String actionArn
The Amazon Resource Name (ARN) of the action.
String actionName
The name of the action.
ActionSource source
The source of the action.
String actionType
The type of the action.
String status
The status of the action.
Date creationTime
When the action was created.
Date lastModifiedTime
When the action was last modified.
String sourceArn
The ARN of the source.
String destinationArn
The Amazon Resource Name (ARN) of the destination.
String associationType
The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.
ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.
AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.
DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.
Produced - The source generated the destination. For example, a training job produced a model artifact.
String name
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.
String description
A description of the additional Inference specification
List<E> containers
The Amazon ECR registry path of the Docker image that contains the inference code.
List<E> supportedTransformInstanceTypes
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
List<E> supportedRealtimeInferenceInstanceTypes
A list of the instance types that are used to generate inferences in real-time.
List<E> supportedContentTypes
The supported MIME types for the input data.
List<E> supportedResponseMIMETypes
The supported MIME types for the output data.
String resourceArn
The Amazon Resource Name (ARN) of the resource that you want to tag.
List<E> tags
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.
String alarmName
The name of a CloudWatch alarm in your account.
String trainingImage
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. Amazon SageMaker supports both
registry/repository[:tag] and registry/repository[@digest] image path formats. For more
information, see Using Your Own
Algorithms with Amazon SageMaker.
String algorithmName
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. If you specify a value for this parameter, you can't
specify a value for TrainingImage.
String trainingInputMode
List<E> metricDefinitions
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
Boolean enableSageMakerMetricsTimeSeries
To generate and save time-series metrics during training, set to true. The default is
false and time-series metrics aren't generated except in the following cases:
You use one of the Amazon SageMaker built-in algorithms
You use one of the following Prebuilt Amazon SageMaker Docker Images:
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
String algorithmName
The name of the algorithm that is described by the summary.
String algorithmArn
The Amazon Resource Name (ARN) of the algorithm.
String algorithmDescription
A brief description of the algorithm.
Date creationTime
A timestamp that shows when the algorithm was created.
String algorithmStatus
The overall status of the algorithm.
String profileName
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).
TrainingJobDefinition trainingJobDefinition
The TrainingJobDefinition object that describes the training job that Amazon SageMaker runs to
validate your algorithm.
TransformJobDefinition transformJobDefinition
The TransformJobDefinition object that describes the transform job that Amazon SageMaker runs to
validate your algorithm.
String validationRole
The IAM roles that Amazon SageMaker uses to run the training jobs.
List<E> validationProfiles
An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch
transform job that Amazon SageMaker runs to validate your algorithm.
String annotationConsolidationLambdaArn
The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.
This parameter is required for all labeling jobs. For built-in task types, use one of
the following Amazon SageMaker Ground Truth Lambda function ARNs for
AnnotationConsolidationLambdaArn. For custom labeling workflows, see Post-annotation Lambda.
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 .
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
String appImageConfigArn
The Amazon Resource Name (ARN) of the AppImageConfig.
String appImageConfigName
The name of the AppImageConfig. Must be unique to your account.
Date creationTime
When the AppImageConfig was created.
Date lastModifiedTime
When the AppImageConfig was last modified.
KernelGatewayImageConfig kernelGatewayImageConfig
The configuration for the file system and kernels in the SageMaker image.
String artifactArn
The Amazon Resource Name (ARN) of the artifact.
String artifactName
The name of the artifact.
ArtifactSource source
The source of the artifact.
String artifactType
The type of the artifact.
Date creationTime
When the artifact was created.
Date lastModifiedTime
When the artifact was last modified.
String sourceArn
The ARN of the source.
String destinationArn
The Amazon Resource Name (ARN) of the destination.
String sourceType
The source type.
String destinationType
The destination type.
String associationType
The type of the association.
String sourceName
The name of the source.
String destinationName
The name of the destination.
Date creationTime
When the association was created.
UserContext createdBy
Integer maxConcurrentInvocationsPerInstance
The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, Amazon SageMaker will choose an optimal value for you.
AsyncInferenceClientConfig clientConfig
Configures the behavior of the client used by Amazon SageMaker to interact with the model container during asynchronous inference.
AsyncInferenceOutputConfig outputConfig
Specifies the configuration for asynchronous inference invocation outputs.
String successTopic
Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
String errorTopic
Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
String kmsKeyId
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
String s3OutputPath
The Amazon S3 location to upload inference responses to.
AsyncInferenceNotificationConfig notificationConfig
Specifies the configuration for notifications of inference results for asynchronous inference.
String catalog
String database
String queryString
String workGroup
String outputS3Uri
The location in Amazon S3 where Athena query results are stored.
String kmsKeyId
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.
String outputFormat
String outputCompression
String candidateName
The name of the candidate.
FinalAutoMLJobObjectiveMetric finalAutoMLJobObjectiveMetric
String objectiveStatus
The objective's status.
List<E> candidateSteps
Information about the candidate's steps.
String candidateStatus
The candidate's status.
List<E> inferenceContainers
Information about the inference container definitions.
Date creationTime
The creation time.
Date endTime
The end time.
Date lastModifiedTime
The last modified time.
String failureReason
The failure reason.
CandidateProperties candidateProperties
The properties of an AutoML candidate job.
AutoMLDataSource dataSource
The data source for an AutoML channel.
String compressionType
You can use Gzip or None. The default value is None.
String targetAttributeName
The name of the target variable in supervised learning, usually represented by 'y'.
String contentType
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.
String image
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see .
String modelDataUrl
The location of the model artifacts. For more information, see .
Map<K,V> environment
The environment variables to set in the container. For more information, see .
AutoMLS3DataSource s3DataSource
The Amazon S3 location of the input data.
The input data must be in CSV format and contain at least 500 rows.
Integer maxCandidates
The maximum number of times a training job is allowed to run.
Integer maxRuntimePerTrainingJobInSeconds
The maximum time, in seconds, that each training job is allowed to run as part of a hyperparameter tuning job. For more information, see the used by the action.
Integer maxAutoMLJobRuntimeInSeconds
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, will not be completed.
AutoMLJobCompletionCriteria completionCriteria
How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
AutoMLSecurityConfig securityConfig
The security configuration for traffic encryption or Amazon VPC settings.
String metricName
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
MSE: The mean squared error (MSE) is the average of the squared differences between the predicted
and actual values. It is used for regression. MSE values are always positive: the better a model is at predicting
the actual values, the smaller the MSE value is. When the data contains outliers, they tend to dominate the MSE,
which might cause subpar prediction performance.
Accuracy: The ratio of the number of correctly classified items to the total number of (correctly
and incorrectly) classified items. It is used for binary and multiclass classification. It measures how close the
predicted class values are to the actual values. Accuracy values vary between zero and one: one indicates perfect
accuracy and zero indicates perfect inaccuracy.
F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
classification into classes traditionally referred to as positive and negative. Predictions are said to be true
when they match their actual (correct) class and false when they do not. Precision is the ratio of the true
positive predictions to all positive predictions (including the false positives) in a data set and measures the
quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true
positive predictions to all actual positive instances and measures how completely a model predicts the actual
class members in a data set. The standard F1 score weighs precision and recall equally. But which metric is
paramount typically depends on specific aspects of a problem. F1 scores vary between zero and one: one indicates
the best possible performance and zero the worst.
AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by
algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities
into classifications. The relevant curve is the receiver operating characteristic curve that plots the true
positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the
threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false
positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so
provides an aggregated measure of the model performance across all possible classification thresholds. The AUC
score can also be interpreted as the probability that a randomly selected positive data point is more likely to
be predicted positive than a randomly selected negative example. AUC scores vary between zero and one: a score of
one indicates perfect accuracy and a score of one half indicates that the prediction is not better than a random
classifier. Values under one half predict less accurately than a random predictor. But such consistently bad
predictors can simply be inverted to obtain better than random predictors.
F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you
have multiple classes to predict. You just calculate the precision and recall for each class as you did for the
positive class in binary classification. Then, use these values to calculate the F1 score for each class and
average them to obtain the F1macro score. F1macro scores vary between zero and one: one indicates the best
possible performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
String autoMLJobName
The name of the AutoML job you are requesting.
String autoMLJobArn
The ARN of the AutoML job.
String autoMLJobStatus
The status of the AutoML job.
String autoMLJobSecondaryStatus
The secondary status of the AutoML job.
Date creationTime
When the AutoML job was created.
Date endTime
The end time of an AutoML job.
Date lastModifiedTime
When the AutoML job was last modified.
String failureReason
The failure reason of an AutoML job.
List<E> partialFailureReasons
The list of reasons for partial failures within an AutoML job.
String partialFailureMessage
The message containing the reason for a partial failure of an AutoML job.
Map<K,V> modelPackageSummaries
The summaries for the model package versions
Map<K,V> batchDescribeModelPackageErrorMap
A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.
String modelPackageGroupName
The group name for the model package
Integer modelPackageVersion
The version number of a versioned model.
String modelPackageArn
The Amazon Resource Name (ARN) of the model package.
String modelPackageDescription
The description of the model package.
Date creationTime
The creation time of the mortgage package summary.
InferenceSpecification inferenceSpecification
String modelPackageStatus
The status of the mortgage package.
String modelApprovalStatus
The approval status of the model.
MetricsSource report
The bias report for a model
MetricsSource preTrainingReport
MetricsSource postTrainingReport
TrafficRoutingConfig trafficRoutingConfiguration
Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.
Integer terminationWaitInSeconds
Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.
Integer maximumExecutionTimeoutInSeconds
Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting
time specified in TerminationWaitInSeconds and WaitIntervalInSeconds.
String sourcePipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String callbackToken
The pipeline generated token from the Amazon SQS queue.
String sqsQueueUrl
The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the callback step.
List<E> outputParameters
A list of the output parameters of the callback step.
String explainability
The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
CandidateArtifactLocations candidateArtifactLocations
The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
List<E> candidateMetrics
Information about the candidate metrics for an AutoML job.
String type
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.
Integer value
Defines the capacity size, either as a number of instances or a capacity percentage.
String captureMode
String channelName
The name of the channel.
DataSource dataSource
The location of the channel data.
String contentType
The MIME type of the data.
String compressionType
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.
String recordWrapperType
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
String inputMode
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode, Amazon SageMaker uses the value set for TrainingInputMode. Use this
parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when
you have a channel that needs a different input mode from the training job's general setting. To download the
data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory
to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container,
choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig shuffleConfig
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.
String name
The name of the channel.
String description
A brief description of the channel.
Boolean isRequired
Indicates whether the channel is required by the algorithm.
List<E> supportedContentTypes
The supported MIME types for the data.
List<E> supportedCompressionTypes
The allowed compression types, if data compression is used.
List<E> supportedInputModes
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.
