvaluatorImage *string // The instance type to deploy a custom rule for profiling a training job. InstanceType ProcessingInstanceType // Path to local storage location for output of rules. Defaults to // /opt/ml/processing/output/rule/ . LocalPath *string // Runtime configuration for rule container. RuleParameters map[string]string // Path to Amazon S3 storage location for rules. S3OutputPath *string // The size, in GB, of the ML storage volume attached to the processing instance. VolumeSizeInGB int32 noSmithyDocumentSerde } // Information about the status of the rule evaluation. type ProfilerRuleEvaluationStatus struct { // Timestamp when the rule evaluation status was last modified. LastModifiedTime *time.Time // The name of the rule configuration. RuleConfigurationName *string // The Amazon Resource Name (ARN) of the rule evaluation job. RuleEvaluationJobArn *string // Status of the rule evaluation. RuleEvaluationStatus RuleEvaluationStatus // Details from the rule evaluation. StatusDetails *string noSmithyDocumentSerde } // The properties of a project as returned by the Search API. type Project struct { // Who created the project. CreatedBy *UserContext // A timestamp specifying when the project was created. CreationTime *time.Time // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. LastModifiedBy *UserContext // A timestamp container for when the project was last modified. LastModifiedTime *time.Time // The Amazon Resource Name (ARN) of the project. ProjectArn *string // The description of the project. ProjectDescription *string // The ID of the project. ProjectId *string // The name of the project. ProjectName *string // The status of the project. ProjectStatus ProjectStatus // Details of a provisioned service catalog product. For information about service // catalog, see What is Amazon Web Services Service Catalog (https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html) // . ServiceCatalogProvisionedProductDetails *ServiceCatalogProvisionedProductDetails // Details that you specify to provision a service catalog product. For // information about service catalog, see What is Amazon Web Services Service // Catalog (https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html) // . ServiceCatalogProvisioningDetails *ServiceCatalogProvisioningDetails // An array of key-value pairs. You can use tags to categorize your Amazon Web // Services resources in different ways, for example, by purpose, owner, or // environment. For more information, see Tagging Amazon Web Services Resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html) // . Tags []Tag noSmithyDocumentSerde } // Information about a project. type ProjectSummary struct { // The time that the project was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the project. // // This member is required. ProjectArn *string // The ID of the project. // // This member is required. ProjectId *string // The name of the project. // // This member is required. ProjectName *string // The status of the project. // // This member is required. ProjectStatus ProjectStatus // The description of the project. ProjectDescription *string noSmithyDocumentSerde } // Part of the SuggestionQuery type. Specifies a hint for retrieving property // names that begin with the specified text. type PropertyNameQuery struct { // Text that begins a property's name. // // This member is required. PropertyNameHint *string noSmithyDocumentSerde } // A property name returned from a GetSearchSuggestions call that specifies a // value in the PropertyNameQuery field. type PropertyNameSuggestion struct { // A suggested property name based on what you entered in the search textbox in // the SageMaker console. PropertyName *string noSmithyDocumentSerde } // A key value pair used when you provision a project as a service catalog // product. For information, see What is Amazon Web Services Service Catalog (https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html) // . type ProvisioningParameter struct { // The key that identifies a provisioning parameter. Key *string // The value of the provisioning parameter. Value *string noSmithyDocumentSerde } // Defines the amount of money paid to an Amazon Mechanical Turk worker for each // task performed. Use one of the following prices for bounding box tasks. Prices // are in US dollars and should be based on the complexity of the task; the longer // it takes in your initial testing, the more you should offer. // - 0.036 // - 0.048 // - 0.060 // - 0.072 // - 0.120 // - 0.240 // - 0.360 // - 0.480 // - 0.600 // - 0.720 // - 0.840 // - 0.960 // - 1.080 // - 1.200 // // Use one of the following prices for image classification, text classification, // and custom tasks. Prices are in US dollars. // - 0.012 // - 0.024 // - 0.036 // - 0.048 // - 0.060 // - 0.072 // - 0.120 // - 0.240 // - 0.360 // - 0.480 // - 0.600 // - 0.720 // - 0.840 // - 0.960 // - 1.080 // - 1.200 // // Use one of the following prices for semantic segmentation tasks. Prices are in // US dollars. // - 0.840 // - 0.960 // - 1.080 // - 1.200 // // Use one of the following prices for Textract AnalyzeDocument Important Form Key // Amazon Augmented AI review tasks. Prices are in US dollars. // - 2.400 // - 2.280 // - 2.160 // - 2.040 // - 1.920 // - 1.800 // - 1.680 // - 1.560 // - 1.440 // - 1.320 // - 1.200 // - 1.080 // - 0.960 // - 0.840 // - 0.720 // - 0.600 // - 0.480 // - 0.360 // - 0.240 // - 0.120 // - 0.072 // - 0.060 // - 0.048 // - 0.036 // - 0.024 // - 0.012 // // Use one of the following prices for Rekognition DetectModerationLabels Amazon // Augmented AI review tasks. Prices are in US dollars. // - 1.200 // - 1.080 // - 0.960 // - 0.840 // - 0.720 // - 0.600 // - 0.480 // - 0.360 // - 0.240 // - 0.120 // - 0.072 // - 0.060 // - 0.048 // - 0.036 // - 0.024 // - 0.012 // // Use one of the following prices for Amazon Augmented AI custom human review // tasks. Prices are in US dollars. // - 1.200 // - 1.080 // - 0.960 // - 0.840 // - 0.720 // - 0.600 // - 0.480 // - 0.360 // - 0.240 // - 0.120 // - 0.072 // - 0.060 // - 0.048 // - 0.036 // - 0.024 // - 0.012 type PublicWorkforceTaskPrice struct { // Defines the amount of money paid to an Amazon Mechanical Turk worker in United // States dollars. AmountInUsd *USD noSmithyDocumentSerde } // Container for the metadata for a Quality check step. For more information, see // the topic on QualityCheck step (https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#step-type-quality-check) // in the Amazon SageMaker Developer Guide. type QualityCheckStepMetadata struct { // The Amazon S3 URI of the baseline constraints file used for the drift check. BaselineUsedForDriftCheckConstraints *string // The Amazon S3 URI of the baseline statistics file used for the drift check. BaselineUsedForDriftCheckStatistics *string // The Amazon S3 URI of the newly calculated baseline constraints file. CalculatedBaselineConstraints *string // The Amazon S3 URI of the newly calculated baseline statistics file. CalculatedBaselineStatistics *string // The Amazon Resource Name (ARN) of the Quality check processing job that was run // by this step execution. CheckJobArn *string // The type of the Quality 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 and // BaselineUsedForDriftCheckStatistics properties. 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 violation report if violations are detected. ViolationReport *string noSmithyDocumentSerde } // A set of filters to narrow the set of lineage entities connected to the StartArn // (s) returned by the QueryLineage API action. type QueryFilters struct { // Filter the lineage entities connected to the StartArn (s) after the create date. CreatedAfter *time.Time // Filter the lineage entities connected to the StartArn (s) by created date. CreatedBefore *time.Time // Filter the lineage entities connected to the StartArn (s) by the type of the // lineage entity. LineageTypes []LineageType // Filter the lineage entities connected to the StartArn (s) after the last // modified date. ModifiedAfter *time.Time // Filter the lineage entities connected to the StartArn (s) before the last // modified date. ModifiedBefore *time.Time // Filter the lineage entities connected to the StartArn (s) by a set if property // key value pairs. If multiple pairs are provided, an entity is included in the // results if it matches any of the provided pairs. Properties map[string]string // Filter the lineage entities connected to the StartArn by type. For example: // DataSet , Model , Endpoint , or ModelDeployment . Types []string noSmithyDocumentSerde } // The infrastructure configuration for deploying the model to a real-time // inference endpoint. type RealTimeInferenceConfig struct { // The number of instances of the type specified by InstanceType . // // This member is required. InstanceCount *int32 // The instance type the model is deployed to. // // This member is required. InstanceType InstanceType noSmithyDocumentSerde } // The recommended configuration to use for Real-Time Inference. type RealTimeInferenceRecommendation struct { // The recommended instance type for Real-Time Inference. // // This member is required. InstanceType ProductionVariantInstanceType // The recommendation ID which uniquely identifies each recommendation. // // This member is required. RecommendationId *string // The recommended environment variables to set in the model container for // Real-Time Inference. Environment map[string]string noSmithyDocumentSerde } // Provides information about the output configuration for the compiled model. type RecommendationJobCompiledOutputConfig struct { // Identifies the Amazon S3 bucket where you want SageMaker to store the compiled // model artifacts. S3OutputUri *string noSmithyDocumentSerde } // Specifies mandatory fields for running an Inference Recommender job directly in // the CreateInferenceRecommendationsJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateInferenceRecommendationsJob.html) // API. The fields specified in ContainerConfig override the corresponding fields // in the model package. Use ContainerConfig if you want to specify these fields // for the recommendation job but don't want to edit them in your model package. type RecommendationJobContainerConfig struct { // Specifies the name and shape of the expected data inputs for your trained model // with a JSON dictionary form. This field is used for optimizing your model using // SageMaker Neo. For more information, see DataInputConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_InputConfig.html#sagemaker-Type-InputConfig-DataInputConfig) // . DataInputConfig *string // The machine learning domain of the model and its components. Valid Values: // COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING Domain *string // The machine learning framework of the container image. Valid Values: TENSORFLOW // | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN Framework *string // The framework version of the container image. FrameworkVersion *string // The name of a pre-trained machine learning model benchmarked by Amazon // SageMaker Inference Recommender that matches your model. Valid Values: // efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | // inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | // resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased // | xceptionV1-keras | resnet50 | retinanet NearestModelName *string // Specifies the SamplePayloadUrl and all other sample payload-related fields. PayloadConfig *RecommendationJobPayloadConfig // The endpoint type to receive recommendations for. By default this is null, and // the results of the inference recommendation job return a combined list of both // real-time and serverless benchmarks. By specifying a value for this field, you // can receive a longer list of benchmarks for the desired endpoint type. SupportedEndpointType RecommendationJobSupportedEndpointType // A list of the instance types that are used to generate inferences in real-time. SupportedInstanceTypes []string // The machine learning task that the model accomplishes. Valid Values: // IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | // FILL_MASK | CLASSIFICATION | REGRESSION | OTHER Task *string noSmithyDocumentSerde } // The details for a specific benchmark from an Inference Recommender job. type RecommendationJobInferenceBenchmark struct { // Defines the model configuration. Includes the specification name and // environment parameters. // // This member is required. ModelConfiguration *ModelConfiguration // The endpoint configuration made by Inference Recommender during a // recommendation job. EndpointConfiguration *EndpointOutputConfiguration // The metrics for an existing endpoint compared in an Inference Recommender job. EndpointMetrics *InferenceMetrics // The reason why a benchmark failed. FailureReason *string // A timestamp that shows when the benchmark completed. InvocationEndTime *time.Time // A timestamp that shows when the benchmark started. InvocationStartTime *time.Time // The metrics of recommendations. Metrics *RecommendationMetrics noSmithyDocumentSerde } // The input configuration of the recommendation job. type RecommendationJobInputConfig struct { // Specifies mandatory fields for running an Inference Recommender job. The fields // specified in ContainerConfig override the corresponding fields in the model // package. ContainerConfig *RecommendationJobContainerConfig // Specifies the endpoint configuration to use for a job. EndpointConfigurations []EndpointInputConfiguration // Existing customer endpoints on which to run an Inference Recommender job. Endpoints []EndpointInfo // Specifies the maximum duration of the job, in seconds.> JobDurationInSeconds *int32 // The name of the created model. ModelName *string // The Amazon Resource Name (ARN) of a versioned model package. ModelPackageVersionArn *string // Defines the resource limit of the job. ResourceLimit *RecommendationJobResourceLimit // Specifies the traffic pattern of the job. TrafficPattern *TrafficPattern // The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service // (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the // storage volume attached to the ML compute instance that hosts the endpoint. This // key will be passed to SageMaker Hosting for endpoint creation. The SageMaker // execution role must have kms:CreateGrant permission in order to encrypt data on // the storage volume of the endpoints created for inference recommendation. The // inference recommendation job will fail asynchronously during endpoint // configuration creation if the role passed does not have kms:CreateGrant // permission. The KmsKeyId can be any of the following formats: // - // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab" // - // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:::key/" // - // KMS Key Alias "alias/ExampleAlias" // - // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:::alias/" // For more information about key identifiers, see Key identifiers (KeyID) (https://docs.aws.amazon.com/kms/latest/developerguide/concepts.html#key-id-key-id) // in the Amazon Web Services Key Management Service (Amazon Web Services KMS) // documentation. VolumeKmsKeyId *string // Inference Recommender provisions SageMaker endpoints with access to VPC in the // inference recommendation job. VpcConfig *RecommendationJobVpcConfig noSmithyDocumentSerde } // Provides information about the output configuration for the compiled model. type RecommendationJobOutputConfig struct { // Provides information about the output configuration for the compiled model. CompiledOutputConfig *RecommendationJobCompiledOutputConfig // The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service // (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt your output // artifacts with Amazon S3 server-side encryption. The SageMaker execution role // must have kms:GenerateDataKey permission. The KmsKeyId can be any of the // following formats: // - // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab" // - // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:::key/" // - // KMS Key Alias "alias/ExampleAlias" // - // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:::alias/" // For more information about key identifiers, see Key identifiers (KeyID) (https://docs.aws.amazon.com/kms/latest/developerguide/concepts.html#key-id-key-id) // in the Amazon Web Services Key Management Service (Amazon Web Services KMS) // documentation. KmsKeyId *string noSmithyDocumentSerde } // The configuration for the payload for a recommendation job. type RecommendationJobPayloadConfig struct { // The Amazon Simple Storage Service (Amazon S3) path where the sample payload is // stored. This path must point to a single gzip compressed tar archive (.tar.gz // suffix). SamplePayloadUrl *string // The supported MIME types for the input data. SupportedContentTypes []string noSmithyDocumentSerde } // Specifies the maximum number of jobs that can run in parallel and the maximum // number of jobs that can run. type RecommendationJobResourceLimit struct { // Defines the maximum number of load tests. MaxNumberOfTests *int32 // Defines the maximum number of parallel load tests. MaxParallelOfTests *int32 noSmithyDocumentSerde } // Specifies conditions for stopping a job. When a job reaches a stopping // condition limit, SageMaker ends the job. type RecommendationJobStoppingConditions struct { // The maximum number of requests per minute expected for the endpoint. MaxInvocations int32 // The interval of time taken by a model to respond as viewed from SageMaker. The // interval includes the local communication time taken to send the request and to // fetch the response from the container of a model and the time taken to complete // the inference in the container. ModelLatencyThresholds []ModelLatencyThreshold noSmithyDocumentSerde } // Inference Recommender provisions SageMaker endpoints with access to VPC in the // inference recommendation job. type RecommendationJobVpcConfig struct { // The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the // security groups for the VPC that is specified in the Subnets field. // // This member is required. SecurityGroupIds []string // The ID of the subnets in the VPC to which you want to connect your model. // // This member is required. Subnets []string noSmithyDocumentSerde } // The metrics of recommendations. type RecommendationMetrics struct { // Defines the cost per hour for the instance. // // This member is required. CostPerHour float32 // Defines the cost per inference for the instance . // // This member is required. CostPerInference float32 // The expected maximum number of requests per minute for the instance. // // This member is required. MaxInvocations int32 // The expected model latency at maximum invocation per minute for the instance. // // This member is required. ModelLatency int32 // The expected CPU utilization at maximum invocations per minute for the // instance. NaN indicates that the value is not available. CpuUtilization *float32 // The expected memory utilization at maximum invocations per minute for the // instance. NaN indicates that the value is not available. MemoryUtilization *float32 // The time it takes to launch new compute resources for a serverless endpoint. // The time can vary depending on the model size, how long it takes to download the // model, and the start-up time of the container. NaN indicates that the value is // not available. ModelSetupTime *int32 noSmithyDocumentSerde } // Configuration for Redshift Dataset Definition input. type RedshiftDatasetDefinition struct { // The Redshift cluster Identifier. // // This member is required. ClusterId *string // The IAM role attached to your Redshift cluster that Amazon SageMaker uses to // generate datasets. // // This member is required. ClusterRoleArn *string // The name of the Redshift database used in Redshift query execution. // // This member is required. Database *string // The database user name used in Redshift query execution. // // This member is required. DbUser *string // The data storage format for Redshift query results. // // This member is required. OutputFormat RedshiftResultFormat // The location in Amazon S3 where the Redshift 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 from a Redshift execution. KmsKeyId *string // The compression used for Redshift query results. OutputCompression RedshiftResultCompressionType noSmithyDocumentSerde } // Metadata for a register model job step. type RegisterModelStepMetadata struct { // The Amazon Resource Name (ARN) of the model package. Arn *string noSmithyDocumentSerde } // Contains input values for a task. type RenderableTask struct { // A JSON object that contains values for the variables defined in the template. // It is made available to the template under the substitution variable task.input // . For example, if you define a variable task.input.text in your template, you // can supply the variable in the JSON object as "text": "sample text" . // // This member is required. Input *string noSmithyDocumentSerde } // A description of an error that occurred while rendering the template. type RenderingError struct { // A unique identifier for a specific class of errors. // // This member is required. Code *string // A human-readable message describing the error. // // This member is required. Message *string noSmithyDocumentSerde } // Specifies an authentication configuration for the private docker registry where // your model image is hosted. Specify a value for this property only if you // specified Vpc as the value for the RepositoryAccessMode field of the ImageConfig // object that you passed to a call to CreateModel and the private Docker registry // where the model image is hosted requires authentication. type RepositoryAuthConfig struct { // The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that // provides credentials to authenticate to the private Docker registry where your // model image is hosted. For information about how to create an Amazon Web // Services Lambda function, see Create a Lambda function with the console (https://docs.aws.amazon.com/lambda/latest/dg/getting-started-create-function.html) // in the Amazon Web Services Lambda Developer Guide. // // This member is required. RepositoryCredentialsProviderArn *string noSmithyDocumentSerde } // The resolved attributes. type ResolvedAttributes struct { // Specifies a metric to minimize or maximize as the objective of a job. AutoMLJobObjective *AutoMLJobObjective // How long a job is allowed to run, or how many candidates a job is allowed to // generate. CompletionCriteria *AutoMLJobCompletionCriteria // The problem type. ProblemType ProblemType noSmithyDocumentSerde } // A resource catalog containing all of the resources of a specific resource type // within a resource owner account. For an example on sharing the Amazon SageMaker // Feature Store DefaultFeatureGroupCatalog , see Share Amazon SageMaker Catalog // resource type (https://docs.aws.amazon.com/sagemaker/latest/APIReference/feature-store-cross-account-discoverability-share-sagemaker-catalog.html) // in the Amazon SageMaker Developer Guide. type ResourceCatalog struct { // The time the ResourceCatalog was created. // // This member is required. CreationTime *time.Time // A free form description of the ResourceCatalog . // // This member is required. Description *string // The Amazon Resource Name (ARN) of the ResourceCatalog . // // This member is required. ResourceCatalogArn *string // The name of the ResourceCatalog . // // This member is required. ResourceCatalogName *string noSmithyDocumentSerde } // Describes the resources, including machine learning (ML) compute instances and // ML storage volumes, to use for model training. type ResourceConfig struct { // The size of the ML storage volume that you want to provision. ML storage // volumes store model artifacts and incremental states. Training algorithms might // also use the ML storage volume for scratch space. If you want to store the // training data in the ML storage volume, choose File as the TrainingInputMode in // the algorithm specification. When using an ML instance with NVMe SSD volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html#nvme-ssd-volumes) // , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. // Available storage is fixed to the NVMe-type instance's storage capacity. // SageMaker configures storage paths for training datasets, checkpoints, model // artifacts, and outputs to use the entire capacity of the instance storage. For // example, ML instance families with the NVMe-type instance storage include ml.p4d // , ml.g4dn , and ml.g5 . When using an ML instance with the EBS-only storage // option and without instance storage, you must define the size of EBS volume // through VolumeSizeInGB in the ResourceConfig API. For example, ML instance // families that use EBS volumes include ml.c5 and ml.p2 . To look up instance // types and their instance storage types and volumes, see Amazon EC2 Instance // Types (http://aws.amazon.com/ec2/instance-types/) . To find the default local // paths defined by the SageMaker training platform, see Amazon SageMaker Training // Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs (https://docs.aws.amazon.com/sagemaker/latest/dg/model-train-storage.html) // . // // This member is required. VolumeSizeInGB int32 // The number of ML compute instances to use. For distributed training, provide a // value greater than 1. InstanceCount int32 // The configuration of a heterogeneous cluster in JSON format. InstanceGroups []InstanceGroup // The ML compute instance type. SageMaker Training on Amazon Elastic Compute // Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. // Amazon EC2 P4de instances (http://aws.amazon.com/ec2/instance-types/p4/) // (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB // high-performance HBM2e GPU memory, which accelerate the speed of training ML // models that need to be trained on large datasets of high-resolution data. In // this preview release, Amazon SageMaker supports ML training jobs on P4de // instances ( ml.p4de.24xlarge ) to reduce model training time. The // ml.p4de.24xlarge instances are available in the following Amazon Web Services // Regions. // - US East (N. Virginia) (us-east-1) // - US West (Oregon) (us-west-2) // To request quota limit increase and start using P4de instances, contact the // SageMaker Training service team through your account team. InstanceType TrainingInstanceType // The duration of time in seconds to retain configured resources in a warm pool // for subsequent training jobs. KeepAlivePeriodInSeconds *int32 // The Amazon Web Services KMS key that SageMaker uses to encrypt data on the // storage volume attached to the ML compute instance(s) that run the training job. // Certain Nitro-based instances include local storage, dependent on the instance // type. Local storage volumes are encrypted using a hardware module on the // instance. You can't request a VolumeKmsKeyId when using an instance type with // local storage. For a list of instance types that support local instance storage, // see Instance Store Volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes) // . For more information about local instance storage encryption, see SSD // Instance Store Volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html) // . The VolumeKmsKeyId can be in any of the following formats: // - // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab" // - // Amazon Resource Name (ARN) of a KMS Key // "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab" VolumeKmsKeyId *string noSmithyDocumentSerde } // The ResourceConfig to update KeepAlivePeriodInSeconds . Other fields in the // ResourceConfig cannot be updated. type ResourceConfigForUpdate struct { // The KeepAlivePeriodInSeconds value specified in the ResourceConfig to update. // // This member is required. KeepAlivePeriodInSeconds *int32 noSmithyDocumentSerde } // Specifies the maximum number of training jobs and parallel training jobs that a // hyperparameter tuning job can launch. type ResourceLimits struct { // The maximum number of concurrent training jobs that a hyperparameter tuning job // can launch. // // This member is required. MaxParallelTrainingJobs int32 // The maximum number of training jobs that a hyperparameter tuning job can launch. MaxNumberOfTrainingJobs *int32 // The maximum time in seconds that a hyperparameter tuning job can run. MaxRuntimeInSeconds *int32 noSmithyDocumentSerde } // Specifies the ARN's of a SageMaker image and SageMaker image version, and the // instance type that the version runs on. type ResourceSpec struct { // The instance type that the image version runs on. JupyterServer apps only // support the system value. For KernelGateway apps, the system value is // translated to ml.t3.medium . KernelGateway apps also support all other values // for available instance types. InstanceType AppInstanceType // The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the // Resource. LifecycleConfigArn *string // The ARN of the SageMaker image that the image version belongs to. SageMakerImageArn *string // The ARN of the image version created on the instance. SageMakerImageVersionArn *string noSmithyDocumentSerde } // The retention policy for data stored on an Amazon Elastic File System (EFS) // volume. type RetentionPolicy struct { // The default is Retain , which specifies to keep the data stored on the EFS // volume. Specify Delete to delete the data stored on the EFS volume. HomeEfsFileSystem RetentionType noSmithyDocumentSerde } // The retry strategy to use when a training job fails due to an // InternalServerError . RetryStrategy is specified as part of the // CreateTrainingJob and CreateHyperParameterTuningJob requests. You can add the // StoppingCondition parameter to the request to limit the training time for the // complete job. type RetryStrategy struct { // The number of times to retry the job. When the job is retried, it's // SecondaryStatus is changed to STARTING . // // This member is required. MaximumRetryAttempts int32 noSmithyDocumentSerde } // Specifies a rolling deployment strategy for updating a SageMaker endpoint. type RollingUpdatePolicy struct { // Batch size for each rolling step to provision capacity and turn on traffic on // the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value // must be between 5% to 50% of the variant's total instance count. // // This member is required. MaximumBatchSize *CapacitySize // The length of the baking period, during which SageMaker monitors alarms for // each batch on the new fleet. // // This member is required. WaitIntervalInSeconds *int32 // The time limit for the total deployment. Exceeding this limit causes a timeout. MaximumExecutionTimeoutInSeconds *int32 // Batch size for rollback to the old endpoint fleet. Each rolling step to // provision capacity and turn on traffic on the old endpoint fleet, and terminate // capacity on the new endpoint fleet. If this field is absent, the default value // will be set to 100% of total capacity which means to bring up the whole capacity // of the old fleet at once during rollback. RollbackMaximumBatchSize *CapacitySize noSmithyDocumentSerde } // A collection of settings that apply to an RSessionGateway app. type RSessionAppSettings struct { // A list of custom SageMaker images that are configured to run as a RSession app. CustomImages []CustomImage // Specifies the ARN's of a SageMaker image and SageMaker image version, and the // instance type that the version runs on. DefaultResourceSpec *ResourceSpec noSmithyDocumentSerde } // A collection of settings that configure user interaction with the // RStudioServerPro app. type RStudioServerProAppSettings struct { // Indicates whether the current user has access to the RStudioServerPro app. AccessStatus RStudioServerProAccessStatus // The level of permissions that the user has within the RStudioServerPro app. // This value defaults to `User`. The `Admin` value allows the user access to the // RStudio Administrative Dashboard. UserGroup RStudioServerProUserGroup noSmithyDocumentSerde } // A collection of settings that configure the RStudioServerPro Domain-level app. type RStudioServerProDomainSettings struct { // The ARN of the execution role for the RStudioServerPro Domain-level app. // // This member is required. DomainExecutionRoleArn *string // Specifies the ARN's of a SageMaker image and SageMaker image version, and the // instance type that the version runs on. DefaultResourceSpec *ResourceSpec // A URL