String checkType
The type of the Clarify Check step
String baselineUsedForDriftCheckConstraints
The Amazon S3 URI of baseline constraints file to be used for the drift check.
String calculatedBaselineConstraints
The Amazon S3 URI of the newly calculated baseline constraints file.
String modelPackageGroupName
The model package group name.
String violationReport
The Amazon S3 URI of the violation report if violations are detected.
String checkJobArn
The Amazon Resource Name (ARN) of the check processing job that was run by this step's execution.
Boolean skipCheck
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.
Boolean registerNewBaseline
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.
String codeRepositoryName
The name of the Git repository.
String codeRepositoryArn
The Amazon Resource Name (ARN) of the Git repository.
Date creationTime
The date and time that the Git repository was created.
Date lastModifiedTime
The date and time that the Git repository was last modified.
GitConfig gitConfig
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.
String userPool
A user pool 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.
String clientId
The client ID for your Amazon Cognito user pool.
String userPool
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
String userGroup
An identifier for a user group.
String clientId
An identifier for an application client. You must create the app client ID using Amazon Cognito.
String collectionName
The name of the tensor collection. The name must be unique relative to other rule configuration names.
Map<K,V> collectionParameters
Parameter values for the tensor collection. The allowed parameters are "name",
"include_regex", "reduction_config", "save_config",
"tensor_names", and "save_histogram".
String compilationJobName
The name of the model compilation job that you want a summary for.
String compilationJobArn
The Amazon Resource Name (ARN) of the model compilation job.
Date creationTime
The time when the model compilation job was created.
Date compilationStartTime
The time when the model compilation job started.
Date compilationEndTime
The time when the model compilation job completed.
String compilationTargetDevice
The type of device that the model will run on after the compilation job has completed.
String compilationTargetPlatformOs
The type of OS that the model will run on after the compilation job has completed.
String compilationTargetPlatformArch
The type of architecture that the model will run on after the compilation job has completed.
String compilationTargetPlatformAccelerator
The type of accelerator that the model will run on after the compilation job has completed.
Date lastModifiedTime
The time when the model compilation job was last modified.
String compilationJobStatus
The status of the model compilation job.
String outcome
The outcome of the Condition step evaluation.
String containerHostname
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. 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.
String image
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 Amazon SageMaker, the inference code must meet Amazon
SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and
registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon
SageMaker
ImageConfig imageConfig
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
String mode
Whether the container hosts a single model or multiple models.
String modelDataUrl
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
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, Amazon 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 IAM user 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 in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the
model artifacts in ModelDataUrl.
Map<K,V> environment
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.
String modelPackageName
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
String inferenceSpecificationName
The inference specification name in the model package version.
MultiModelConfig multiModelConfig
Specifies additional configuration for multi-model endpoints.
String contextArn
The Amazon Resource Name (ARN) of the context.
String contextName
The name of the context.
ContextSource source
The source of the context.
String contextType
The type of the context.
Date creationTime
When the context was created.
Date lastModifiedTime
When the context was last modified.
String name
The name of the continuous hyperparameter to tune.
String minValue
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and
MaxValuefor tuning.
String maxValue
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue
value and this value for tuning.
String scalingType
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
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.
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.
String actionName
The name of the action. Must be unique to your account in an Amazon Web Services Region.
ActionSource source
The source type, ID, and URI.
String actionType
The action type.
String description
The description of the action.
String status
The status of the action.
Map<K,V> properties
A list of properties to add to the action.
MetadataProperties metadataProperties
List<E> tags
A list of tags to apply to the action.
String actionArn
The Amazon Resource Name (ARN) of the action.
String algorithmName
The name of the algorithm.
String algorithmDescription
A description of the algorithm.
TrainingSpecification trainingSpecification
Specifies details about training jobs run by this algorithm, including the following:
The Amazon ECR path of the container and the version digest of the algorithm.
The hyperparameters that the algorithm supports.
The instance types that the algorithm supports for training.
Whether the algorithm supports distributed training.
The metrics that the algorithm emits to Amazon CloudWatch.
Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.
The input channels that the algorithm supports for training data. For example, an algorithm might support
train, validation, and test channels.
InferenceSpecification inferenceSpecification
Specifies details about inference jobs that the algorithm runs, including the following:
The Amazon ECR paths of containers that contain the inference code and model artifacts.
The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.
The input and output content formats that the algorithm supports for inference.
AlgorithmValidationSpecification validationSpecification
Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm's inference code.
Boolean certifyForMarketplace
Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
List<E> tags
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.
String algorithmArn
The Amazon Resource Name (ARN) of the new algorithm.
String appImageConfigName
The name of the AppImageConfig. Must be unique to your account.
List<E> tags
A list of tags to apply to the AppImageConfig.
KernelGatewayImageConfig kernelGatewayImageConfig
The KernelGatewayImageConfig.
String appImageConfigArn
The Amazon Resource Name (ARN) of the AppImageConfig.
String domainId
The domain ID.
String userProfileName
The user profile name.
String appType
The type of app. Supported apps are JupyterServer and KernelGateway.
TensorBoard is not supported.
String appName
The name of the app.
List<E> tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
ResourceSpec resourceSpec
The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
String appArn
The Amazon Resource Name (ARN) of the app.
String artifactName
The name of the artifact. Must be unique to your account in an Amazon Web Services Region.
ArtifactSource source
The ID, ID type, and URI of the source.
String artifactType
The artifact type.
Map<K,V> properties
A list of properties to add to the artifact.
MetadataProperties metadataProperties
List<E> tags
A list of tags to apply to the artifact.
String artifactArn
The Amazon Resource Name (ARN) of the artifact.
String autoMLJobName
Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.
List<E> inputDataConfig
An array of channel objects that describes the input data and its location. Each channel is a named input source.
Similar to InputDataConfig supported by . Format(s) supported: CSV. Minimum of 500 rows.
AutoMLOutputDataConfig outputDataConfig
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
String problemType
Defines the type of supervised learning available for the candidates. Options include:
BinaryClassification, MulticlassClassification, and Regression. For more
information, see
Amazon SageMaker Autopilot problem types and algorithm support.
AutoMLJobObjective autoMLJobObjective
Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.
AutoMLJobConfig autoMLJobConfig
Contains CompletionCriteria and SecurityConfig settings for the AutoML job.
String roleArn
The ARN of the role that is used to access the data.
Boolean generateCandidateDefinitionsOnly
Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
List<E> tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
ModelDeployConfig modelDeployConfig
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
String autoMLJobArn
The unique ARN assigned to the AutoML job when it is created.
String codeRepositoryName
The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
GitConfig gitConfig
Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
List<E> tags
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.
String codeRepositoryArn
The Amazon Resource Name (ARN) of the new repository.
String compilationJobName
A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
During model compilation, Amazon SageMaker needs your permission to:
Read input data from an S3 bucket
Write model artifacts to an S3 bucket
Write logs to Amazon CloudWatch Logs
Publish metrics to Amazon CloudWatch
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of
this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.
String modelPackageVersionArn
The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn
or an InputConfig object in the request syntax. The presence of both objects in the
CreateCompilationJob request will return an exception.
InputConfig inputConfig
Provides 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.
OutputConfig outputConfig
Provides information about the output location for the compiled model and the target device the model runs on.
NeoVpcConfig vpcConfig
A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.
StoppingCondition stoppingCondition
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
List<E> tags
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.
String compilationJobArn
If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:
CompilationJobArn: The Amazon Resource Name (ARN) of the compiled job.
String contextName
The name of the context. Must be unique to your account in an Amazon Web Services Region.
ContextSource source
The source type, ID, and URI.
String contextType
The context type.
String description
The description of the context.
Map<K,V> properties
A list of properties to add to the context.
List<E> tags
A list of tags to apply to the context.
String contextArn
The Amazon Resource Name (ARN) of the context.
String jobDefinitionName
The name for the monitoring job definition.
DataQualityBaselineConfig dataQualityBaselineConfig
Configures the constraints and baselines for the monitoring job.
DataQualityAppSpecification dataQualityAppSpecification
Specifies the container that runs the monitoring job.
DataQualityJobInput dataQualityJobInput
A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.
MonitoringOutputConfig dataQualityJobOutputConfig
MonitoringResources jobResources
MonitoringNetworkConfig networkConfig
Specifies networking configuration for the monitoring job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringStoppingCondition stoppingCondition
List<E> tags
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
String jobDefinitionArn
The Amazon Resource Name (ARN) of the job definition.
String deviceFleetName
The name of the fleet that the device belongs to.
String roleArn
The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
String description
A description of the fleet.
EdgeOutputConfig outputConfig
The output configuration for storing sample data collected by the fleet.
List<E> tags
Creates tags for the specified fleet.
Boolean enableIotRoleAlias
Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}".
For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
String domainName
A name for the domain.
String authMode
The mode of authentication that members use to access the domain.
UserSettings defaultUserSettings
The default settings to use to create a user profile when UserSettings isn't specified in the call
to the CreateUserProfile API.
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.
List<E> subnetIds
The VPC subnets that Studio uses for communication.
String vpcId
The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
List<E> tags
Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per
resource. Tags are searchable using the Search API.
Tags that you specify for the Domain are also added to all Apps that the Domain launches.
String appNetworkAccessType
Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows
direct internet access
VpcOnly - All Studio traffic is through the specified VPC and subnets
String homeEfsFileSystemKmsKeyId
Use KmsKeyId.
String kmsKeyId
SageMaker uses Amazon Web Services KMS to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.
String appSecurityGroupManagement
The entity that creates and manages the required security groups for inter-app communication in
VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly
and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.
DomainSettings domainSettings
A collection of Domain settings.
String edgePackagingJobName
The name of the edge packaging job.
String compilationJobName
The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.
String modelName
The name of the model.
String modelVersion
The version of the model.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.
EdgeOutputConfig outputConfig
Provides information about the output location for the packaged model.
String resourceKey
The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.
List<E> tags
Creates tags for the packaging job.
String endpointConfigName
The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
List<E> productionVariants
An list of ProductionVariant objects, one for each model that you want to host at this endpoint.
DataCaptureConfig dataCaptureConfig
List<E> tags
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.
String kmsKeyId
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.
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
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint,
UpdateEndpoint requests. For more information, refer to the Amazon Web Services Key Management
Service section Using Key
Policies in Amazon Web Services KMS
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 KmsKeyId when using an
instance type with local storage. If any of the models that you specify in the ProductionVariants
parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId
parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local
storage, the call to CreateEndpointConfig fails.
For a list of instance types that support local instance storage, see Instance Store Volumes.
For more information about local instance storage encryption, see SSD Instance Store Volumes.
AsyncInferenceConfig asyncInferenceConfig
Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order
for your Endpoint to be invoked using
InvokeEndpointAsync .
String endpointConfigArn
The Amazon Resource Name (ARN) of the endpoint configuration.
String endpointName
The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services
account. The name is case-insensitive in CreateEndpoint, but the case is preserved and must be
matched in .
String endpointConfigName
The name of an endpoint configuration. For more information, see CreateEndpointConfig.
DeploymentConfig deploymentConfig
List<E> tags
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.
String endpointArn
The Amazon Resource Name (ARN) of the endpoint.
String experimentName
The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.
String displayName
The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify
DisplayName, the value in ExperimentName is displayed.
String description
The description of the experiment.
List<E> tags
A list of tags to associate with the experiment. You can use Search API to search on the tags.
String experimentArn
The Amazon Resource Name (ARN) of the experiment.
String featureGroupName
The name of the FeatureGroup. The name must be unique within an Amazon Web Services Region in an
Amazon Web Services account. The name:
Must start and end with an alphanumeric character.
Can only contain alphanumeric character and hyphens. Spaces are not allowed.
String recordIdentifierFeatureName
The name of the Feature whose value uniquely identifies a Record defined in the
FeatureStore. Only the latest record per identifier value will be stored in the
OnlineStore. RecordIdentifierFeatureName must be one of feature definitions' names.
You use the RecordIdentifierFeatureName to access data in a FeatureStore.
This name:
Must start and end with an alphanumeric character.
Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.
String eventTimeFeatureName
The name of the feature that stores the EventTime of a Record in a
FeatureGroup.
An EventTime is a point in time when a new event occurs that corresponds to the creation or update
of a Record in a FeatureGroup. All Records in the
FeatureGroup must have a corresponding EventTime.
An EventTime can be a String or Fractional.
Fractional: EventTime feature values must be a Unix timestamp in seconds.
String: EventTime feature values must be an ISO-8601 string in the format. The
following formats are supported yyyy-MM-dd'T'HH:mm:ssZ and yyyy-MM-dd'T'HH:mm:ss.SSSZ
where yyyy, MM, and dd represent the year, month, and day respectively and
HH, mm, ss, and if applicable, SSS represent the hour, month,
second and milliseconds respsectively. 'T' and Z are constants.
List<E> featureDefinitions
A list of Feature names and types. Name and Type is compulsory per
Feature.
Valid feature FeatureTypes are Integral, Fractional and
String.
FeatureNames cannot be any of the following: is_deleted, write_time,
api_invocation_time
You can create up to 2,500 FeatureDefinitions per FeatureGroup.
OnlineStoreConfig onlineStoreConfig
You can turn the OnlineStore on or off by specifying True for the
EnableOnlineStore flag in OnlineStoreConfig; the default value is False.
You can also include an Amazon Web Services KMS key ID (KMSKeyId) for at-rest encryption of the
OnlineStore.
OfflineStoreConfig offlineStoreConfig
Use this to configure an OfflineFeatureStore. This parameter allows you to specify:
The Amazon Simple Storage Service (Amazon S3) location of an OfflineStore.
A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.
An KMS encryption key to encrypt the Amazon S3 location used for OfflineStore. If KMS encryption key
is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you
can reduce Amazon Web Services KMS requests costs by up to 99 percent.
To learn more about this parameter, see OfflineStoreConfig.
String roleArn
The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore
if an OfflineStoreConfig is provided.
String description
A free-form description of a FeatureGroup.
List<E> tags
Tags used to identify Features in each FeatureGroup.
String featureGroupArn
The Amazon Resource Name (ARN) of the FeatureGroup. This is a unique identifier for the feature
group.
String flowDefinitionName
The name of your flow definition.
HumanLoopRequestSource humanLoopRequestSource
Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
HumanLoopActivationConfig humanLoopActivationConfig
An object containing information about the events that trigger a human workflow.
HumanLoopConfig humanLoopConfig
An object containing information about the tasks the human reviewers will perform.
FlowDefinitionOutputConfig outputConfig
An object containing information about where the human review results will be uploaded.
String roleArn
The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example,
arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298.
List<E> tags
An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.
String flowDefinitionArn
The Amazon Resource Name (ARN) of the flow definition you create.
String humanTaskUiName
The name of the user interface you are creating.
UiTemplate uiTemplate
List<E> tags
An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.
String humanTaskUiArn
The Amazon Resource Name (ARN) of the human review workflow user interface you create.
String hyperParameterTuningJobName
The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
HyperParameterTuningJobConfig hyperParameterTuningJobConfig
The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.
HyperParameterTrainingJobDefinition trainingJobDefinition
The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
List<E> trainingJobDefinitions
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
HyperParameterTuningJobWarmStartConfig warmStartConfig
Specifies the configuration for starting the hyperparameter tuning job using one or more previous 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. If
you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start
configuration, the training job that performs the best in the new tuning job 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.
List<E> tags
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.
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
String hyperParameterTuningJobArn
The Amazon Resource Name (ARN) of the tuning job. Amazon SageMaker assigns an ARN to a hyperparameter tuning job when you create it.
String description
The description of the image.
String displayName
The display name of the image. If not provided, ImageName is displayed.
String imageName
The name of the image. Must be unique to your account.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
List<E> tags
A list of tags to apply to the image.
String imageArn
The Amazon Resource Name (ARN) of the image.
String baseImage
The registry path of the container image to use as the starting point for this version. The path is an Amazon Container Registry (ECR) URI in the following format:
<acct-id>.dkr.ecr.<region>.amazonaws.com/<repo-name[:tag] or [@digest]>
String clientToken
A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.
String imageName
The ImageName of the Image to create a version of.
String imageVersionArn
The Amazon Resource Name (ARN) of the image version.
String jobName
A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.
String jobType
Defines the type of recommendation job. Specify Default to initiate an instance recommendation and
Advanced to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will
run an instance recommendation (DEFAULT) job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
RecommendationJobInputConfig inputConfig
Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.
String jobDescription
Description of the recommendation job.
RecommendationJobStoppingConditions stoppingConditions
A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.
List<E> tags
The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.
String jobArn
The Amazon Resource Name (ARN) of the recommendation job.
String labelingJobName
The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job
names must be unique within an Amazon Web Services account and region. LabelingJobName is not case
sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
String labelAttributeName
The attribute name to use for the label in the output manifest file. This is the key for the key/value pair
formed with the label that a worker assigns to the object. The LabelAttributeName must meet the
following requirements.
The name can't end with "-metadata".
If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
Image semantic segmentation (SemanticSegmentation), and adjustment (
AdjustmentSemanticSegmentation) and verification (VerificationSemanticSegmentation)
labeling jobs for this task type.
Video frame object detection (VideoObjectDetection), and adjustment and verification (
AdjustmentVideoObjectDetection) labeling jobs for this task type.
Video frame object tracking (VideoObjectTracking), and adjustment and verification (
AdjustmentVideoObjectTracking) labeling jobs for this task type.
3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and
verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type.
3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification (
Adjustment3DPointCloudObjectTracking) labeling jobs for this task type.
If you are creating an adjustment or verification labeling job, you must use a different
LabelAttributeName than the one used in the original labeling job. The original labeling job is the
Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about
adjustment and verification labeling jobs, see Verify and Adjust Labels.
LabelingJobInputConfig inputConfig
Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
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 that stops after all data objects in
the input manifest file have been labeled.
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.
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information,
personal information or protected health information. Use ContentClassifiers to specify that your
data is free of personally identifiable information and adult content.
LabelingJobOutputConfig outputConfig
The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
String roleArn
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
String labelCategoryConfigS3Uri
The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For named entity recognition jobs, in addition to "labels", you must provide worker instructions in
the label category configuration file using the "instructions" parameter:
"instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}
. For details and an example, see Create a
Named Entity Recognition Labeling Job (API) .
For all other built-in task
types and custom
tasks, your label category configuration file must be a JSON file in the following format. Identify the
labels you want to use by replacing label_1, label_2,...,
label_n with your label categories.
{
"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
Each label category must be unique, you cannot specify duplicate label categories.
If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include
auditLabelAttributeName in the label category configuration. Use this parameter to enter the LabelAttributeName of the labeling job you want to adjust or verify annotations of.
LabelingJobStoppingConditions stoppingConditions
A set of conditions for stopping the 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.
LabelingJobAlgorithmsConfig labelingJobAlgorithmsConfig
Configures the information required to perform automated data labeling.
HumanTaskConfig humanTaskConfig
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
List<E> tags
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
String labelingJobArn
The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.
String jobDefinitionName
The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ModelBiasBaselineConfig modelBiasBaselineConfig
The baseline configuration for a model bias job.
ModelBiasAppSpecification modelBiasAppSpecification
Configures the model bias job to run a specified Docker container image.
ModelBiasJobInput modelBiasJobInput
Inputs for the model bias job.
MonitoringOutputConfig modelBiasJobOutputConfig
MonitoringResources jobResources
MonitoringNetworkConfig networkConfig
Networking options for a model bias job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringStoppingCondition stoppingCondition
List<E> tags
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
String jobDefinitionArn
The Amazon Resource Name (ARN) of the model bias job.
String jobDefinitionName
The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ModelExplainabilityBaselineConfig modelExplainabilityBaselineConfig
The baseline configuration for a model explainability job.
ModelExplainabilityAppSpecification modelExplainabilityAppSpecification
Configures the model explainability job to run a specified Docker container image.
ModelExplainabilityJobInput modelExplainabilityJobInput
Inputs for the model explainability job.
MonitoringOutputConfig modelExplainabilityJobOutputConfig
MonitoringResources jobResources
MonitoringNetworkConfig networkConfig
Networking options for a model explainability job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringStoppingCondition stoppingCondition
List<E> tags
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
String jobDefinitionArn
The Amazon Resource Name (ARN) of the model explainability job.
String modelPackageGroupName
The name of the model group.
String modelPackageGroupDescription
A description for the model group.
List<E> tags
A list of key value pairs associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
String modelPackageGroupArn
The Amazon Resource Name (ARN) of the model group.
String modelPackageName
The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
This parameter is required for unversioned models. It is not applicable to versioned models.
String modelPackageGroupName
The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.
This parameter is required for versioned models, and does not apply to unversioned models.
String modelPackageDescription
A description of the model package.
InferenceSpecification inferenceSpecification
Specifies details about inference jobs that can be run with models based on this model package, including the following:
The Amazon ECR paths of containers that contain the inference code and model artifacts.
The instance types that the model package supports for transform jobs and real-time endpoints used for inference.
The input and output content formats that the model package supports for inference.
ModelPackageValidationSpecification validationSpecification
Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
SourceAlgorithmSpecification sourceAlgorithmSpecification
Details about the algorithm that was used to create the model package.
Boolean certifyForMarketplace
Whether to certify the model package for listing on Amazon Web Services Marketplace.