pointing to an RStudio Connect server. RStudioConnectUrl *string // A URL pointing to an RStudio Package Manager server. RStudioPackageManagerUrl *string noSmithyDocumentSerde } // A collection of settings that update the current configuration for the // RStudioServerPro Domain-level app. type RStudioServerProDomainSettingsForUpdate struct { // The execution role for the RStudioServerPro Domain-level app. // // This member is required. DomainExecutionRoleArn *string // Specifies the ARN's of a SageMaker image and SageMaker image version, and the // instance type that the version runs on. DefaultResourceSpec *ResourceSpec // A URL pointing to an RStudio Connect server. RStudioConnectUrl *string // A URL pointing to an RStudio Package Manager server. RStudioPackageManagerUrl *string noSmithyDocumentSerde } // Describes the S3 data source. Your input bucket must be in the same Amazon Web // Services region as your training job. type S3DataSource struct { // If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all // objects that match the specified key name prefix for model training. If you // choose ManifestFile , S3Uri identifies an object that is a manifest file // containing a list of object keys that you want SageMaker to use for model // training. If you choose AugmentedManifestFile , S3Uri identifies an object that // is an augmented manifest file in JSON lines format. This file contains the data // you want to use for model training. AugmentedManifestFile can only be used if // the Channel's input mode is Pipe . // // This member is required. S3DataType S3DataType // Depending on the value specified for the S3DataType , identifies either a key // name prefix or a manifest. For example: // - A key name prefix might look like this: s3://bucketname/exampleprefix // - A manifest might look like this: s3://bucketname/example.manifest A manifest // is an S3 object which is a JSON file consisting of an array of elements. The // first element is a prefix which is followed by one or more suffixes. SageMaker // appends the suffix elements to the prefix to get a full set of S3Uri . Note // that the prefix must be a valid non-empty S3Uri that precludes users from // specifying a manifest whose individual S3Uri is sourced from different S3 // buckets. The following code example shows a valid manifest format: [ // {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", // "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is // equivalent to the following S3Uri list: // s3://customer_bucket/some/prefix/relative/path/to/custdata-1 // s3://customer_bucket/some/prefix/relative/path/custdata-2 ... // s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of // S3Uri in this manifest is the input data for the channel for this data source. // The object that each S3Uri points to must be readable by the IAM role that // SageMaker uses to perform tasks on your behalf. // Your input bucket must be located in same Amazon Web Services region as your // training job. // // This member is required. S3Uri *string // A list of one or more attribute names to use that are found in a specified // augmented manifest file. AttributeNames []string // A list of names of instance groups that get data from the S3 data source. InstanceGroupNames []string // If you want SageMaker to replicate the entire dataset on each ML compute // instance that is launched for model training, specify FullyReplicated . If you // want SageMaker to replicate a subset of data on each ML compute instance that is // launched for model training, specify ShardedByS3Key . If there are n ML compute // instances launched for a training job, each instance gets approximately 1/n of // the number of S3 objects. In this case, model training on each machine uses only // the subset of training data. Don't choose more ML compute instances for training // than available S3 objects. If you do, some nodes won't get any data and you will // pay for nodes that aren't getting any training data. This applies in both File // and Pipe modes. Keep this in mind when developing algorithms. In distributed // training, where you use multiple ML compute EC2 instances, you might choose // ShardedByS3Key . If the algorithm requires copying training data to the ML // storage volume (when TrainingInputMode is set to File ), this copies 1/n of the // number of objects. S3DataDistributionType S3DataDistribution noSmithyDocumentSerde } // Specifies the S3 location of ML model data to deploy. type S3ModelDataSource struct { // Specifies how the ML model data is prepared. If you choose Gzip and choose // S3Object as the value of S3DataType , S3Uri identifies an object that is a // gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the // object during model deployment. If you choose None and chooose S3Object as the // value of S3DataType , S3Uri identifies an object that represents an // uncompressed ML model to deploy. If you choose None and choose S3Prefix as the // value of S3DataType , S3Uri identifies a key name prefix, under which all // objects represents the uncompressed ML model to deploy. If you choose None, then // SageMaker will follow rules below when creating model data files under // /opt/ml/model directory for use by your inference code: // - If you choose S3Object as the value of S3DataType , then SageMaker will // split the key of the S3 object referenced by S3Uri by slash (/), and use the // last part as the filename of the file holding the content of the S3 object. // - If you choose S3Prefix as the value of S3DataType , then for each S3 object // under the key name pefix referenced by S3Uri , SageMaker will trim its key by // the prefix, and use the remainder as the path (relative to /opt/ml/model ) of // the file holding the content of the S3 object. SageMaker will split the // remainder by slash (/), using intermediate parts as directory names and the last // part as filename of the file holding the content of the S3 object. // - Do not use any of the following as file names or directory names: // - An empty or blank string // - A string which contains null bytes // - A string longer than 255 bytes // - A single dot ( . ) // - A double dot ( .. ) // - Ambiguous file names will result in model deployment failure. For example, // if your uncompressed ML model consists of two S3 objects // s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you // specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value // of S3DataType , then it will result in name clash between // /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a // directory). // - Do not organize the model artifacts in S3 console using folders (https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) // . When you create a folder in S3 console, S3 creates a 0-byte object with a key // set to the folder name you provide. They key of the 0-byte object ends with a // slash (/) which violates SageMaker restrictions on model artifact file names, // leading to model deployment failure. // // This member is required. CompressionType ModelCompressionType // Specifies the type of ML model data to deploy. If you choose S3Prefix , S3Uri // identifies a key name prefix. SageMaker uses all objects that match the // specified key name prefix as part of the ML model data to deploy. A valid key // name prefix identified by S3Uri always ends with a forward slash (/). If you // choose S3Object , S3Uri identifies an object that is the ML model data to // deploy. // // This member is required. S3DataType S3ModelDataType // Specifies the S3 path of ML model data to deploy. // // This member is required. S3Uri *string noSmithyDocumentSerde } // The Amazon Simple Storage (Amazon S3) location and and security configuration // for OfflineStore . type S3StorageConfig struct { // The S3 URI, or location in Amazon S3, of OfflineStore . S3 URIs have a format // similar to the following: s3://example-bucket/prefix/ . // // This member is required. S3Uri *string // The Amazon Web Services Key Management Service (KMS) key ARN of the key used to // encrypt any objects written into the OfflineStore S3 location. The IAM roleARN // that is passed as a parameter to CreateFeatureGroup must have below permissions // to the KmsKeyId : // - "kms:GenerateDataKey" KmsKeyId *string // The S3 path where offline records are written. ResolvedOutputS3Uri *string noSmithyDocumentSerde } // Configuration details about the monitoring schedule. type ScheduleConfig struct { // A cron expression that describes details about the monitoring schedule. // Currently the only supported cron expressions are: // - If you want to set the job to start every hour, please use the following: // Hourly: cron(0 * ? * * *) // - If you want to start the job daily: cron(0 [00-23] ? * * *) // For example, the following are valid cron expressions: // - Daily at noon UTC: cron(0 12 ? * * *) // - Daily at midnight UTC: cron(0 0 ? * * *) // To support running every 6, 12 hours, the following are also supported: cron(0 // [00-23]/[01-24] ? * * *) For example, the following are valid cron expressions: // - Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *) // - Every two hours starting at midnight: cron(0 0/2 ? * * *) // // - Even though the cron expression is set to start at 5PM UTC, note that there // could be a delay of 0-20 minutes from the actual requested time to run the // execution. // - We recommend that if you would like a daily schedule, you do not provide // this parameter. Amazon SageMaker will pick a time for running every day. // // This member is required. ScheduleExpression *string noSmithyDocumentSerde } // A multi-expression that searches for the specified resource or resources in a // search. All resource objects that satisfy the expression's condition are // included in the search results. You must specify at least one subexpression, // filter, or nested filter. A SearchExpression can contain up to twenty elements. // A SearchExpression contains the following components: // - A list of Filter objects. Each filter defines a simple Boolean expression // comprised of a resource property name, Boolean operator, and value. // - A list of NestedFilter objects. Each nested filter defines a list of Boolean // expressions using a list of resource properties. A nested filter is satisfied if // a single object in the list satisfies all Boolean expressions. // - A list of SearchExpression objects. A search expression object can be nested // in a list of search expression objects. // - A Boolean operator: And or Or . type SearchExpression struct { // A list of filter objects. Filters []Filter // A list of nested filter objects. NestedFilters []NestedFilters // A Boolean operator used to evaluate the search expression. If you want every // conditional statement in all lists to be satisfied for the entire search // expression to be true, specify And . If only a single conditional statement // needs to be true for the entire search expression to be true, specify Or . The // default value is And . Operator BooleanOperator // A list of search expression objects. SubExpressions []SearchExpression noSmithyDocumentSerde } // A single resource returned as part of the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) // API response. type SearchRecord struct { // A hosted endpoint for real-time inference. Endpoint *Endpoint // The properties of an experiment. Experiment *Experiment // Amazon SageMaker Feature Store stores features in a collection called Feature // Group. A Feature Group can be visualized as a table which has rows, with a // unique identifier for each row where each column in the table is a feature. In // principle, a Feature Group is composed of features and values per features. FeatureGroup *FeatureGroup // The feature metadata used to search through the features. FeatureMetadata *FeatureMetadata // The properties of a hyperparameter tuning job. HyperParameterTuningJob *HyperParameterTuningJobSearchEntity // A model displayed in the Amazon SageMaker Model Dashboard. Model *ModelDashboardModel // An Amazon SageMaker Model Card that documents details about a machine learning // model. ModelCard *ModelCard // A versioned model that can be deployed for SageMaker inference. ModelPackage *ModelPackage // A group of versioned models in the model registry. ModelPackageGroup *ModelPackageGroup // A SageMaker Model Building Pipeline instance. Pipeline *Pipeline // An execution of a pipeline. PipelineExecution *PipelineExecution // The properties of a project. Project *Project // The properties of a training job. TrainingJob *TrainingJob // The properties of a trial. Trial *Trial // The properties of a trial component. TrialComponent *TrialComponent noSmithyDocumentSerde } // An array element of SecondaryStatusTransitions for DescribeTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) // . It provides additional details about a status that the training job has // transitioned through. A training job can be in one of several states, for // example, starting, downloading, training, or uploading. Within each state, there // are a number of intermediate states. For example, within the starting state, // SageMaker could be starting the training job or launching the ML instances. // These transitional states are referred to as the job's secondary status. type SecondaryStatusTransition struct { // A timestamp that shows when the training job transitioned to the current // secondary status state. // // This member is required. StartTime *time.Time // Contains a secondary status information from a training job. Status might be // one of the following secondary statuses: InProgress // - Starting - Starting the training job. // - Downloading - An optional stage for algorithms that support File training // input mode. It indicates that data is being downloaded to the ML storage // volumes. // - Training - Training is in progress. // - Uploading - Training is complete and the model artifacts are being uploaded // to the S3 location. // Completed // - Completed - The training job has completed. // Failed // - Failed - The training job has failed. The reason for the failure is returned // in the FailureReason field of DescribeTrainingJobResponse . // Stopped // - MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed // runtime. // - Stopped - The training job has stopped. // Stopping // - Stopping - Stopping the training job. // We no longer support the following secondary statuses: // - LaunchingMLInstances // - PreparingTrainingStack // - DownloadingTrainingImage // // This member is required. Status SecondaryStatus // A timestamp that shows when the training job transitioned out of this secondary // status state into another secondary status state or when the training job has // ended. EndTime *time.Time // A detailed description of the progress within a secondary status. SageMaker // provides secondary statuses and status messages that apply to each of them: // Starting // - Starting the training job. // - Launching requested ML instances. // - Insufficient capacity error from EC2 while launching instances, retrying! // - Launched instance was unhealthy, replacing it! // - Preparing the instances for training. // Training // - Downloading the training image. // - Training image download completed. Training in progress. // Status messages are subject to change. Therefore, we recommend not including // them in code that programmatically initiates actions. For examples, don't use // status messages in if statements. To have an overview of your training job's // progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) // , and StatusMessage together. For example, at the start of a training job, you // might see the following: // - TrainingJobStatus - InProgress // - SecondaryStatus - Training // - StatusMessage - Downloading the training image StatusMessage *string noSmithyDocumentSerde } // A step selected to run in selective execution mode. type SelectedStep struct { // The name of the pipeline step. // // This member is required. StepName *string noSmithyDocumentSerde } // The selective execution configuration applied to the pipeline run. type SelectiveExecutionConfig struct { // A list of pipeline steps to run. All step(s) in all path(s) between two // selected steps should be included. // // This member is required. SelectedSteps []SelectedStep // The ARN from a reference execution of the current pipeline. Used to copy input // collaterals needed for the selected steps to run. The execution status of the // pipeline can be either Failed or Success . // // This member is required. SourcePipelineExecutionArn *string noSmithyDocumentSerde } // The ARN from an execution of the current pipeline. type SelectiveExecutionResult struct { // The ARN from an execution of the current pipeline. SourcePipelineExecutionArn *string noSmithyDocumentSerde } // Details of a provisioned service catalog product. For information about service // catalog, see What is Amazon Web Services Service Catalog (https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html) // . type ServiceCatalogProvisionedProductDetails struct { // The ID of the provisioned product. ProvisionedProductId *string // The current status of the product. // - AVAILABLE - Stable state, ready to perform any operation. The most recent // operation succeeded and completed. // - UNDER_CHANGE - Transitive state. Operations performed might not have valid // results. Wait for an AVAILABLE status before performing operations. // - TAINTED - Stable state, ready to perform any operation. The stack has // completed the requested operation but is not exactly what was requested. For // example, a request to update to a new version failed and the stack rolled back // to the current version. // - ERROR - An unexpected error occurred. The provisioned product exists but the // stack is not running. For example, CloudFormation received a parameter value // that was not valid and could not launch the stack. // - PLAN_IN_PROGRESS - Transitive state. The plan operations were performed to // provision a new product, but resources have not yet been created. After // reviewing the list of resources to be created, execute the plan. Wait for an // AVAILABLE status before performing operations. ProvisionedProductStatusMessage *string noSmithyDocumentSerde } // Details that you specify to provision a service catalog product. For // information about service catalog, see What is Amazon Web Services Service // Catalog (https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html) // . type ServiceCatalogProvisioningDetails struct { // The ID of the product to provision. // // This member is required. ProductId *string // The path identifier of the product. This value is optional if the product has a // default path, and required if the product has more than one path. PathId *string // The ID of the provisioning artifact. ProvisioningArtifactId *string // A list of key value pairs that you specify when you provision a product. ProvisioningParameters []ProvisioningParameter noSmithyDocumentSerde } // Details that you specify to provision a service catalog product. For // information about service catalog, see What is Amazon Web Services Service // Catalog (https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html) // . type ServiceCatalogProvisioningUpdateDetails struct { // The ID of the provisioning artifact. ProvisioningArtifactId *string // A list of key value pairs that you specify when you provision a product. ProvisioningParameters []ProvisioningParameter noSmithyDocumentSerde } // The configuration of ShadowMode inference experiment type, which specifies a // production variant to take all the inference requests, and a shadow variant to // which Amazon SageMaker replicates a percentage of the inference requests. For // the shadow variant it also specifies the percentage of requests that Amazon // SageMaker replicates. type ShadowModeConfig struct { // List of shadow variant configurations. // // This member is required. ShadowModelVariants []ShadowModelVariantConfig // The name of the production variant, which takes all the inference requests. // // This member is required. SourceModelVariantName *string noSmithyDocumentSerde } // The name and sampling percentage of a shadow variant. type ShadowModelVariantConfig struct { // The percentage of inference requests that Amazon SageMaker replicates from the // production variant to the shadow variant. // // This member is required. SamplingPercentage int32 // The name of the shadow variant. // // This member is required. ShadowModelVariantName *string noSmithyDocumentSerde } // Specifies options for sharing SageMaker Studio notebooks. These settings are // specified as part of DefaultUserSettings when the CreateDomain API is called, // and as part of UserSettings when the CreateUserProfile API is called. When // SharingSettings is not specified, notebook sharing isn't allowed. type SharingSettings struct { // Whether to include the notebook cell output when sharing the notebook. The // default is Disabled . NotebookOutputOption NotebookOutputOption // When NotebookOutputOption is Allowed , the Amazon Web Services Key Management // Service (KMS) encryption key ID used to encrypt the notebook cell output in the // Amazon S3 bucket. S3KmsKeyId *string // When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the // shared notebook snapshots. S3OutputPath *string noSmithyDocumentSerde } // A configuration for a shuffle option for input data in a channel. If you use // S3Prefix for S3DataType , the results of the S3 key prefix matches are shuffled. // 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, when ShuffleConfig is // specified 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, // and 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. type ShuffleConfig struct { // Determines the shuffling order in ShuffleConfig value. // // This member is required. Seed int64 noSmithyDocumentSerde } // Specifies an algorithm that was used to create the model package. The algorithm // must be either an algorithm resource in your SageMaker account or an algorithm // in Amazon Web Services Marketplace that you are subscribed to. type SourceAlgorithm struct { // The name of an algorithm that was used to create the model package. The // algorithm must be either an algorithm resource in your SageMaker account or an // algorithm in Amazon Web Services Marketplace that you are subscribed to. // // This member is required. AlgorithmName *string // The Amazon S3 path where the model artifacts, which result from model training, // are stored. This path must point to a single gzip compressed tar archive ( // .tar.gz suffix). The model artifacts must be in an S3 bucket that is in the same // Amazon Web Services region as the algorithm. ModelDataUrl *string noSmithyDocumentSerde } // A list of algorithms that were used to create a model package. type SourceAlgorithmSpecification struct { // A list of the algorithms that were used to create a model package. // // This member is required. SourceAlgorithms []SourceAlgorithm noSmithyDocumentSerde } // A list of IP address ranges ( CIDRs (https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html) // ). Used to create an allow list of IP addresses for a private workforce. Workers // will only be able to login to their worker portal from an IP address within this // range. By default, a workforce isn't restricted to specific IP addresses. type SourceIpConfig struct { // A list of one to ten Classless Inter-Domain Routing (https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html) // (CIDR) values. Maximum: Ten CIDR values The following Length Constraints apply // to individual CIDR values in the CIDR value list. // // This member is required. Cidrs []string noSmithyDocumentSerde } // The space's details. type SpaceDetails struct { // The creation time. CreationTime *time.Time // The ID of the associated Domain. DomainId *string // The last modified time. LastModifiedTime *time.Time // The name of the space. SpaceName *string // The status. Status SpaceStatus noSmithyDocumentSerde } // A collection of space settings. type SpaceSettings struct { // The JupyterServer app settings. JupyterServerAppSettings *JupyterServerAppSettings // The KernelGateway app settings. KernelGatewayAppSettings *KernelGatewayAppSettings noSmithyDocumentSerde } // Specifies a limit to how long a model training job or model compilation job can // run. It also specifies how long a managed spot training job has to complete. // When the job reaches the time limit, SageMaker ends the training or compilation // job. Use this API to cap model training costs. To stop a training job, SageMaker // sends the algorithm the SIGTERM signal, which delays job termination for 120 // seconds. Algorithms can use this 120-second window to save the model artifacts, // so the results of training are not lost. The training algorithms provided by // SageMaker automatically save the intermediate results of a model training job // when possible. This attempt to save artifacts is only a best effort case as // model might not be in a state from which it can be saved. For example, if // training has just started, the model might not be ready to save. When saved, // this intermediate data is a valid model artifact. You can use it to create a // model with CreateModel . The Neural Topic Model (NTM) currently does not support // saving intermediate model artifacts. When training NTMs, make sure that the // maximum runtime is sufficient for the training job to complete. type StoppingCondition struct { // The maximum length of time, in seconds, that a training or compilation job can // run before it is stopped. For compilation jobs, if the job does not complete // during this time, a TimeOut error is generated. We recommend starting with 900 // seconds and increasing as necessary based on your model. For all other jobs, if // the job does not complete during this time, SageMaker ends the job. When // RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies // the maximum time for all of the attempts in total, not each individual attempt. // The default value is 1 day. The maximum value is 28 days. The maximum time that // a TrainingJob can run in total, including any time spent publishing metrics or // archiving and uploading models after it has been stopped, is 30 days. MaxRuntimeInSeconds int32 // The maximum length of time, in seconds, that a managed Spot training job has to // complete. It is the amount of time spent waiting for Spot capacity plus the // amount of time the job can run. It must be equal to or greater than // MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker // ends the job. When RetryStrategy is specified in the job request, // MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in // total, not each individual attempt. MaxWaitTimeInSeconds *int32 noSmithyDocumentSerde } // Details of the Studio Lifecycle Configuration. type StudioLifecycleConfigDetails struct { // The creation time of the Studio Lifecycle Configuration. CreationTime *time.Time // This value is equivalent to CreationTime because Studio Lifecycle // Configurations are immutable. LastModifiedTime *time.Time // The App type to which the Lifecycle Configuration is attached. StudioLifecycleConfigAppType StudioLifecycleConfigAppType // The Amazon Resource Name (ARN) of the Lifecycle Configuration. StudioLifecycleConfigArn *string // The name of the Studio Lifecycle Configuration. StudioLifecycleConfigName *string noSmithyDocumentSerde } // Describes a work team of a vendor that does the a labelling job. type SubscribedWorkteam struct { // The Amazon Resource Name (ARN) of the vendor that you have subscribed. // // This member is required. WorkteamArn *string // Marketplace product listing ID. ListingId *string // The description of the vendor from the Amazon Marketplace. MarketplaceDescription *string // The title of the service provided by the vendor in the Amazon Marketplace. MarketplaceTitle *string // The name of the vendor in the Amazon Marketplace. SellerName *string noSmithyDocumentSerde } // Specified in the GetSearchSuggestions (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_GetSearchSuggestions.html) // request. Limits the property names that are included in the response. type SuggestionQuery struct { // Defines a property name hint. Only property names that begin with the specified // hint are included in the response. PropertyNameQuery *PropertyNameQuery noSmithyDocumentSerde } // The collection of settings used by an AutoML job V2 for the TABULAR problem // type. type TabularJobConfig struct { // The name of the target variable in supervised learning, usually represented by // 'y'. // // This member is required. TargetAttributeName *string // The configuration information of how model candidates are generated. CandidateGenerationConfig *CandidateGenerationConfig // How long a job is allowed to run, or how many candidates a job is allowed to // generate. CompletionCriteria *AutoMLJobCompletionCriteria // A URL to the Amazon S3 data source containing selected features from the input // data source to run an Autopilot job V2. 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 // Generates possible candidates without training the models. A model candidate is // a combination of data preprocessors, algorithms, and algorithm parameter // settings. GenerateCandidateDefinitionsOnly bool // 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 type of supervised learning problem available for the model candidates of // the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem // types (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types) // . You must either specify the type of supervised learning problem in ProblemType // and provide the AutoMLJobObjective (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html#sagemaker-CreateAutoMLJobV2-request-AutoMLJobObjective) // metric, or none at all. ProblemType ProblemType // 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 noSmithyDocumentSerde } // The resolved attributes specific to the TABULAR problem type. type TabularResolvedAttributes struct { // The type of supervised learning problem available for the model candidates of // the AutoML job V2 (Binary Classification, Multiclass Classification, // Regression). For more information, see Amazon SageMaker Autopilot problem types (https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types) // . ProblemType ProblemType noSmithyDocumentSerde } // A tag object that consists of a key and an optional value, used to manage // metadata for SageMaker Amazon Web Services resources. You can add tags to // notebook instances, training jobs, hyperparameter tuning jobs, batch transform // jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. // For more information on adding tags to SageMaker resources, see AddTags (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AddTags.html) // . For more information on adding metadata to your Amazon Web Services resources // with tagging, see Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html) // . For advice on best practices for managing Amazon Web Services resources with // tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services // Resource Tagging Strategy (https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf) // . type Tag struct { // The tag key. Tag keys must be unique per resource. // // This member is required. Key *string // The tag value. // // This member is required. Value *string noSmithyDocumentSerde } // Contains information about a target platform that you want your model to run // on, such as OS, architecture, and accelerators. It is an alternative of // TargetDevice . type TargetPlatform struct { // Specifies a target platform architecture. // - X86_64 : 64-bit version of the x86 instruction set. // - X86 : 32-bit version of the x86 instruction set. // - ARM64 : ARMv8 64-bit CPU. // - ARM_EABIHF : ARMv7 32-bit, Hard Float. // - ARM_EABI : ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform. // // This member is required. Arch TargetPlatformArch // Specifies a target platform OS. // - LINUX : Linux-based operating systems. // - ANDROID : Android operating systems. Android API level can be specified // using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": // {'ANDROID_PLATFORM': 28} // // This member is required. Os TargetPlatformOs // Specifies a target platform accelerator (optional). // - NVIDIA : Nvidia graphics processing unit. It also requires gpu-code , // trt-ver , cuda-ver compiler options // - MALI : ARM Mali