This parameter is optional for unversioned models, and does not apply to versioned models.
List<E> tags
A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
String modelApprovalStatus
Whether the model is approved for deployment.
This parameter is optional for versioned models, and does not apply to unversioned models.
For versioned models, the value of this parameter must be set to Approved to deploy the model.
MetadataProperties metadataProperties
ModelMetrics modelMetrics
A structure that contains model metrics reports.
String clientToken
A unique token that guarantees that the call to this API is idempotent.
Map<K,V> customerMetadataProperties
The metadata properties associated with the model package versions.
DriftCheckBaselines driftCheckBaselines
Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
String domain
The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
String task
The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.
String samplePayloadUrl
The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
List<E> additionalInferenceSpecifications
An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
String modelPackageArn
The Amazon Resource Name (ARN) of the new model package.
String jobDefinitionName
The name of the monitoring job definition.
ModelQualityBaselineConfig modelQualityBaselineConfig
Specifies the constraints and baselines for the monitoring job.
ModelQualityAppSpecification modelQualityAppSpecification
The container that runs the monitoring job.
ModelQualityJobInput modelQualityJobInput
A list of the inputs that are monitored. Currently endpoints are supported.
MonitoringOutputConfig modelQualityJobOutputConfig
MonitoringResources jobResources
MonitoringNetworkConfig networkConfig
Specifies the network configuration for the monitoring job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringStoppingCondition stoppingCondition
List<E> tags
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
String jobDefinitionArn
The Amazon Resource Name (ARN) of the model quality monitoring job.
String modelName
The name of the new model.
ContainerDefinition primaryContainer
The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
List<E> containers
Specifies the containers in the inference pipeline.
InferenceExecutionConfig inferenceExecutionConfig
Specifies details of how containers in a multi-container endpoint are called.
String executionRoleArn
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles.
To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole
permission.
List<E> tags
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.
VpcConfig vpcConfig
A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and
from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch
transform. For more information, see Protect Endpoints by Using an Amazon Virtual
Private Cloud and Protect Data in
Batch Transform Jobs by Using an Amazon Virtual Private Cloud.
Boolean enableNetworkIsolation
Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
String modelArn
The ARN of the model created in Amazon SageMaker.
String monitoringScheduleName
The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
MonitoringScheduleConfig monitoringScheduleConfig
The configuration object that specifies the monitoring schedule and defines the monitoring job.
List<E> tags
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
String monitoringScheduleArn
The Amazon Resource Name (ARN) of the monitoring schedule.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration.
List<E> onCreate
A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
List<E> onStart
A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
String notebookInstanceLifecycleConfigArn
The Amazon Resource Name (ARN) of the lifecycle configuration.
String notebookInstanceName
The name of the new notebook instance.
String instanceType
The type of ML compute instance to launch for the notebook instance.
String subnetId
The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.
List<E> securityGroupIds
The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
String roleArn
When you send any requests to Amazon Web Services resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see Amazon SageMaker Roles.
To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole
permission.
String kmsKeyId
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.
List<E> tags
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.
String lifecycleConfigName
The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
String directInternetAccess
Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to
Disabled this notebook instance is able to access resources only in your VPC, and is not be able to
connect to Amazon SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC.
For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to
Disabled only if you set a value for the SubnetId parameter.
Integer volumeSizeInGB
The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.
List<E> acceleratorTypes
A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
String defaultCodeRepository
A Git repository to associate 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 or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
List<E> additionalCodeRepositories
An array of up to three Git repositories to associate 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 or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
String rootAccess
Whether root access is enabled or disabled for users of the notebook instance. The default value is
Enabled.
Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.
String platformIdentifier
The platform identifier of the notebook instance runtime environment.
String notebookInstanceArn
The Amazon Resource Name (ARN) of the notebook instance.
String pipelineName
The name of the pipeline.
String pipelineDisplayName
The display name of the pipeline.
String pipelineDefinition
The JSON pipeline definition of the pipeline.
PipelineDefinitionS3Location pipelineDefinitionS3Location
The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.
String pipelineDescription
A description of the pipeline.
String clientRequestToken
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
String roleArn
The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.
List<E> tags
A list of tags to apply to the created pipeline.
ParallelismConfiguration parallelismConfiguration
This is the configuration that controls the parallelism of the pipeline. If specified, it applies to all runs of this pipeline by default.
String pipelineArn
The Amazon Resource Name (ARN) of the created pipeline.
String domainId
The domain ID.
String userProfileName
The name of the UserProfile to sign-in as.
Integer sessionExpirationDurationInSeconds
The session expiration duration in seconds. This value defaults to 43200.
Integer expiresInSeconds
The number of seconds until the pre-signed URL expires. This value defaults to 300.
String authorizedUrl
The presigned URL.
String authorizedUrl
A JSON object that contains the URL string.
List<E> processingInputs
An array of inputs configuring the data to download into the processing container.
ProcessingOutputConfig processingOutputConfig
Output configuration for the processing job.
String processingJobName
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ProcessingResources processingResources
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.
ProcessingStoppingCondition stoppingCondition
The time limit for how long the processing job is allowed to run.
AppSpecification appSpecification
Configures the processing job to run a specified Docker container image.
Map<K,V> environment
The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.
NetworkConfig networkConfig
Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
List<E> tags
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
ExperimentConfig experimentConfig
String processingJobArn
The Amazon Resource Name (ARN) of the processing job.
String projectName
The name of the project.
String projectDescription
A description for the project.
ServiceCatalogProvisioningDetails serviceCatalogProvisioningDetails
The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.
List<E> tags
An array of key-value pairs that you want to use to organize and track your Amazon Web Services resource costs. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
String studioLifecycleConfigName
The name of the Studio Lifecycle Configuration to create.
String studioLifecycleConfigContent
The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
String studioLifecycleConfigAppType
The App type that the Lifecycle Configuration is attached to.
List<E> tags
Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.
String studioLifecycleConfigArn
The ARN of your created Lifecycle Configuration.
String trainingJobName
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
Map<K,V> hyperParameters
Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is
limited to 256 characters, as specified by the Length Constraint.
AlgorithmSpecification algorithmSpecification
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.
To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole
permission.
List<E> inputDataConfig
An array of Channel objects. Each channel is a named input source. InputDataConfig
describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of
input data, training_data and validation_data. The configuration for each channel
provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the
stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.
Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
OutputDataConfig outputDataConfig
Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
ResourceConfig resourceConfig
The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage
volumes for scratch space. If you want Amazon SageMaker to use the ML 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.
VpcConfig vpcConfig
A VpcConfig object that specifies the VPC that you want your training job 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.
StoppingCondition stoppingCondition
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, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination
for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of
training are not lost.
List<E> tags
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.
Boolean enableNetworkIsolation
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 you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
Boolean enableInterContainerTrafficEncryption
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. For more information, see Protect Communications Between ML
Compute Instances in a Distributed Training Job.
Boolean enableManagedSpotTraining
To train models using managed spot training, choose True. Managed spot training provides a fully
managed and scalable infrastructure for training machine learning models. this option is useful when training
jobs can be interrupted and when there is flexibility when the training job is run.
The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
CheckpointConfig checkpointConfig
Contains information about the output location for managed spot training checkpoint data.
DebugHookConfig debugHookConfig
List<E> debugRuleConfigurations
Configuration information for Debugger rules for debugging output tensors.
TensorBoardOutputConfig tensorBoardOutputConfig
ExperimentConfig experimentConfig
ProfilerConfig profilerConfig
List<E> profilerRuleConfigurations
Configuration information for Debugger rules for profiling system and framework metrics.
Map<K,V> environment
The environment variables to set in the Docker container.
RetryStrategy retryStrategy
The number of times to retry the job when the job fails due to an InternalServerError.
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
String transformJobName
The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
String modelName
The name of the model that you want to use for the transform job. ModelName must be the name of an
existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.
Integer maxConcurrentTransforms
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, Amazon 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 more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value for
MaxConcurrentTransforms.
ModelClientConfig modelClientConfig
Configures the timeout and maximum number of retries for processing a transform job invocation.
Integer maxPayloadInMB
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, Amazon SageMaker built-in
algorithms do not support HTTP chunked encoding.
String batchStrategy
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.
To enable the batch strategy, you must set the SplitType property to Line,
RecordIO, or TFRecord.
To use only one record when making an HTTP invocation request to a container, set BatchStrategy to
SingleRecord and SplitType to Line.
To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set
BatchStrategy to MultiRecord and SplitType to Line.
Map<K,V> environment
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput transformInput
Describes the input source and the way the transform job consumes it.
TransformOutput transformOutput
Describes the results of the transform job.
TransformResources transformResources
Describes the resources, including ML instance types and ML instance count, to use for the transform job.
DataProcessing dataProcessing
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.
List<E> tags
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
ExperimentConfig experimentConfig
String transformJobArn
The Amazon Resource Name (ARN) of the transform job.
String trialComponentName
The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.
String displayName
The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't
specified, TrialComponentName is displayed.
TrialComponentStatus status
The status of the component. States include:
InProgress
Completed
Failed
Date startTime
When the component started.
Date endTime
When the component ended.
Map<K,V> parameters
The hyperparameters for the component.
Map<K,V> inputArtifacts
The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.
Map<K,V> outputArtifacts
The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.
MetadataProperties metadataProperties
List<E> tags
A list of tags to associate with the component. You can use Search API to search on the tags.
String trialComponentArn
The Amazon Resource Name (ARN) of the trial component.
String trialName
The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.
String displayName
The name of the trial as displayed. The name doesn't need to be unique. If DisplayName isn't
specified, TrialName is displayed.
String experimentName
The name of the experiment to associate the trial with.
MetadataProperties metadataProperties
List<E> tags
A list of tags to associate with the trial. You can use Search API to search on the tags.
String trialArn
The Amazon Resource Name (ARN) of the trial.
String domainId
The ID of the associated Domain.
String userProfileName
A name for the UserProfile. This value is not case sensitive.
String singleSignOnUserIdentifier
A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is SSO, this field is required. If the Domain's AuthMode is not SSO, this field cannot be specified.
String singleSignOnUserValue
The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain's AuthMode is SSO, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not SSO, this field cannot be specified.
List<E> tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.
UserSettings userSettings
A collection of settings.
String userProfileArn
The user profile Amazon Resource Name (ARN).
CognitoConfig cognitoConfig
Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
Do not use OidcConfig if you specify values for CognitoConfig.
OidcConfig oidcConfig
Use this parameter to configure a private workforce using your own OIDC Identity Provider.
Do not use CognitoConfig if you specify values for OidcConfig.
SourceIpConfig sourceIpConfig
String workforceName
The name of the private workforce.
List<E> tags
An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define.
String workforceArn
The Amazon Resource Name (ARN) of the workforce.
String workteamName
The name of the work team. Use this name to identify the work team.
String workforceName
The name of the workforce.
List<E> memberDefinitions
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. Do not provide input for both of these parameters
in a single request.
For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups
within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that
make up the member definition must have the same ClientId and UserPool values. To add a
Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more
information about user pools, see Amazon Cognito
User Pools.
For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private
work team in OidcMemberDefinition by listing those groups in Groups.
String description
A description of the work team.
NotificationConfiguration notificationConfiguration
Configures notification of workers regarding available or expiring work items.
List<E> tags
An array of key-value pairs.
For more information, see Resource Tag and Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
String workteamArn
The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.
String inputFilter
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the
InputFilter parameter to exclude fields, such as an ID column, from the input. If you want Amazon
SageMaker to pass the entire input dataset to the algorithm, accept the default value $.
Examples: "$", "$[1:]", "$.features"
String outputFilter
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch
transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the
default value, $. If you specify indexes that aren't within the dimension size of the joined
dataset, you get an error.
Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
String joinSource
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.
String imageUri
The container image that the data quality monitoring job runs.
List<E> containerEntrypoint
The entrypoint for a container used to run a monitoring job.
List<E> containerArguments
The arguments to send to the container that the monitoring job runs.
String recordPreprocessorSourceUri
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.
String postAnalyticsProcessorSourceUri
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
Map<K,V> environment
Sets the environment variables in the container that the monitoring job runs.
String baseliningJobName
The name of the job that performs baselining for the data quality monitoring job.
MonitoringConstraintsResource constraintsResource
MonitoringStatisticsResource statisticsResource
EndpointInput endpointInput
AthenaDatasetDefinition athenaDatasetDefinition
RedshiftDatasetDefinition redshiftDatasetDefinition
String localPath
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).
String dataDistributionType
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
String inputMode
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.
S3DataSource s3DataSource
The S3 location of the data source that is associated with a channel.
FileSystemDataSource fileSystemDataSource
The file system that is associated with a channel.
String localPath
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/.
String s3OutputPath
Path to Amazon S3 storage location for metrics and tensors.
Map<K,V> hookParameters
Configuration information for the Debugger hook parameters.
List<E> collectionConfigurations
Configuration information for 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.
String ruleConfigurationName
The name of the rule configuration. It must be unique relative to other rule configuration names.
String localPath
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
String s3OutputPath
Path to Amazon S3 storage location for rules.
String ruleEvaluatorImage
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
String instanceType
The instance type to deploy a Debugger custom rule for debugging a training job.
Integer volumeSizeInGB
The size, in GB, of the ML storage volume attached to the processing instance.
Map<K,V> ruleParameters
Runtime configuration for rule container.
String ruleConfigurationName
The name of the rule configuration.
String ruleEvaluationJobArn
The Amazon Resource Name (ARN) of the rule evaluation job.
String ruleEvaluationStatus
Status of the rule evaluation.
String statusDetails
Details from the rule evaluation.
Date lastModifiedTime
Timestamp when the rule evaluation status was last modified.
String actionName
The name of the action to delete.
String actionArn
The Amazon Resource Name (ARN) of the action.
String algorithmName
The name of the algorithm to delete.
String appImageConfigName
The name of the AppImageConfig to delete.
String artifactArn
The Amazon Resource Name (ARN) of the artifact to delete.
ArtifactSource source
The URI of the source.
String artifactArn
The Amazon Resource Name (ARN) of the artifact.
String codeRepositoryName
The name of the Git repository to delete.
String contextName
The name of the context to delete.
String contextArn
The Amazon Resource Name (ARN) of the context.
String jobDefinitionName
The name of the data quality monitoring job definition to delete.
String deviceFleetName
The name of the fleet to delete.
String domainId
The domain ID.
RetentionPolicy retentionPolicy
The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted).
String endpointConfigName
The name of the endpoint configuration that you want to delete.
String endpointName
The name of the endpoint that you want to delete.
String experimentName
The name of the experiment to delete.
String experimentArn
The Amazon Resource Name (ARN) of the experiment that is being deleted.
String featureGroupName
The name of the FeatureGroup you want to delete. The name must be unique within an Amazon Web
Services Region in an Amazon Web Services account.
String flowDefinitionName
The name of the flow definition you are deleting.
String humanTaskUiName
The name of the human task user interface (work task template) you want to delete.
String imageName
The name of the image to delete.
String jobDefinitionName
The name of the model bias job definition to delete.
String jobDefinitionName
The name of the model explainability job definition to delete.
String modelPackageGroupName
The name of the model group for which to delete the policy.
String modelPackageGroupName
The name of the model group to delete.
String modelPackageName
The name or Amazon Resource Name (ARN) of the model package to delete.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
String jobDefinitionName
The name of the model quality monitoring job definition to delete.
String modelName
The name of the model to delete.
String monitoringScheduleName
The name of the monitoring schedule to delete.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration to delete.
String notebookInstanceName
The name of the Amazon SageMaker notebook instance to delete.
String pipelineArn
The Amazon Resource Name (ARN) of the pipeline to delete.
String projectName
The name of the project to delete.
String studioLifecycleConfigName
The name of the Studio Lifecycle Configuration to delete.
String trialComponentName
The name of the component to delete.
String trialComponentArn
The Amazon Resource Name (ARN) of the component is being deleted.
String trialName
The name of the trial to delete.
String trialArn
The Amazon Resource Name (ARN) of the trial that is being deleted.
String workforceName
The name of the workforce.
String workteamName
The name of the work team to delete.
Boolean success
Returns true if the work team was successfully deleted; otherwise, returns false.
String specifiedImage
The image path you specified when you created the model.
String resolvedImage
The specific digest path of the image hosted in this ProductionVariant.
Date resolutionTime
The date and time when the image path for the model resolved to the ResolvedImage
BlueGreenUpdatePolicy blueGreenUpdatePolicy
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.
AutoRollbackConfig autoRollbackConfiguration
Automatic rollback configuration for handling endpoint deployment failures and recovery.
String actionName
The name of the action to describe.
String actionName
The name of the action.
String actionArn
The Amazon Resource Name (ARN) of the action.
ActionSource source
The source of the action.
String actionType
The type of the action.
String description
The description of the action.
String status
The status of the action.
Map<K,V> properties
A list of the action's properties.
Date creationTime
When the action was created.
UserContext createdBy
Date lastModifiedTime
When the action was last modified.
UserContext lastModifiedBy
MetadataProperties metadataProperties
String lineageGroupArn
The Amazon Resource Name (ARN) of the lineage group.
String algorithmName
The name of the algorithm to describe.
String algorithmName
The name of the algorithm being described.
String algorithmArn
The Amazon Resource Name (ARN) of the algorithm.
String algorithmDescription
A brief summary about the algorithm.
Date creationTime
A timestamp specifying when the algorithm was created.
TrainingSpecification trainingSpecification
Details about training jobs run by this algorithm.
InferenceSpecification inferenceSpecification
Details about inference jobs that the algorithm runs.
AlgorithmValidationSpecification validationSpecification
Details about configurations for one or more training jobs that Amazon SageMaker runs to test the algorithm.
String algorithmStatus
The current status of the algorithm.
AlgorithmStatusDetails algorithmStatusDetails
Details about the current status of the algorithm.
String productId
The product identifier of the algorithm.
Boolean certifyForMarketplace
Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.
String appImageConfigName
The name of the AppImageConfig to describe.
String appImageConfigArn
The Amazon Resource Name (ARN) of the AppImageConfig.
String appImageConfigName
The name of the AppImageConfig.
Date creationTime
When the AppImageConfig was created.
Date lastModifiedTime
When the AppImageConfig was last modified.
KernelGatewayImageConfig kernelGatewayImageConfig
The configuration of a KernelGateway app.
String appArn
The Amazon Resource Name (ARN) of the app.
String appType
The type of app.
String appName
The name of the app.
String domainId
The domain ID.
String userProfileName
The user profile name.
String status
The status.
Date lastHealthCheckTimestamp
The timestamp of the last health check.
Date lastUserActivityTimestamp
The timestamp of the last user's activity. LastUserActivityTimestamp is also updated when SageMaker
performs health checks without user activity. As a result, this value is set to the same value as
LastHealthCheckTimestamp.
Date creationTime
The creation time.
String failureReason
The failure reason.
ResourceSpec resourceSpec
The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
String artifactArn
The Amazon Resource Name (ARN) of the artifact to describe.
String artifactName
The name of the artifact.
String artifactArn
The Amazon Resource Name (ARN) of the artifact.
ArtifactSource source
The source of the artifact.
String artifactType
The type of the artifact.
Map<K,V> properties
A list of the artifact's properties.
Date creationTime
When the artifact was created.
UserContext createdBy
Date lastModifiedTime
When the artifact was last modified.
UserContext lastModifiedBy
MetadataProperties metadataProperties
String lineageGroupArn
The Amazon Resource Name (ARN) of the lineage group.
String autoMLJobName
Requests information about an AutoML job using its unique name.
String autoMLJobName
Returns the name of the AutoML job.
String autoMLJobArn
Returns the ARN of the AutoML job.
List<E> inputDataConfig
Returns the input data configuration for the AutoML job..
AutoMLOutputDataConfig outputDataConfig
Returns the job's output data config.
String roleArn
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.
AutoMLJobObjective autoMLJobObjective
Returns the job's objective.
String problemType
Returns the job's problem type.
AutoMLJobConfig autoMLJobConfig
Returns the configuration for the AutoML job.
Date creationTime
Returns the creation time of the AutoML job.
Date endTime
Returns the end time of the AutoML job.
Date lastModifiedTime
Returns the job's last modified time.
String failureReason
Returns the failure reason for an AutoML job, when applicable.
List<E> partialFailureReasons
Returns a list of reasons for partial failures within an AutoML job.
AutoMLCandidate bestCandidate
Returns the job's best AutoMLCandidate.
String autoMLJobStatus
Returns the status of the AutoML job.
String autoMLJobSecondaryStatus
Returns the secondary status of the AutoML job.
Boolean generateCandidateDefinitionsOnly
Indicates whether the output for an AutoML job generates candidate definitions only.
AutoMLJobArtifacts autoMLJobArtifacts
Returns information on the job's artifacts found in AutoMLJobArtifacts.
ResolvedAttributes resolvedAttributes
This contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If
you do not provide these values, they are auto-inferred. If you do provide them, the values used are the ones you
provide.
ModelDeployConfig modelDeployConfig
Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
ModelDeployResult modelDeployResult
Provides information about endpoint for the model deployment.
String codeRepositoryName
The name of the Git repository to describe.
String codeRepositoryName
The name of the Git repository.
String codeRepositoryArn
The Amazon Resource Name (ARN) of the Git repository.