graphics processor // - INTEL_GRAPHICS : Integrated Intel graphics Accelerator TargetPlatformAccelerator noSmithyDocumentSerde } // The TensorBoard app settings. type TensorBoardAppSettings struct { // The default instance type and the Amazon Resource Name (ARN) of the SageMaker // image created on the instance. DefaultResourceSpec *ResourceSpec noSmithyDocumentSerde } // Configuration of storage locations for the Amazon SageMaker Debugger // TensorBoard output data. type TensorBoardOutputConfig struct { // Path to Amazon S3 storage location for TensorBoard output. // // This member is required. S3OutputPath *string // Path to local storage location for tensorBoard output. Defaults to // /opt/ml/output/tensorboard . LocalPath *string noSmithyDocumentSerde } // Stores the configuration information for the text classification problem of an // AutoML job V2. type TextClassificationJobConfig struct { // The name of the column used to provide the sentences to be classified. It // should not be the same as the target column. // // This member is required. ContentColumn *string // The name of the column used to provide the class labels. It should not be same // as the content column. // // This member is required. TargetLabelColumn *string // How long a job is allowed to run, or how many candidates a job is allowed to // generate. CompletionCriteria *AutoMLJobCompletionCriteria noSmithyDocumentSerde } // The collection of components that defines the time-series. type TimeSeriesConfig struct { // The name of the column that represents the set of item identifiers for which // you want to predict the target value. // // This member is required. ItemIdentifierAttributeName *string // The name of the column representing the target variable that you want to // predict for each item in your dataset. The data type of the target variable must // be numerical. // // This member is required. TargetAttributeName *string // The name of the column indicating a point in time at which the target value of // a given item is recorded. // // This member is required. TimestampAttributeName *string // A set of columns names that can be grouped with the item identifier column to // create a composite key for which a target value is predicted. GroupingAttributeNames []string noSmithyDocumentSerde } // The collection of settings used by an AutoML job V2 for the time-series // forecasting problem type. The TimeSeriesForecastingJobConfig problem type is // only available in private beta. Contact Amazon Web Services Support or your // account manager to learn more about access privileges. type TimeSeriesForecastingJobConfig struct { // The frequency of predictions in a forecast. Valid intervals are an integer // followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). // For example, 1D indicates every day and 15min indicates every 15 minutes. The // value of a frequency must not overlap with the next larger frequency. For // example, you must use a frequency of 1H instead of 60min . The valid values for // each frequency are the following: // - Minute - 1-59 // - Hour - 1-23 // - Day - 1-6 // - Week - 1-4 // - Month - 1-11 // - Year - 1 // // This member is required. ForecastFrequency *string // The number of time-steps that the model predicts. The forecast horizon is also // called the prediction length. The maximum forecast horizon is the lesser of 500 // time-steps or 1/4 of the time-steps in the dataset. // // This member is required. ForecastHorizon *int32 // The collection of components that defines the time-series. // // This member is required. TimeSeriesConfig *TimeSeriesConfig // How long a job is allowed to run, or how many candidates a job is allowed to // generate. CompletionCriteria *AutoMLJobCompletionCriteria // A URL to the Amazon S3 data source containing additional selected features that // complement the target, itemID, timestamp, and grouped columns set in // TimeSeriesConfig . When not provided, the AutoML job V2 includes all the columns // from the original dataset that are not already declared in TimeSeriesConfig . If // provided, the AutoML job V2 only considers these additional columns as a // complement to the ones declared in TimeSeriesConfig . 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" ... } } Autopilot // supports the following data types: numeric , categorical , text , and datetime . // These column keys must not include any column set in TimeSeriesConfig . FeatureSpecificationS3Uri *string // The quantiles used to train the model for forecasts at a specified quantile. // You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 // or higher. Up to five forecast quantiles can be specified. When // ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, // and p90 as default. ForecastQuantiles []string // The transformations modifying specific attributes of the time-series, such as // filling strategies for missing values. Transformations *TimeSeriesTransformations noSmithyDocumentSerde } // Time series forecast settings for the SageMaker Canvas application. type TimeSeriesForecastingSettings struct { // The IAM role that Canvas passes to Amazon Forecast for time series forecasting. // By default, Canvas uses the execution role specified in the UserProfile that // launches the Canvas application. If an execution role is not specified in the // UserProfile , Canvas uses the execution role specified in the Domain that owns // the UserProfile . To allow time series forecasting, this IAM role should have // the AmazonSageMakerCanvasForecastAccess (https://docs.aws.amazon.com/sagemaker/latest/dg/security-iam-awsmanpol-canvas.html#security-iam-awsmanpol-AmazonSageMakerCanvasForecastAccess) // policy attached and forecast.amazonaws.com added in the trust relationship as a // service principal. AmazonForecastRoleArn *string // Describes whether time series forecasting is enabled or disabled in the Canvas // application. Status FeatureStatus noSmithyDocumentSerde } // Transformations allowed on the dataset. Supported transformations are Filling // and Aggregation . Filling specifies how to add values to missing values in the // dataset. Aggregation defines how to aggregate data that does not align with // forecast frequency. type TimeSeriesTransformations struct { // A key value pair defining the aggregation method for a column, where the key is // the column name and the value is the aggregation method. The supported // aggregation methods are sum (default), avg , first , min , max . Aggregation is // only supported for the target column. Aggregation map[string]AggregationTransformationValue // A key value pair defining the filling method for a column, where the key is the // column name and the value is an object which defines the filling logic. You can // specify multiple filling methods for a single column. The supported filling // methods and their corresponding options are: // - frontfill : none (Supported only for target column) // - middlefill : zero , value , median , mean , min , max // - backfill : zero , value , median , mean , min , max // - futurefill : zero , value , median , mean , min , max // To set a filling method to a specific value, set the fill parameter to the // chosen filling method value (for example "backfill" : "value" ), and define the // filling value in an additional parameter prefixed with "_value". For example, to // set backfill to a value of 2 , you must include two parameters: "backfill": // "value" and "backfill_value":"2" . Filling map[string]map[string]string noSmithyDocumentSerde } // Defines the traffic pattern of the load test. type TrafficPattern struct { // Defines the phases traffic specification. Phases []Phase // Defines the traffic patterns. TrafficType TrafficType noSmithyDocumentSerde } // Defines the traffic routing strategy during an endpoint deployment to shift // traffic from the old fleet to the new fleet. type TrafficRoutingConfig struct { // Traffic routing strategy type. // - ALL_AT_ONCE : Endpoint traffic shifts to the new fleet in a single step. // - CANARY : Endpoint traffic shifts to the new fleet in two steps. The first // step is the canary, which is a small portion of the traffic. The second step is // the remainder of the traffic. // - LINEAR : Endpoint traffic shifts to the new fleet in n steps of a // configurable size. // // This member is required. Type TrafficRoutingConfigType // The waiting time (in seconds) between incremental steps to turn on traffic on // the new endpoint fleet. // // This member is required. WaitIntervalInSeconds *int32 // Batch size for the first step to turn on traffic on the new endpoint fleet. // Value must be less than or equal to 50% of the variant's total instance count. CanarySize *CapacitySize // Batch size for each step to turn on traffic on the new endpoint fleet. Value // must be 10-50% of the variant's total instance count. LinearStepSize *CapacitySize noSmithyDocumentSerde } // The configuration to use an image from a private Docker registry for a training // job. type TrainingImageConfig struct { // The method that your training job will use to gain access to the images in your // private Docker registry. For access to an image in a private Docker registry, // set to Vpc . // // This member is required. TrainingRepositoryAccessMode TrainingRepositoryAccessMode // An object containing authentication information for a private Docker registry // containing your training images. TrainingRepositoryAuthConfig *TrainingRepositoryAuthConfig noSmithyDocumentSerde } // Contains information about a training job. type TrainingJob struct { // Information about the algorithm used for training, and algorithm metadata. AlgorithmSpecification *AlgorithmSpecification // The Amazon Resource Name (ARN) of the job. AutoMLJobArn *string // The billable time in seconds. BillableTimeInSeconds *int32 // Contains information about the output location for managed spot training // checkpoint data. CheckpointConfig *CheckpointConfig // A timestamp that indicates when the training job was created. CreationTime *time.Time // Configuration information for the Amazon SageMaker Debugger hook parameters, // metric and tensor collections, and storage paths. To learn more about how to // configure the DebugHookConfig parameter, see Use the SageMaker and Debugger // Configuration API Operations to Create, Update, and Debug Your Training Job (https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html) // . DebugHookConfig *DebugHookConfig // Information about the debug rule configuration. DebugRuleConfigurations []DebugRuleConfiguration // Information about the evaluation status of the rules for the training job. DebugRuleEvaluationStatuses []DebugRuleEvaluationStatus // To encrypt all communications between ML compute instances in distributed // training, choose True . Encryption provides greater security for distributed // training, but training might take longer. How long it takes depends on the // amount of communication between compute instances, especially if you use a deep // learning algorithm in distributed training. EnableInterContainerTrafficEncryption bool // When true, enables managed spot training using Amazon EC2 Spot instances to run // training jobs instead of on-demand instances. For more information, see Managed // Spot Training (https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html) // . EnableManagedSpotTraining bool // If the TrainingJob was created with network isolation, the value is set to true // . If network isolation is enabled, nodes can't communicate beyond the VPC they // run in. EnableNetworkIsolation bool // The environment variables to set in the Docker container. Environment map[string]string // Associates a SageMaker job as a trial component with an experiment and trial. // Specified when you call the following APIs: // - CreateProcessingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html) // - CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) // - CreateTransformJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html) ExperimentConfig *ExperimentConfig // If the training job failed, the reason it failed. FailureReason *string // A list of final metric values that are set when the training job completes. // Used only if the training job was configured to use metrics. FinalMetricDataList []MetricData // Algorithm-specific parameters. HyperParameters map[string]string // An array of Channel objects that describes each data input channel. Your input // must be in the same Amazon Web Services region as your training job. InputDataConfig []Channel // The Amazon Resource Name (ARN) of the labeling job. LabelingJobArn *string // A timestamp that indicates when the status of the training job was last // modified. LastModifiedTime *time.Time // Information about the Amazon S3 location that is configured for storing model // artifacts. ModelArtifacts *ModelArtifacts // The S3 path where model artifacts that you configured when creating the job are // stored. SageMaker creates subfolders for model artifacts. OutputDataConfig *OutputDataConfig // Configuration information for Amazon SageMaker Debugger system monitoring, // framework profiling, and storage paths. ProfilerConfig *ProfilerConfig // Resources, including ML compute instances and ML storage volumes, that are // configured for model training. ResourceConfig *ResourceConfig // The number of times to retry the job when the job fails due to an // InternalServerError . RetryStrategy *RetryStrategy // The Amazon Web Services Identity and Access Management (IAM) role configured // for the training job. RoleArn *string // Provides detailed information about the state of the training job. For detailed // information about the secondary status of the training job, see StatusMessage // under SecondaryStatusTransition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_SecondaryStatusTransition.html) // . SageMaker provides primary statuses and secondary statuses that apply to each // of them: InProgress // - Starting - Starting the training job. // - Downloading - An optional stage for algorithms that support File training // input mode. It indicates that data is being downloaded to the ML storage // volumes. // - Training - Training is in progress. // - Uploading - Training is complete and the model artifacts are being uploaded // to the S3 location. // Completed // - Completed - The training job has completed. // Failed // - Failed - The training job has failed. The reason for the failure is returned // in the FailureReason field of DescribeTrainingJobResponse . // Stopped // - MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed // runtime. // - Stopped - The training job has stopped. // Stopping // - Stopping - Stopping the training job. // Valid values for SecondaryStatus are subject to change. We no longer support // the following secondary statuses: // - LaunchingMLInstances // - PreparingTrainingStack // - DownloadingTrainingImage SecondaryStatus SecondaryStatus // A history of all of the secondary statuses that the training job has // transitioned through. SecondaryStatusTransitions []SecondaryStatusTransition // Specifies a limit to how long a model training job can run. It also specifies // how long a managed Spot training job has to complete. When the job reaches the // time limit, SageMaker ends the training job. Use this API to cap model training // costs. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which // delays job termination for 120 seconds. Algorithms can use this 120-second // window to save the model artifacts, so the results of training are not lost. StoppingCondition *StoppingCondition // An array of key-value pairs. You can use tags to categorize your Amazon Web // Services resources in different ways, for example, by purpose, owner, or // environment. For more information, see Tagging Amazon Web Services Resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html) // . Tags []Tag // Configuration of storage locations for the Amazon SageMaker Debugger // TensorBoard output data. TensorBoardOutputConfig *TensorBoardOutputConfig // Indicates the time when the training job ends on training instances. You are // billed for the time interval between the value of TrainingStartTime and this // time. For successful jobs and stopped jobs, this is the time after model // artifacts are uploaded. For failed jobs, this is the time when SageMaker detects // a job failure. TrainingEndTime *time.Time // The Amazon Resource Name (ARN) of the training job. TrainingJobArn *string // The name of the training job. TrainingJobName *string // The status of the training job. Training job statuses are: // - InProgress - The training is in progress. // - Completed - The training job has completed. // - Failed - The training job has failed. To see the reason for the failure, see // the FailureReason field in the response to a DescribeTrainingJobResponse call. // - Stopping - The training job is stopping. // - Stopped - The training job has stopped. // For more detailed information, see SecondaryStatus . TrainingJobStatus TrainingJobStatus // Indicates the time when the training job starts on training instances. You are // billed for the time interval between this time and the value of TrainingEndTime // . The start time in CloudWatch Logs might be later than this time. The // difference is due to the time it takes to download the training data and to the // size of the training container. TrainingStartTime *time.Time // The training time in seconds. TrainingTimeInSeconds *int32 // The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if // the training job was launched by a hyperparameter tuning job. TuningJobArn *string // A VpcConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html) // object that specifies the VPC that this training job has access to. For more // information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html) // . VpcConfig *VpcConfig noSmithyDocumentSerde } // Defines the input needed to run a training job using the algorithm. type TrainingJobDefinition struct { // An array of Channel objects, each of which specifies an input source. // // This member is required. InputDataConfig []Channel // the path to the S3 bucket where you want to store model artifacts. SageMaker // creates subfolders for the artifacts. // // This member is required. OutputDataConfig *OutputDataConfig // The resources, including the ML compute instances and ML storage volumes, to // use for model training. // // This member is required. ResourceConfig *ResourceConfig // Specifies a limit to how long a model training job can run. It also specifies // how long a managed Spot training job has to complete. When the job reaches the // time limit, SageMaker ends the training job. Use this API to cap model training // costs. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which // delays job termination for 120 seconds. Algorithms can use this 120-second // window to save the model artifacts. // // This member is required. StoppingCondition *StoppingCondition // The 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 hyperparameters used for the training job. HyperParameters map[string]string noSmithyDocumentSerde } // The numbers of training jobs launched by a hyperparameter tuning job, // categorized by status. type TrainingJobStatusCounters struct { // The number of completed training jobs launched by the hyperparameter tuning job. Completed int32 // The number of in-progress training jobs launched by a hyperparameter tuning job. InProgress int32 // The number of training jobs that failed and can't be retried. A failed training // job can't be retried if it failed because a client error occurred. NonRetryableError int32 // The number of training jobs that failed, but can be retried. A failed training // job can be retried only if it failed because an internal service error occurred. RetryableError int32 // The number of training jobs launched by a hyperparameter tuning job that were // manually stopped. Stopped int32 noSmithyDocumentSerde } // Metadata for a training job step. type TrainingJobStepMetadata struct { // The Amazon Resource Name (ARN) of the training job that was run by this step // execution. Arn *string noSmithyDocumentSerde } // Provides summary information about a training job. type TrainingJobSummary struct { // A timestamp that shows when the training job was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the training job. // // This member is required. TrainingJobArn *string // The name of the training job that you want a summary for. // // This member is required. TrainingJobName *string // The status of the training job. // // This member is required. TrainingJobStatus TrainingJobStatus // Timestamp when the training job was last modified. LastModifiedTime *time.Time // A timestamp that shows when the training job ended. This field is set only if // the training job has one of the terminal statuses ( Completed , Failed , or // Stopped ). TrainingEndTime *time.Time // The status of the warm pool associated with the training job. WarmPoolStatus *WarmPoolStatus noSmithyDocumentSerde } // An object containing authentication information for a private Docker registry. type TrainingRepositoryAuthConfig struct { // The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used // to give SageMaker access credentials to your private Docker registry. // // This member is required. TrainingRepositoryCredentialsProviderArn *string noSmithyDocumentSerde } // Defines how the algorithm is used for a training job. type TrainingSpecification struct { // A list of the instance types that this algorithm can use for training. // // This member is required. SupportedTrainingInstanceTypes []TrainingInstanceType // A list of ChannelSpecification objects, which specify the input sources to be // used by the algorithm. // // This member is required. TrainingChannels []ChannelSpecification // The Amazon ECR registry path of the Docker image that contains the training // algorithm. // // This member is required. TrainingImage *string // A list of MetricDefinition objects, which are used for parsing metrics // generated by the algorithm. MetricDefinitions []MetricDefinition // A list of the HyperParameterSpecification objects, that define the supported // hyperparameters. This is required if the algorithm supports automatic model // tuning.> SupportedHyperParameters []HyperParameterSpecification // A list of the metrics that the algorithm emits that can be used as the // objective metric in a hyperparameter tuning job. SupportedTuningJobObjectiveMetrics []HyperParameterTuningJobObjective // Indicates whether the algorithm supports distributed training. If set to false, // buyers can't request more than one instance during training. SupportsDistributedTraining bool // An MD5 hash of the training algorithm that identifies the Docker image used for // training. TrainingImageDigest *string noSmithyDocumentSerde } // Describes the location of the channel data. type TransformDataSource struct { // The S3 location of the data source that is associated with a channel. // // This member is required. S3DataSource *TransformS3DataSource noSmithyDocumentSerde } // Describes the input source of a transform job and the way the transform job // consumes it. type TransformInput struct { // Describes the location of the channel data, which is, the S3 location of the // input data that the model can consume. // // This member is required. DataSource *TransformDataSource // If your transform data is compressed, specify the compression type. Amazon // SageMaker automatically decompresses the data for the transform job accordingly. // The default value is None . CompressionType CompressionType // The multipurpose internet mail extension (MIME) type of the data. Amazon // SageMaker uses the MIME type with each http call to transfer data to the // transform job. ContentType *string // The method to use to split the transform job's data files into smaller batches. // Splitting is necessary when the total size of each object is too large to fit in // a single request. You can also use data splitting to improve performance by // processing multiple concurrent mini-batches. The default value for SplitType is // None , which indicates that input data files are not split, and request payloads // contain the entire contents of an input object. Set the value of this parameter // to Line to split records on a newline character boundary. SplitType also // supports a number of record-oriented binary data formats. Currently, the // supported record formats are: // - RecordIO // - TFRecord // When splitting is enabled, the size of a mini-batch depends on the values of // the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy // is MultiRecord , Amazon SageMaker sends the maximum number of records in each // request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is // SingleRecord , Amazon SageMaker sends individual records in each request. Some // data formats represent a record as a binary payload wrapped with extra padding // bytes. When splitting is applied to a binary data format, padding is removed if // the value of BatchStrategy is set to SingleRecord . Padding is not removed if // the value of BatchStrategy is set to MultiRecord . For more information about // RecordIO , see Create a Dataset Using RecordIO (https://mxnet.apache.org/api/faq/recordio) // in the MXNet documentation. For more information about TFRecord , see Consuming // TFRecord data (https://www.tensorflow.org/guide/data#consuming_tfrecord_data) in // the TensorFlow documentation. SplitType SplitType noSmithyDocumentSerde } // A batch transform job. For information about SageMaker batch transform, see Use // Batch Transform (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html) // . type TransformJob struct { // The Amazon Resource Name (ARN) of the AutoML job that created the transform job. AutoMLJobArn *string // Specifies the number of records to include in a mini-batch for an HTTP // inference request. A record is a single unit of input data that inference can be // made on. For example, a single line in a CSV file is a record. BatchStrategy BatchStrategy // A timestamp that shows when the transform Job was created. CreationTime *time.Time // The data structure used to specify the data to be used for inference in a batch // transform job and to associate the data that is relevant to the prediction // results in the output. The input filter provided allows you to exclude input // data that is not needed for inference in a batch transform job. The output // filter provided allows you to include input data relevant to interpreting the // predictions in the output from the job. For more information, see Associate // Prediction Results with their Corresponding Input Records (https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html) // . DataProcessing *DataProcessing // The environment variables to set in the Docker container. We support up to 16 // key and values entries in the map. Environment map[string]string // Associates a SageMaker job as a trial component with an experiment and trial. // Specified when you call the following APIs: // - CreateProcessingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateProcessingJob.html) // - CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) // - CreateTransformJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html) ExperimentConfig *ExperimentConfig // If the transform job failed, the reason it failed. FailureReason *string // The Amazon Resource Name (ARN) of the labeling job that created the transform // job. LabelingJobArn *string // The maximum number of parallel requests that can be sent to each instance in a // transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker // checks the optional execution-parameters to determine the settings for your // chosen algorithm. If the execution-parameters endpoint is not enabled, the // default value is 1. For built-in algorithms, you don't need to set a value for // MaxConcurrentTransforms . MaxConcurrentTransforms *int32 // The maximum allowed size of the payload, in MB. A payload is the data portion // of a record (without metadata). The value in MaxPayloadInMB must be greater // than, or equal to, the size of a single record. To estimate the size of a record // in MB, divide the size of your dataset by the number of records. To ensure that // the records fit within the maximum payload size, we recommend using a slightly // larger value. The default value is 6 MB. For cases where the payload might be // arbitrarily large and is transmitted using HTTP chunked encoding, set the value // to 0. This feature works only in supported algorithms. Currently, SageMaker // built-in algorithms do not support HTTP chunked encoding. MaxPayloadInMB *int32 // Configures the timeout and maximum number of retries for processing a transform // job invocation. ModelClientConfig *ModelClientConfig // The name of the model associated with the transform job. ModelName *string // A list of tags associated with the transform job. Tags []Tag // Indicates when the transform job has been completed, or has stopped or failed. // You are billed for the time interval between this time and the value of // TransformStartTime . TransformEndTime *time.Time // Describes the input source of a transform job and the way the transform job // consumes it. TransformInput *TransformInput // The Amazon Resource Name (ARN) of the transform job. TransformJobArn *string // The name of the transform job. TransformJobName *string // The status of the transform job. Transform job statuses are: // - InProgress - The job is in progress. // - Completed - The job has completed. // - Failed - The transform job has failed. To see the reason for the failure, // see the FailureReason field in the response to a DescribeTransformJob call. // - Stopping - The transform job is stopping. // - Stopped - The transform job has stopped. TransformJobStatus TransformJobStatus // Describes the results of a transform job. TransformOutput *TransformOutput // Describes the resources, including ML instance types and ML instance count, to // use for transform job. TransformResources *TransformResources // Indicates when the transform job starts on ML instances. You are billed for the // time interval between this time and the value of TransformEndTime . TransformStartTime *time.Time noSmithyDocumentSerde } // Defines the input needed to run a transform job using the inference // specification specified in the algorithm. type TransformJobDefinition struct { // A description of the input source and the way the transform job consumes it. // // This member is required. TransformInput *TransformInput // Identifies the Amazon S3 location where you want Amazon SageMaker to save the // results from the transform job. // // This member is required. TransformOutput *TransformOutput // Identifies the ML compute instances for the transform job. // // This member is required. TransformResources *TransformResources // A string that determines the number of records included in a single mini-batch. // SingleRecord means only one record is used per mini-batch. MultiRecord means a // mini-batch is set to contain as many records that can fit within the // MaxPayloadInMB limit. BatchStrategy BatchStrategy // The environment variables to set in the Docker container. We support up to 16 // key and values entries in the map. Environment map[string]string // The maximum number of parallel requests that can be sent to each instance in a // transform job. The default value is 1. MaxConcurrentTransforms *int32 // The maximum payload size allowed, in MB. A payload is the data portion of a // record (without metadata). MaxPayloadInMB *int32 noSmithyDocumentSerde } // Metadata for a transform job step. type TransformJobStepMetadata struct { // The Amazon Resource Name (ARN) of the transform job that was run by this step // execution. Arn *string noSmithyDocumentSerde } // Provides a summary of a transform job. Multiple TransformJobSummary objects are // returned as a list after in response to a ListTransformJobs (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListTransformJobs.html) // call. type TransformJobSummary struct { // A timestamp that shows when the transform Job was created. // // This member is required. CreationTime *time.Time // The Amazon Resource Name (ARN) of the transform job. // // This member is required. TransformJobArn *string // The name of the transform job. // // This member is required. TransformJobName *string // The status of the