Date creationTime
The date and time that the repository was created.
Date lastModifiedTime
The date and time that the repository was last changed.
GitConfig gitConfig
Configuration details about the repository, including the URL where the repository is located, the default branch, and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
String compilationJobName
The name of the model compilation job that you want information about.
String compilationJobName
The name of the model compilation job.
String compilationJobArn
The Amazon Resource Name (ARN) of the model compilation job.
String compilationJobStatus
The status of the model compilation job.
Date compilationStartTime
The time when the model compilation job started the CompilationJob instances.
You are billed for the time between this timestamp and the timestamp in the DescribeCompilationJobResponse$CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container.
Date compilationEndTime
The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.
StoppingCondition stoppingCondition
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
String inferenceImage
The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance.
String modelPackageVersionArn
The Amazon Resource Name (ARN) of the versioned model package that was provided to SageMaker Neo when you initiated a compilation job.
Date creationTime
The time that the model compilation job was created.
Date lastModifiedTime
The time that the status of the model compilation job was last modified.
String failureReason
If a model compilation job failed, the reason it failed.
ModelArtifacts modelArtifacts
Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.
ModelDigests modelDigests
Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.
InputConfig inputConfig
Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
OutputConfig outputConfig
Information about the output location for the compiled model and the target device that the model runs on.
NeoVpcConfig vpcConfig
A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.
String contextName
The name of the context to describe.
String contextName
The name of the context.
String contextArn
The Amazon Resource Name (ARN) of the context.
ContextSource source
The source of the context.
String contextType
The type of the context.
String description
The description of the context.
Map<K,V> properties
A list of the context's properties.
Date creationTime
When the context was created.
UserContext createdBy
Date lastModifiedTime
When the context was last modified.
UserContext lastModifiedBy
String lineageGroupArn
The Amazon Resource Name (ARN) of the lineage group.
String jobDefinitionName
The name of the data quality monitoring job definition to describe.
String jobDefinitionArn
The Amazon Resource Name (ARN) of the data quality monitoring job definition.
String jobDefinitionName
The name of the data quality monitoring job definition.
Date creationTime
The time that the data quality monitoring job definition was created.
DataQualityBaselineConfig dataQualityBaselineConfig
The constraints and baselines for the data quality monitoring job definition.
DataQualityAppSpecification dataQualityAppSpecification
Information about the container that runs the data quality monitoring job.
DataQualityJobInput dataQualityJobInput
The list of inputs for the data quality monitoring job. Currently endpoints are supported.
MonitoringOutputConfig dataQualityJobOutputConfig
MonitoringResources jobResources
MonitoringNetworkConfig networkConfig
The networking configuration for the data quality monitoring job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringStoppingCondition stoppingCondition
String deviceFleetName
The name of the fleet.
String deviceFleetName
The name of the fleet.
String deviceFleetArn
The The Amazon Resource Name (ARN) of the fleet.
EdgeOutputConfig outputConfig
The output configuration for storing sampled data.
String description
A description of the fleet.
Date creationTime
Timestamp of when the device fleet was created.
Date lastModifiedTime
Timestamp of when the device fleet was last updated.
String roleArn
The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
String iotRoleAlias
The Amazon Resource Name (ARN) alias created in Amazon Web Services Internet of Things (IoT).
String deviceArn
The Amazon Resource Name (ARN) of the device.
String deviceName
The unique identifier of the device.
String description
A description of the device.
String deviceFleetName
The name of the fleet the device belongs to.
String iotThingName
The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.
Date registrationTime
The timestamp of the last registration or de-reregistration.
Date latestHeartbeat
The last heartbeat received from the device.
List<E> models
Models on the device.
Integer maxModels
The maximum number of models.
String nextToken
The response from the last list when returning a list large enough to need tokening.
String agentVersion
Edge Manager agent version.
String domainId
The domain ID.
String domainArn
The domain's Amazon Resource Name (ARN).
String domainId
The domain ID.
String domainName
The domain name.
String homeEfsFileSystemId
The ID of the Amazon Elastic File System (EFS) managed by this Domain.
String singleSignOnManagedApplicationInstanceId
The SSO managed application instance ID.
String status
The status.
Date creationTime
The creation time.
Date lastModifiedTime
The last modified time.
String failureReason
The failure reason.
String authMode
The domain's authentication mode.
UserSettings defaultUserSettings
Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile.
String appNetworkAccessType
Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows
direct internet access
VpcOnly - All Studio traffic is through the specified VPC and subnets
String homeEfsFileSystemKmsKeyId
Use KmsKeyId.
List<E> subnetIds
The VPC subnets that Studio uses for communication.
String url
The domain's URL.
String vpcId
The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
String kmsKeyId
The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.
DomainSettings domainSettings
A collection of Domain settings.
String appSecurityGroupManagement
The entity that creates and manages the required security groups for inter-app communication in
VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly
and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.
String securityGroupIdForDomainBoundary
The ID of the security group that authorizes traffic between the RSessionGateway apps and the
RStudioServerPro app.
String edgePackagingJobName
The name of the edge packaging job.
String edgePackagingJobArn
The Amazon Resource Name (ARN) of the edge packaging job.
String edgePackagingJobName
The name of the edge packaging job.
String compilationJobName
The name of the SageMaker Neo compilation job that is used to locate model artifacts that are being packaged.
String modelName
The name of the model.
String modelVersion
The version of the model.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact Neo.
EdgeOutputConfig outputConfig
The output configuration for the edge packaging job.
String resourceKey
The Amazon Web Services KMS key to use when encrypting the EBS volume the job run on.
String edgePackagingJobStatus
The current status of the packaging job.
String edgePackagingJobStatusMessage
Returns a message describing the job status and error messages.
Date creationTime
The timestamp of when the packaging job was created.
Date lastModifiedTime
The timestamp of when the job was last updated.
String modelArtifact
The Amazon Simple Storage (S3) URI where model artifacts ares stored.
String modelSignature
The signature document of files in the model artifact.
EdgePresetDeploymentOutput presetDeploymentOutput
The output of a SageMaker Edge Manager deployable resource.
String endpointConfigName
The name of the endpoint configuration.
String endpointConfigName
Name of the Amazon SageMaker endpoint configuration.
String endpointConfigArn
The Amazon Resource Name (ARN) of the endpoint configuration.
List<E> productionVariants
An array of ProductionVariant objects, one for each model that you want to host at this endpoint.
DataCaptureConfig dataCaptureConfig
String kmsKeyId
Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
Date creationTime
A timestamp that shows when the endpoint configuration was created.
AsyncInferenceConfig asyncInferenceConfig
Returns the description of an endpoint configuration created using the
CreateEndpointConfig API.
String endpointName
The name of the endpoint.
String endpointName
Name of the endpoint.
String endpointArn
The Amazon Resource Name (ARN) of the endpoint.
String endpointConfigName
The name of the endpoint configuration associated with this endpoint.
List<E> productionVariants
An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.
DataCaptureConfigSummary dataCaptureConfig
String endpointStatus
The status of the endpoint.
OutOfService: Endpoint is not available to take incoming requests.
Creating: CreateEndpoint is executing.
Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.
SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled
until it has completed. This maintenance operation does not change any customer-specified values such as VPC
config, KMS encryption, model, instance type, or instance count.
RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process
of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an
InService status. This transitional status only applies to an endpoint that has autoscaling enabled
and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call
or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.
InService: Endpoint is available to process incoming requests.
Deleting: DeleteEndpoint is executing.
Failed: Endpoint could not be created, updated, or re-scaled. Use
DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only
operation that can be performed on a failed endpoint.
String failureReason
If the status of the endpoint is Failed, the reason why it failed.
Date creationTime
A timestamp that shows when the endpoint was created.
Date lastModifiedTime
A timestamp that shows when the endpoint was last modified.
DeploymentConfig lastDeploymentConfig
The most recent deployment configuration for the endpoint.
AsyncInferenceConfig asyncInferenceConfig
Returns the description of an endpoint configuration created using the
CreateEndpointConfig API.
PendingDeploymentSummary pendingDeploymentSummary
Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.
String experimentName
The name of the experiment to describe.
String experimentName
The name of the experiment.
String experimentArn
The Amazon Resource Name (ARN) of the experiment.
String displayName
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName
is displayed.
ExperimentSource source
The ARN of the source and, optionally, the type.
String description
The description of the experiment.
Date creationTime
When the experiment was created.
UserContext createdBy
Who created the experiment.
Date lastModifiedTime
When the experiment was last modified.
UserContext lastModifiedBy
Who last modified the experiment.
String featureGroupArn
The Amazon Resource Name (ARN) of the FeatureGroup.
String featureGroupName
he name of the FeatureGroup.
String recordIdentifierFeatureName
The name of the Feature used for RecordIdentifier, whose value uniquely identifies a
record stored in the feature store.
String eventTimeFeatureName
The name of the feature that stores the EventTime of a Record in a FeatureGroup.
An EventTime is a point in time when a new event occurs that corresponds to the creation or update
of a Record in a FeatureGroup. All Records in the
FeatureGroup have a corresponding EventTime.
List<E> featureDefinitions
A list of the Features in the FeatureGroup. Each feature is defined by a
FeatureName and FeatureType.
Date creationTime
A timestamp indicating when SageMaker created the FeatureGroup.
OnlineStoreConfig onlineStoreConfig
The configuration for the OnlineStore.
OfflineStoreConfig offlineStoreConfig
The configuration of the OfflineStore, inducing the S3 location of the OfflineStore,
Amazon Web Services Glue or Amazon Web Services Hive data catalogue configurations, and the security
configuration.
String roleArn
The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore
if an OfflineStoreConfig is provided.
String featureGroupStatus
The status of the feature group.
OfflineStoreStatus offlineStoreStatus
The status of the OfflineStore. Notifies you if replicating data into the OfflineStore
has failed. Returns either: Active or Blocked
String failureReason
The reason that the FeatureGroup failed to be replicated in the OfflineStore. This is
failure can occur because:
The FeatureGroup could not be created in the OfflineStore.
The FeatureGroup could not be deleted from the OfflineStore.
String description
A free form description of the feature group.
String nextToken
A token to resume pagination of the list of Features (FeatureDefinitions).
String flowDefinitionName
The name of the flow definition.
String flowDefinitionArn
The Amazon Resource Name (ARN) of the flow defintion.
String flowDefinitionName
The Amazon Resource Name (ARN) of the flow definition.
String flowDefinitionStatus
The status of the flow definition. Valid values are listed below.
Date creationTime
The timestamp when the flow definition was created.
HumanLoopRequestSource humanLoopRequestSource
Container for configuring the source of human task requests. Used to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
HumanLoopActivationConfig humanLoopActivationConfig
An object containing information about what triggers a human review workflow.
HumanLoopConfig humanLoopConfig
An object containing information about who works on the task, the workforce task price, and other task details.
FlowDefinitionOutputConfig outputConfig
An object containing information about the output file.
String roleArn
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.
String failureReason
The reason your flow definition failed.
String humanTaskUiName
The name of the human task user interface (worker task template) you want information about.
String humanTaskUiArn
The Amazon Resource Name (ARN) of the human task user interface (worker task template).
String humanTaskUiName
The name of the human task user interface (worker task template).
String humanTaskUiStatus
The status of the human task user interface (worker task template). Valid values are listed below.
Date creationTime
The timestamp when the human task user interface was created.
UiTemplateInfo uiTemplate
String hyperParameterTuningJobName
The name of the tuning job.
String hyperParameterTuningJobName
The name of the tuning job.
String hyperParameterTuningJobArn
The Amazon Resource Name (ARN) of the tuning job.
HyperParameterTuningJobConfig hyperParameterTuningJobConfig
The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.
HyperParameterTrainingJobDefinition trainingJobDefinition
The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
List<E> trainingJobDefinitions
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
String hyperParameterTuningJobStatus
The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.
Date creationTime
The date and time that the tuning job started.
Date hyperParameterTuningEndTime
The date and time that the tuning job ended.
Date lastModifiedTime
The date and time that the status of the tuning job was modified.
TrainingJobStatusCounters trainingJobStatusCounters
The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.
ObjectiveStatusCounters objectiveStatusCounters
The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.
HyperParameterTrainingJobSummary bestTrainingJob
A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.
HyperParameterTrainingJobSummary overallBestTrainingJob
If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of
IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the
best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for
the warm start tuning job.
HyperParameterTuningJobWarmStartConfig warmStartConfig
The configuration for starting the hyperparameter parameter tuning job using one or more previous 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.
String failureReason
If the tuning job failed, the reason it failed.
String imageName
The name of the image to describe.
Date creationTime
When the image was created.
String description
The description of the image.
String displayName
The name of the image as displayed.
String failureReason
When a create, update, or delete operation fails, the reason for the failure.
String imageArn
The Amazon Resource Name (ARN) of the image.
String imageName
The name of the image.
String imageStatus
The status of the image.
Date lastModifiedTime
When the image was last modified.
String roleArn
The Amazon Resource Name (ARN) of the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
String baseImage
The registry path of the container image on which this image version is based.
String containerImage
The registry path of the container image that contains this image version.
Date creationTime
When the version was created.
String failureReason
When a create or delete operation fails, the reason for the failure.
String imageArn
The Amazon Resource Name (ARN) of the image the version is based on.
String imageVersionArn
The ARN of the version.
String imageVersionStatus
The status of the version.
Date lastModifiedTime
When the version was last modified.
Integer version
The version number.
String jobName
The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
String jobName
The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
String jobDescription
The job description that you provided when you initiated the job.
String jobType
The job type that you provided when you initiated the job.
String jobArn
The Amazon Resource Name (ARN) of the job.
String roleArn
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role you provided when you initiated the job.
String status
The status of the job.
Date creationTime
A timestamp that shows when the job was created.
Date completionTime
A timestamp that shows when the job completed.
Date lastModifiedTime
A timestamp that shows when the job was last modified.
String failureReason
If the job fails, provides information why the job failed.
RecommendationJobInputConfig inputConfig
Returns information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations you provided when you initiated the job.
RecommendationJobStoppingConditions stoppingConditions
The stopping conditions that you provided when you initiated the job.
List<E> inferenceRecommendations
The recommendations made by Inference Recommender.
String labelingJobName
The name of the labeling job to return information for.
String labelingJobStatus
The processing status of the labeling job.
LabelCounters labelCounters
Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn't be labeled, and the total number of objects labeled.
String failureReason
If the job failed, the reason that it failed.
Date creationTime
The date and time that the labeling job was created.
Date lastModifiedTime
The date and time that the labeling job was last updated.
String jobReferenceCode
A unique identifier for work done as part of a labeling job.
String labelingJobName
The name assigned to the labeling job when it was created.
String labelingJobArn
The Amazon Resource Name (ARN) of the labeling job.
String labelAttributeName
The attribute used as the label in the output manifest file.
LabelingJobInputConfig inputConfig
Input configuration information for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
LabelingJobOutputConfig outputConfig
The location of the job's output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
String roleArn
The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling.
String labelCategoryConfigS3Uri
The S3 location of the JSON file that defines the categories used to label data objects. Please note the following label-category limits:
Semantic segmentation labeling jobs using automated labeling: 20 labels
Box bounding labeling jobs (all): 10 labels
The file is a JSON structure in the following format:
{
"document-version": "2018-11-28"
"labels": [
{
"label": "label 1"
},
{
"label": "label 2"
},
...
{
"label": "label n"
}
]
}
LabelingJobStoppingConditions stoppingConditions
A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.
LabelingJobAlgorithmsConfig labelingJobAlgorithmsConfig
Configuration information for automated data labeling.
HumanTaskConfig humanTaskConfig
Configuration information required for human workers to complete a labeling task.
List<E> tags
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.
LabelingJobOutput labelingJobOutput
The location of the output produced by the labeling job.
String lineageGroupName
The name of the lineage group.
String lineageGroupName
The name of the lineage group.
String lineageGroupArn
The Amazon Resource Name (ARN) of the lineage group.
String displayName
The display name of the lineage group.
String description
The description of the lineage group.
Date creationTime
The creation time of lineage group.
UserContext createdBy
Date lastModifiedTime
The last modified time of the lineage group.
UserContext lastModifiedBy
String jobDefinitionName
The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
String jobDefinitionArn
The Amazon Resource Name (ARN) of the model bias job.
String jobDefinitionName
The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
Date creationTime
The time at which the model bias job was created.
ModelBiasBaselineConfig modelBiasBaselineConfig
The baseline configuration for a model bias job.
ModelBiasAppSpecification modelBiasAppSpecification
Configures the model bias job to run a specified Docker container image.
ModelBiasJobInput modelBiasJobInput
Inputs for the model bias job.
MonitoringOutputConfig modelBiasJobOutputConfig
MonitoringResources jobResources
MonitoringNetworkConfig networkConfig
Networking options for a model bias job.
String roleArn
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.
MonitoringStoppingCondition stoppingCondition
String jobDefinitionName
The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
String jobDefinitionArn
The Amazon Resource Name (ARN) of the model explainability job.
String jobDefinitionName
The name of the explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
Date creationTime
The time at which the model explainability job was created.
ModelExplainabilityBaselineConfig modelExplainabilityBaselineConfig
The baseline configuration for a model explainability job.
ModelExplainabilityAppSpecification modelExplainabilityAppSpecification
Configures the model explainability job to run a specified Docker container image.
ModelExplainabilityJobInput modelExplainabilityJobInput
Inputs for the model explainability job.
MonitoringOutputConfig modelExplainabilityJobOutputConfig
MonitoringResources jobResources
MonitoringNetworkConfig networkConfig
Networking options for a model explainability job.
String roleArn
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.
MonitoringStoppingCondition stoppingCondition
String modelPackageGroupName
The name of the model group to describe.
String modelPackageGroupName
The name of the model group.
String modelPackageGroupArn
The Amazon Resource Name (ARN) of the model group.
String modelPackageGroupDescription
A description of the model group.
Date creationTime
The time that the model group was created.
UserContext createdBy
String modelPackageGroupStatus
The status of the model group.
String modelPackageName
The name or Amazon Resource Name (ARN) of the model package to describe.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
String modelPackageName
The name of the model package being described.
String modelPackageGroupName
If the model is a versioned model, the name of the model group that the versioned model belongs to.
Integer modelPackageVersion
The version of the model package.
String modelPackageArn
The Amazon Resource Name (ARN) of the model package.
String modelPackageDescription
A brief summary of the model package.
Date creationTime
A timestamp specifying when the model package was created.
InferenceSpecification inferenceSpecification
Details about inference jobs that can be run with models based on this model package.
SourceAlgorithmSpecification sourceAlgorithmSpecification
Details about the algorithm that was used to create the model package.
ModelPackageValidationSpecification validationSpecification
Configurations for one or more transform jobs that SageMaker runs to test the model package.
String modelPackageStatus
The current status of the model package.
ModelPackageStatusDetails modelPackageStatusDetails
Details about the current status of the model package.
Boolean certifyForMarketplace
Whether the model package is certified for listing on Amazon Web Services Marketplace.
String modelApprovalStatus
The approval status of the model package.
UserContext createdBy
MetadataProperties metadataProperties
ModelMetrics modelMetrics
Metrics for the model.
Date lastModifiedTime
The last time the model package was modified.
UserContext lastModifiedBy
String approvalDescription
A description provided for the model approval.
Map<K,V> customerMetadataProperties
The metadata properties associated with the model package versions.
DriftCheckBaselines driftCheckBaselines
Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
String domain
The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.
String task
The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.
String samplePayloadUrl
The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).
List<E> additionalInferenceSpecifications
An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
String jobDefinitionName
The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
String jobDefinitionArn
The Amazon Resource Name (ARN) of the model quality job.
String jobDefinitionName
The name of the quality job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
Date creationTime
The time at which the model quality job was created.
ModelQualityBaselineConfig modelQualityBaselineConfig
The baseline configuration for a model quality job.
ModelQualityAppSpecification modelQualityAppSpecification
Configures the model quality job to run a specified Docker container image.
ModelQualityJobInput modelQualityJobInput
Inputs for the model quality job.
MonitoringOutputConfig modelQualityJobOutputConfig
MonitoringResources jobResources
MonitoringNetworkConfig networkConfig
Networking options for a model quality job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringStoppingCondition stoppingCondition
String modelName
The name of the model.
String modelName
Name of the Amazon SageMaker model.
ContainerDefinition primaryContainer
The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.
List<E> containers
The containers in the inference pipeline.
InferenceExecutionConfig inferenceExecutionConfig
Specifies details of how containers in a multi-container endpoint are called.
String executionRoleArn
The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
VpcConfig vpcConfig
A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud
Date creationTime
A timestamp that shows when the model was created.
String modelArn
The Amazon Resource Name (ARN) of the model.
Boolean enableNetworkIsolation
If True, no inbound or outbound network calls can be made to or from the model container.
String monitoringScheduleName
Name of a previously created monitoring schedule.
String monitoringScheduleArn
The Amazon Resource Name (ARN) of the monitoring schedule.
String monitoringScheduleName
Name of the monitoring schedule.
String monitoringScheduleStatus
The status of an monitoring job.
String monitoringType
The type of the monitoring job that this schedule runs. This is one of the following values.
DATA_QUALITY - The schedule is for a data quality monitoring job.