transform job. // // This member is required. TransformJobStatus TransformJobStatus // If the transform job failed, the reason it failed. FailureReason *string // Indicates when the transform job was last modified. LastModifiedTime *time.Time // Indicates when the transform job ends on compute instances. For successful jobs // and stopped jobs, this is the exact time recorded after the results are // uploaded. For failed jobs, this is when Amazon SageMaker detected that the job // failed. TransformEndTime *time.Time noSmithyDocumentSerde } // Describes the results of a transform job. type TransformOutput struct { // The Amazon S3 path where you want Amazon SageMaker to store the results of the // transform job. For example, s3://bucket-name/key-name-prefix . For every S3 // object used as input for the transform job, batch transform stores the // transformed data with an . out suffix in a corresponding subfolder in the // location in the output prefix. For example, for the input data stored at // s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores // the transformed data at // s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch // transform doesn't upload partially processed objects. For an input S3 object // that contains multiple records, it creates an . out file only if the transform // job succeeds on the entire file. When the input contains multiple S3 objects, // the batch transform job processes the listed S3 objects and uploads only the // output for successfully processed objects. If any object fails in the transform // job batch transform marks the job as failed to prompt investigation. // // This member is required. S3OutputPath *string // The MIME type used to specify the output data. Amazon SageMaker uses the MIME // type with each http call to transfer data from the transform job. Accept *string // Defines how to assemble the results of the transform job as a single S3 object. // Choose a format that is most convenient to you. To concatenate the results in // binary format, specify None . To add a newline character at the end of every // transformed record, specify Line . AssembleWith AssemblyType // The Amazon Web Services Key Management Service (Amazon Web Services KMS) key // that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon // S3 server-side encryption. The KmsKeyId can be any of the following formats: // - Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab // - Key ARN: // arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab // - Alias name: alias/ExampleAlias // - Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias // If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key // for Amazon S3 for your role's account. For more information, see KMS-Managed // Encryption Keys (https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html) // in the Amazon Simple Storage Service Developer Guide. The KMS key policy must // grant permission to the IAM role that you specify in your CreateModel (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html) // request. For more information, see Using Key Policies in Amazon Web Services KMS (https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html) // in the Amazon Web Services Key Management Service Developer Guide. KmsKeyId *string noSmithyDocumentSerde } // Describes the resources, including ML instance types and ML instance count, to // use for transform job. type TransformResources struct { // The number of ML compute instances to use in the transform job. The default // value is 1 , and the maximum is 100 . For distributed transform jobs, specify a // value greater than 1 . // // This member is required. InstanceCount *int32 // The ML compute instance type for the transform job. If you are using built-in // algorithms to transform moderately sized datasets, we recommend using // ml.m4.xlarge or ml.m5.large instance types. // // This member is required. InstanceType TransformInstanceType // The Amazon Web Services Key Management Service (Amazon Web Services KMS) key // that Amazon SageMaker uses to encrypt model data on the storage volume attached // to the ML compute instance(s) that run the batch transform job. Certain // Nitro-based instances include local storage, dependent on the instance type. // Local storage volumes are encrypted using a hardware module on the instance. You // can't request a VolumeKmsKeyId when using an instance type with local storage. // For a list of instance types that support local instance storage, see Instance // Store Volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes) // . For more information about local instance storage encryption, see SSD // Instance Store Volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html) // . The VolumeKmsKeyId can be any of the following formats: // - Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab // - Key ARN: // arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab // - Alias name: alias/ExampleAlias // - Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias VolumeKmsKeyId *string noSmithyDocumentSerde } // Describes the S3 data source. type TransformS3DataSource struct { // If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker // uses all objects with the specified key name prefix for batch transform. If you // choose ManifestFile , S3Uri identifies an object that is a manifest file // containing a list of object keys that you want Amazon SageMaker to use for batch // transform. The following values are compatible: ManifestFile , S3Prefix The // following value is not compatible: AugmentedManifestFile // // This member is required. S3DataType S3DataType // Depending on the value specified for the S3DataType , identifies either a key // name prefix or a manifest. For example: // - A key name prefix might look like this: s3://bucketname/exampleprefix . // - A manifest might look like this: s3://bucketname/example.manifest The // manifest is an S3 object which is a JSON file with the following format: [ // {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", // "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding // JSON matches the following S3Uris : // s3://customer_bucket/some/prefix/relative/path/to/custdata-1 // s3://customer_bucket/some/prefix/relative/path/custdata-2 ... // s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of // S3Uris in this manifest constitutes the input data for the channel for this // datasource. The object that each S3Uris points to must be readable by the IAM // role that Amazon SageMaker uses to perform tasks on your behalf. // // This member is required. S3Uri *string noSmithyDocumentSerde } // The properties of a trial as returned by the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) // API. type Trial struct { // Who created the trial. CreatedBy *UserContext // When the trial was created. CreationTime *time.Time // The name of the trial as displayed. If DisplayName isn't specified, TrialName // is displayed. DisplayName *string // The name of the experiment the trial is part of. ExperimentName *string // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. LastModifiedBy *UserContext // Who last modified the trial. LastModifiedTime *time.Time // Metadata properties of the tracking entity, trial, or trial component. MetadataProperties *MetadataProperties // The source of the trial. Source *TrialSource // The list of tags that are associated with the trial. You can use Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) // API to search on the tags. Tags []Tag // The Amazon Resource Name (ARN) of the trial. TrialArn *string // A list of the components associated with the trial. For each component, a // summary of the component's properties is included. TrialComponentSummaries []TrialComponentSimpleSummary // The name of the trial. TrialName *string noSmithyDocumentSerde } // The properties of a trial component as returned by the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) // API. type TrialComponent struct { // Who created the trial component. CreatedBy *UserContext // When the component was created. CreationTime *time.Time // The name of the component as displayed. If DisplayName isn't specified, // TrialComponentName is displayed. DisplayName *string // When the component ended. EndTime *time.Time // The input artifacts of the component. InputArtifacts map[string]TrialComponentArtifact // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. LastModifiedBy *UserContext // When the component was last modified. LastModifiedTime *time.Time // The Amazon Resource Name (ARN) of the lineage group resource. LineageGroupArn *string // Metadata properties of the tracking entity, trial, or trial component. MetadataProperties *MetadataProperties // The metrics for the component. Metrics []TrialComponentMetricSummary // The output artifacts of the component. OutputArtifacts map[string]TrialComponentArtifact // The hyperparameters of the component. Parameters map[string]TrialComponentParameterValue // An array of the parents of the component. A parent is a trial the component is // associated with and the experiment the trial is part of. A component might not // have any parents. Parents []Parent // The name of the experiment run. RunName *string // The Amazon Resource Name (ARN) and job type of the source of the component. Source *TrialComponentSource // Details of the source of the component. SourceDetail *TrialComponentSourceDetail // When the component started. StartTime *time.Time // The status of the trial component. Status *TrialComponentStatus // The list of tags that are associated with the component. You can use Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) // API to search on the tags. Tags []Tag // The Amazon Resource Name (ARN) of the trial component. TrialComponentArn *string // The name of the trial component. TrialComponentName *string noSmithyDocumentSerde } // Represents an input or output artifact of a trial component. You specify // TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts // parameters in the CreateTrialComponent (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrialComponent.html) // request. Examples of input artifacts are datasets, algorithms, hyperparameters, // source code, and instance types. Examples of output artifacts are metrics, // snapshots, logs, and images. type TrialComponentArtifact struct { // The location of the artifact. // // This member is required. Value *string // The media type of the artifact, which indicates the type of data in the // artifact file. The media type consists of a type and a subtype concatenated with // a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type // specifies the category of the media. The subtype specifies the kind of data. MediaType *string noSmithyDocumentSerde } // A summary of the metrics of a trial component. type TrialComponentMetricSummary struct { // The average value of the metric. Avg *float64 // The number of samples used to generate the metric. Count *int32 // The most recent value of the metric. Last *float64 // The maximum value of the metric. Max *float64 // The name of the metric. MetricName *string // The minimum value of the metric. Min *float64 // The Amazon Resource Name (ARN) of the source. SourceArn *string // The standard deviation of the metric. StdDev *float64 // When the metric was last updated. TimeStamp *time.Time noSmithyDocumentSerde } // The value of a hyperparameter. Only one of NumberValue or StringValue can be // specified. This object is specified in the CreateTrialComponent (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrialComponent.html) // request. // // The following types satisfy this interface: // // TrialComponentParameterValueMemberNumberValue // TrialComponentParameterValueMemberStringValue type TrialComponentParameterValue interface { isTrialComponentParameterValue() } // The numeric value of a numeric hyperparameter. If you specify a value for this // parameter, you can't specify the StringValue parameter. type TrialComponentParameterValueMemberNumberValue struct { Value float64 noSmithyDocumentSerde } func (*TrialComponentParameterValueMemberNumberValue) isTrialComponentParameterValue() {} // The string value of a categorical hyperparameter. If you specify a value for // this parameter, you can't specify the NumberValue parameter. type TrialComponentParameterValueMemberStringValue struct { Value string noSmithyDocumentSerde } func (*TrialComponentParameterValueMemberStringValue) isTrialComponentParameterValue() {} // A short summary of a trial component. type TrialComponentSimpleSummary struct { // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. CreatedBy *UserContext // When the component was created. CreationTime *time.Time // The Amazon Resource Name (ARN) of the trial component. TrialComponentArn *string // The name of the trial component. TrialComponentName *string // The Amazon Resource Name (ARN) and job type of the source of a trial component. TrialComponentSource *TrialComponentSource noSmithyDocumentSerde } // The Amazon Resource Name (ARN) and job type of the source of a trial component. type TrialComponentSource struct { // The source Amazon Resource Name (ARN). // // This member is required. SourceArn *string // The source job type. SourceType *string noSmithyDocumentSerde } // Detailed information about the source of a trial component. Either ProcessingJob // or TrainingJob is returned. type TrialComponentSourceDetail struct { // Information about a processing job that's the source of a trial component. ProcessingJob *ProcessingJob // The Amazon Resource Name (ARN) of the source. SourceArn *string // Information about a training job that's the source of a trial component. TrainingJob *TrainingJob // Information about a transform job that's the source of a trial component. TransformJob *TransformJob noSmithyDocumentSerde } // The status of the trial component. type TrialComponentStatus struct { // If the component failed, a message describing why. Message *string // The status of the trial component. PrimaryStatus TrialComponentPrimaryStatus noSmithyDocumentSerde } // A summary of the properties of a trial component. To get all the properties, // call the DescribeTrialComponent (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrialComponent.html) // API and provide the TrialComponentName . type TrialComponentSummary struct { // Who created the trial component. CreatedBy *UserContext // When the component was created. CreationTime *time.Time // The name of the component as displayed. If DisplayName isn't specified, // TrialComponentName is displayed. DisplayName *string // When the component ended. EndTime *time.Time // Who last modified the component. LastModifiedBy *UserContext // When the component was last modified. LastModifiedTime *time.Time // When the component started. StartTime *time.Time // The status of the component. States include: // - InProgress // - Completed // - Failed Status *TrialComponentStatus // The Amazon Resource Name (ARN) of the trial component. TrialComponentArn *string // The name of the trial component. TrialComponentName *string // The Amazon Resource Name (ARN) and job type of the source of a trial component. TrialComponentSource *TrialComponentSource noSmithyDocumentSerde } // The source of the trial. type TrialSource struct { // The Amazon Resource Name (ARN) of the source. // // This member is required. SourceArn *string // The source job type. SourceType *string noSmithyDocumentSerde } // A summary of the properties of a trial. To get the complete set of properties, // call the DescribeTrial (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrial.html) // API and provide the TrialName . type TrialSummary struct { // When the trial was created. CreationTime *time.Time // The name of the trial as displayed. If DisplayName isn't specified, TrialName // is displayed. DisplayName *string // When the trial was last modified. LastModifiedTime *time.Time // The Amazon Resource Name (ARN) of the trial. TrialArn *string // The name of the trial. TrialName *string // The source of the trial. TrialSource *TrialSource noSmithyDocumentSerde } // Time to live duration, where the record is hard deleted after the expiration // time is reached; ExpiresAt = EventTime + TtlDuration . For information on // HardDelete, see the DeleteRecord (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_feature_store_DeleteRecord.html) // API in the Amazon SageMaker API Reference guide. type TtlDuration struct { // TtlDuration time unit. Unit TtlDurationUnit // TtlDuration time value. Value *int32 noSmithyDocumentSerde } // The job completion criteria. type TuningJobCompletionCriteria struct { // A flag to stop your hyperparameter tuning job if model performance fails to // improve as evaluated against an objective function. BestObjectiveNotImproving *BestObjectiveNotImproving // A flag to top your hyperparameter tuning job if automatic model tuning (AMT) // has detected that your model has converged as evaluated against your objective // function. ConvergenceDetected *ConvergenceDetected // The value of the objective metric. TargetObjectiveMetricValue *float32 noSmithyDocumentSerde } // Metadata for a tuning step. type TuningJobStepMetaData struct { // The Amazon Resource Name (ARN) of the tuning job that was run by this step // execution. Arn *string noSmithyDocumentSerde } // Provided configuration information for the worker UI for a labeling job. // Provide either HumanTaskUiArn or UiTemplateS3Uri . For named entity recognition, // 3D point cloud and video frame labeling jobs, use HumanTaskUiArn . For all other // Ground Truth built-in task types and custom task types, use UiTemplateS3Uri to // specify the location of a worker task template in Amazon S3. type UiConfig struct { // The ARN of the worker task template used to render the worker UI and tools for // labeling job tasks. Use this parameter when you are creating a labeling job for // named entity recognition, 3D point cloud and video frame labeling jobs. Use your // labeling job task type to select one of the following ARNs and use it with this // parameter when you create a labeling job. Replace aws-region with the Amazon // Web Services Region you are creating your labeling job in. For example, replace // aws-region with us-west-1 if you create a labeling job in US West (N. // California). Named Entity Recognition Use the following HumanTaskUiArn for // named entity recognition labeling jobs: // arn:aws:sagemaker:aws-region:394669845002:human-task-ui/NamedEntityRecognition // 3D Point Cloud HumanTaskUiArns Use this HumanTaskUiArn for 3D point cloud // object detection and 3D point cloud object detection adjustment labeling jobs. // - // arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection // Use this HumanTaskUiArn for 3D point cloud object tracking and 3D point cloud // object tracking adjustment labeling jobs. // - // arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking // Use this HumanTaskUiArn for 3D point cloud semantic segmentation and 3D point // cloud semantic segmentation adjustment labeling jobs. // - // arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation // Video Frame HumanTaskUiArns Use this HumanTaskUiArn for video frame object // detection and video frame object detection adjustment labeling jobs. // - arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetection // Use this HumanTaskUiArn for video frame object tracking and video frame object // tracking adjustment labeling jobs. // - arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTracking HumanTaskUiArn *string // The Amazon S3 bucket location of the UI template, or worker task template. This // is the template used to render the worker UI and tools for labeling job tasks. // For more information about the contents of a UI template, see Creating Your // Custom Labeling Task Template (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates-step2.html) // . UiTemplateS3Uri *string noSmithyDocumentSerde } // The Liquid template for the worker user interface. type UiTemplate struct { // The content of the Liquid template for the worker user interface. // // This member is required. Content *string noSmithyDocumentSerde } // Container for user interface template information. type UiTemplateInfo struct { // The SHA-256 digest of the contents of the template. ContentSha256 *string // The URL for the user interface template. Url *string noSmithyDocumentSerde } // Represents an amount of money in United States dollars. type USD struct { // The fractional portion, in cents, of the amount. Cents int32 // The whole number of dollars in the amount. Dollars int32 // Fractions of a cent, in tenths. TenthFractionsOfACent int32 noSmithyDocumentSerde } // Information about the user who created or modified an experiment, trial, trial // component, lineage group, project, or model card. type UserContext struct { // The domain associated with the user. DomainId *string // The IAM Identity details associated with the user. These details are associated // with model package groups, model packages, and project entities only. IamIdentity *IamIdentity // The Amazon Resource Name (ARN) of the user's profile. UserProfileArn *string // The name of the user's profile. UserProfileName *string noSmithyDocumentSerde } // The user profile details. type UserProfileDetails struct { // The creation time. CreationTime *time.Time // The domain ID. DomainId *string // The last modified time. LastModifiedTime *time.Time // The status. Status UserProfileStatus // The user profile name. UserProfileName *string noSmithyDocumentSerde } // A collection of settings that apply to users of Amazon SageMaker Studio. These // settings are specified when the CreateUserProfile API is called, and as // DefaultUserSettings when the CreateDomain API is called. SecurityGroups is // aggregated when specified in both calls. For all other settings in UserSettings // , the values specified in CreateUserProfile take precedence over those // specified in CreateDomain . type UserSettings struct { // The Canvas app settings. CanvasAppSettings *CanvasAppSettings // The execution role for the user. ExecutionRole *string // The Jupyter server's app settings. JupyterServerAppSettings *JupyterServerAppSettings // The kernel gateway app settings. KernelGatewayAppSettings *KernelGatewayAppSettings // A collection of settings that configure the RSessionGateway app. RSessionAppSettings *RSessionAppSettings // A collection of settings that configure user interaction with the // RStudioServerPro app. RStudioServerProAppSettings *RStudioServerProAppSettings // The security groups for the Amazon Virtual Private Cloud (VPC) that Studio uses // for communication. Optional when the CreateDomain.AppNetworkAccessType // parameter is set to PublicInternetOnly . Required when the // CreateDomain.AppNetworkAccessType parameter is set to VpcOnly , unless specified // as part of the DefaultUserSettings for the domain. Amazon SageMaker adds a // security group to allow NFS traffic from SageMaker Studio. Therefore, the number // of security groups that you can specify is one less than the maximum number // shown. SecurityGroups []string // Specifies options for sharing SageMaker Studio notebooks. SharingSettings *SharingSettings // The TensorBoard app settings. TensorBoardAppSettings *TensorBoardAppSettings noSmithyDocumentSerde } // Specifies a production variant property type for an Endpoint. If you are // updating an endpoint with the RetainAllVariantProperties option of // UpdateEndpointInput (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) // set to true , the VariantProperty objects listed in the // ExcludeRetainedVariantProperties parameter of UpdateEndpointInput (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) // override the existing variant properties of the endpoint. type VariantProperty struct { // The type of variant property. The supported values are: // - DesiredInstanceCount : Overrides the existing variant instance counts using // the InitialInstanceCount values in the ProductionVariants of // CreateEndpointConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html) // . // - DesiredWeight : Overrides the existing variant weights using the // InitialVariantWeight values in the ProductionVariants of CreateEndpointConfig (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html) // . // - DataCaptureConfig : (Not currently supported.) // // This member is required. VariantPropertyType VariantPropertyType noSmithyDocumentSerde } // A lineage entity connected to the starting entity(ies). type Vertex struct { // The Amazon Resource Name (ARN) of the lineage entity resource. Arn *string // The type of resource of the lineage entity. LineageType LineageType // The type of the lineage entity resource. For example: DataSet , Model , Endpoint // , etc... Type *string noSmithyDocumentSerde } // Specifies a VPC that your training jobs and hosted models have access to. // Control access to and from your training and model containers by configuring the // VPC. For more information, see Protect Endpoints by Using an Amazon Virtual // Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html) // and Protect Training Jobs by Using an Amazon Virtual Private Cloud (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html) // . type VpcConfig struct { // The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security // groups for the VPC that is specified in the Subnets field. // // This member is required. SecurityGroupIds []string // The ID of the subnets in the VPC to which you want to connect your training job // or model. For information about the availability of specific instance types, see // Supported Instance Types and Availability Zones (https://docs.aws.amazon.com/sagemaker/latest/dg/instance-types-az.html) // . // // This member is required. Subnets []string noSmithyDocumentSerde } // Status and billing information about the warm pool. type WarmPoolStatus struct { // The status of the warm pool. // - InUse : The warm pool is in use for the training job. // - Available : The warm pool is available to reuse for a matching training job. // - Reused : The warm pool moved to a matching training job for reuse. // - Terminated : The warm pool is no longer available. Warm pools are // unavailable if they are terminated by a user, terminated for a patch update, or // terminated for exceeding the specified KeepAlivePeriodInSeconds . // // This member is required. Status WarmPoolResourceStatus // The billable time in seconds used by the warm pool. Billable time refers to the // absolute wall-clock time. Multiply ResourceRetainedBillableTimeInSeconds by the // number of instances ( InstanceCount ) in your training cluster to get the total // compute time SageMaker bills you if you run warm pool training. The formula is // as follows: ResourceRetainedBillableTimeInSeconds * InstanceCount . ResourceRetainedBillableTimeInSeconds *int32 // The name of the matching training job that reused the warm pool. ReusedByJob *string noSmithyDocumentSerde } // A single private workforce, which is automatically created when you create your // first private work team. You can create one private work force in each Amazon // Web Services Region. By default, any workforce-related API operation used in a // specific region will apply to the workforce created in that region. To learn how // to create a private workforce, see Create a Private Workforce (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-create-private.html) // . type Workforce struct { // The Amazon Resource Name (ARN) of the private workforce. // // This member is required. WorkforceArn *string // The name of the private workforce. // // This member is required. WorkforceName *string // The configuration of an Amazon Cognito workforce. A single Cognito workforce is // created using and corresponds to a single Amazon Cognito user pool (https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html) // . CognitoConfig *CognitoConfig // The date that the workforce is created. CreateDate *time.Time // The reason your workforce failed. FailureReason *string // The most recent date that UpdateWorkforce (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateWorkforce.html) // was used to successfully add one or more IP address ranges ( CIDRs (https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html) // ) to a private workforce's allow list. LastUpdatedDate *time.Time // The configuration of an OIDC Identity Provider (IdP) private workforce. OidcConfig *OidcConfigForResponse // A list of one to ten IP address ranges ( CIDRs (https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html) // ) to be added to the workforce allow list. By default, a workforce isn't // restricted to specific IP addresses. SourceIpConfig *SourceIpConfig // The status of your workforce. Status WorkforceStatus // The subdomain for your OIDC Identity Provider. SubDomain *string // The configuration of a VPC workforce. WorkforceVpcConfig *WorkforceVpcConfigResponse noSmithyDocumentSerde } // The VPC object you use to create or update a workforce. type WorkforceVpcConfigRequest struct { // The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must // be for the same VPC as specified in the subnet. SecurityGroupIds []string // The ID of the subnets in the VPC that you want to connect. Subnets []string // The ID of the VPC that the workforce uses for communication. VpcId *string noSmithyDocumentSerde } // A VpcConfig object that specifies the VPC that you want your workforce to // connect to. type WorkforceVpcConfigResponse struct { // The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must // be for the same VPC as specified in the subnet. // // This member is required. SecurityGroupIds []string // The ID of the subnets in the VPC that you want to connect. // // This member is required. Subnets []string // The ID of the VPC that the workforce uses for communication. // // This member is required. VpcId *string // The IDs for the VPC service endpoints of your VPC workforce when it is created // and updated. VpcEndpointId *string noSmithyDocumentSerde } // The workspace settings for the SageMaker Canvas application. type WorkspaceSettings struct { // The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the // Amazon S3 location impacts existing configuration settings, and Canvas users no // longer have access to their artifacts. Canvas users must log out and log back in // to apply the new location. S3ArtifactPath *string // The Amazon Web Services Key Management Service (KMS) encryption key ID that is // used to encrypt artifacts generated by Canvas in the Amazon S3 bucket. S3KmsKeyId *string noSmithyDocumentSerde } // Provides details about a labeling work team. type Workteam struct { // A description of the work team. // // This member is required. Description *string // A list of MemberDefinition objects that contains objects that identify the // workers that make up the work team. Workforces can be created using Amazon // Cognito or your own OIDC Identity Provider (IdP). For private workforces created // using Amazon Cognito use CognitoMemberDefinition . For workforces created using // your own OIDC identity provider (IdP) use OidcMemberDefinition . // // This member is required. MemberDefinitions []MemberDefinition // The Amazon Resource Name (ARN) that identifies the work team. // // This member is required. WorkteamArn *string // The name of the work team. // // This member is required. WorkteamName *string // The date and time that the work team was created (timestamp). CreateDate *time.Time // The date and time that the work team was last updated (timestamp). LastUpdatedDate *time.Time // Configures SNS notifications of available or expiring work items for work teams. NotificationConfiguration *NotificationConfiguration // The Amazon Marketplace identifier for a vendor's work team. ProductListingIds []string // The URI of the labeling job's user interface. Workers open this URI to start // labeling your data objects. SubDomain *string // The Amazon Resource Name (ARN) of the workforce. WorkforceArn *string noSmithyDocumentSerde } type noSmithyDocumentSerde = smithydocument.NoSerde // UnknownUnionMember is returned when a union member is returned over the wire, // but has an unknown tag. type UnknownUnionMember struct { Tag string Value []byte noSmithyDocumentSerde } func (*UnknownUnionMember) isAutoMLProblemTypeConfig() {} func (*UnknownUnionMember) isAutoMLProblemTypeResolvedAttributes() {} func (*UnknownUnionMember) isTrialComponentParameterValue() {}