MODEL_QUALITY - The schedule is for a model quality monitoring job.
MODEL_BIAS - The schedule is for a bias monitoring job.
MODEL_EXPLAINABILITY - The schedule is for an explainability monitoring job.
String failureReason
A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.
Date creationTime
The time at which the monitoring job was created.
Date lastModifiedTime
The time at which the monitoring job was last modified.
MonitoringScheduleConfig monitoringScheduleConfig
The configuration object that specifies the monitoring schedule and defines the monitoring job.
String endpointName
The name of the endpoint for the monitoring job.
MonitoringExecutionSummary lastMonitoringExecutionSummary
Describes metadata on the last execution to run, if there was one.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration to describe.
String notebookInstanceLifecycleConfigArn
The Amazon Resource Name (ARN) of the lifecycle configuration.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration.
List<E> onCreate
The shell script that runs only once, when you create a notebook instance.
List<E> onStart
The shell script that runs every time you start a notebook instance, including when you create the notebook instance.
Date lastModifiedTime
A timestamp that tells when the lifecycle configuration was last modified.
Date creationTime
A timestamp that tells when the lifecycle configuration was created.
String notebookInstanceName
The name of the notebook instance that you want information about.
String notebookInstanceArn
The Amazon Resource Name (ARN) of the notebook instance.
String notebookInstanceName
The name of the Amazon SageMaker notebook instance.
String notebookInstanceStatus
The status of the notebook instance.
String failureReason
If status is Failed, the reason it failed.
String url
The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.
String instanceType
The type of ML compute instance running on the notebook instance.
String subnetId
The ID of the VPC subnet.
List<E> securityGroups
The IDs of the VPC security groups.
String roleArn
The Amazon Resource Name (ARN) of the IAM role associated with the instance.
String kmsKeyId
The Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
String networkInterfaceId
The network interface IDs that Amazon SageMaker created at the time of creating the instance.
Date lastModifiedTime
A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.
Date creationTime
A timestamp. Use this parameter to return the time when the notebook instance was created
String notebookInstanceLifecycleConfigName
Returns the name of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance
String directInternetAccess
Describes whether Amazon SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to Amazon SageMaker training and endpoint services.
For more information, see Notebook Instances Are Internet-Enabled by Default.
Integer volumeSizeInGB
The size, in GB, of the ML storage volume attached to the notebook instance.
List<E> acceleratorTypes
A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
String defaultCodeRepository
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 or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
List<E> additionalCodeRepositories
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 or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
String rootAccess
Whether root access is enabled or disabled for users of the notebook instance.
Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.
String platformIdentifier
The platform identifier of the notebook instance runtime environment.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String pipelineArn
The Amazon Resource Name (ARN) of the pipeline.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String pipelineExecutionDisplayName
The display name of the pipeline execution.
String pipelineExecutionStatus
The status of the pipeline execution.
String pipelineExecutionDescription
The description of the pipeline execution.
PipelineExperimentConfig pipelineExperimentConfig
String failureReason
If the execution failed, a message describing why.
Date creationTime
The time when the pipeline execution was created.
Date lastModifiedTime
The time when the pipeline execution was modified last.
UserContext createdBy
UserContext lastModifiedBy
ParallelismConfiguration parallelismConfiguration
The parallelism configuration applied to the pipeline.
String pipelineName
The name of the pipeline to describe.
String pipelineArn
The Amazon Resource Name (ARN) of the pipeline.
String pipelineName
The name of the pipeline.
String pipelineDisplayName
The display name of the pipeline.
String pipelineDefinition
The JSON pipeline definition.
String pipelineDescription
The description of the pipeline.
String roleArn
The Amazon Resource Name (ARN) that the pipeline uses to execute.
String pipelineStatus
The status of the pipeline execution.
Date creationTime
The time when the pipeline was created.
Date lastModifiedTime
The time when the pipeline was last modified.
Date lastRunTime
The time when the pipeline was last run.
UserContext createdBy
UserContext lastModifiedBy
ParallelismConfiguration parallelismConfiguration
Lists the parallelism configuration applied to the pipeline.
String processingJobName
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
List<E> processingInputs
The inputs for a processing job.
ProcessingOutputConfig processingOutputConfig
Output configuration for the processing job.
String processingJobName
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ProcessingResources processingResources
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.
ProcessingStoppingCondition stoppingCondition
The time limit for how long the processing job is allowed to run.
AppSpecification appSpecification
Configures the processing job to run a specified container image.
Map<K,V> environment
The environment variables set in the Docker container.
NetworkConfig networkConfig
Networking options for a processing job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
ExperimentConfig experimentConfig
The configuration information used to create an experiment.
String processingJobArn
The Amazon Resource Name (ARN) of the processing job.
String processingJobStatus
Provides the status of a processing job.
String exitMessage
An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
String failureReason
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
Date processingEndTime
The time at which the processing job completed.
Date processingStartTime
The time at which the processing job started.
Date lastModifiedTime
The time at which the processing job was last modified.
Date creationTime
The time at which the processing job was created.
String monitoringScheduleArn
The ARN of a monitoring schedule for an endpoint associated with this processing job.
String autoMLJobArn
The ARN of an AutoML job associated with this processing job.
String trainingJobArn
The ARN of a training job associated with this processing job.
String projectName
The name of the project to describe.
String projectArn
The Amazon Resource Name (ARN) of the project.
String projectName
The name of the project.
String projectId
The ID of the project.
String projectDescription
The description of the project.
ServiceCatalogProvisioningDetails serviceCatalogProvisioningDetails
Information used to provision a service catalog product. For information, see What is Amazon Web Services Service Catalog.
ServiceCatalogProvisionedProductDetails serviceCatalogProvisionedProductDetails
Information about a provisioned service catalog product.
String projectStatus
The status of the project.
UserContext createdBy
Date creationTime
The time when the project was created.
Date lastModifiedTime
The timestamp when project was last modified.
UserContext lastModifiedBy
String studioLifecycleConfigName
The name of the Studio Lifecycle Configuration to describe.
String studioLifecycleConfigArn
The ARN of the Lifecycle Configuration to describe.
String studioLifecycleConfigName
The name of the Studio Lifecycle Configuration that is described.
Date creationTime
The creation time of the Studio Lifecycle Configuration.
Date lastModifiedTime
This value is equivalent to CreationTime because Studio Lifecycle Configurations are immutable.
String studioLifecycleConfigContent
The content of your Studio Lifecycle Configuration script.
String studioLifecycleConfigAppType
The App type that the Lifecycle Configuration is attached to.
String workteamArn
The Amazon Resource Name (ARN) of the subscribed work team to describe.
SubscribedWorkteam subscribedWorkteam
A Workteam instance that contains information about the work team.
String trainingJobName
The name of the training job.
String trainingJobName
Name of the model training job.
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
String tuningJobArn
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
String labelingJobArn
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
String autoMLJobArn
The Amazon Resource Name (ARN) of an AutoML job.
ModelArtifacts modelArtifacts
Information about the Amazon S3 location that is configured for storing model artifacts.
String trainingJobStatus
The status of the training job.
Amazon SageMaker provides the following training job statuses:
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.
String secondaryStatus
Provides detailed information about the state of the training job. For detailed information on the secondary
status of the training job, see StatusMessage under SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
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.
Interrupted - The job stopped because the managed spot training instances were interrupted.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed - The training job has completed.
Failed - The training job has failed. The reason for the failure is returned in the
FailureReason field of DescribeTrainingJobResponse.
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.
Stopped - The training job has stopped.
Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTraining
DownloadingTrainingImage
String failureReason
If the training job failed, the reason it failed.
Map<K,V> hyperParameters
Algorithm-specific parameters.
AlgorithmSpecification algorithmSpecification
Information about the algorithm used for training, and algorithm metadata.
String roleArn
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
List<E> inputDataConfig
An array of Channel objects that describes each data input channel.
OutputDataConfig outputDataConfig
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
ResourceConfig resourceConfig
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
VpcConfig vpcConfig
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
StoppingCondition stoppingCondition
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, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination
for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of
training are not lost.
Date creationTime
A timestamp that indicates when the training job was created.
Date trainingStartTime
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.
Date trainingEndTime
Indicates the time when the training job ends on training instances. You are billed for the time interval between
the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time
after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job
failure.
Date lastModifiedTime
A timestamp that indicates when the status of the training job was last modified.
List<E> secondaryStatusTransitions
A history of all of the secondary statuses that the training job has transitioned through.
List<E> finalMetricDataList
A collection of MetricData objects that specify the names, values, and dates and times that the
training algorithm emitted to Amazon CloudWatch.
Boolean enableNetworkIsolation
If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster
for distributed training, choose True. If you enable network isolation for training jobs that are
configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the
specified VPC, but the training container does not have network access.
Boolean enableInterContainerTrafficEncryption
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
algorithms in distributed training.
Boolean enableManagedSpotTraining
A Boolean indicating whether managed spot training is enabled (True) or not (False).
CheckpointConfig checkpointConfig
Integer trainingTimeInSeconds
The training time in seconds.
Integer billableTimeInSeconds
The billable time in seconds. Billable time refers to the absolute wall-clock time.
Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your
training cluster to get the total compute time SageMaker will bill you if you run distributed training. The
formula is as follows: BillableTimeInSeconds * InstanceCount .
You can calculate the savings from using managed spot training using the formula
(1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if
BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.
DebugHookConfig debugHookConfig
ExperimentConfig experimentConfig
List<E> debugRuleConfigurations
Configuration information for Debugger rules for debugging output tensors.
TensorBoardOutputConfig tensorBoardOutputConfig
List<E> debugRuleEvaluationStatuses
Evaluation status of Debugger rules for debugging on a training job.
ProfilerConfig profilerConfig
List<E> profilerRuleConfigurations
Configuration information for Debugger rules for profiling system and framework metrics.
List<E> profilerRuleEvaluationStatuses
Evaluation status of Debugger rules for profiling on a training job.
String profilingStatus
Profiling status of a training job.
RetryStrategy retryStrategy
The number of times to retry the job when the job fails due to an InternalServerError.
Map<K,V> environment
The environment variables to set in the Docker container.
String transformJobName
The name of the transform job that you want to view details of.
String transformJobName
The name of the transform job.
String transformJobArn
The Amazon Resource Name (ARN) of the transform job.
String transformJobStatus
The status of the transform job. If the transform job failed, the reason is returned in the
FailureReason field.
String failureReason
If the transform job failed, FailureReason describes why it failed. A transform job creates a log
file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with
Amazon CloudWatch.
String modelName
The name of the model used in the transform job.
Integer maxConcurrentTransforms
The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.
ModelClientConfig modelClientConfig
The timeout and maximum number of retries for processing a transform job invocation.
Integer maxPayloadInMB
The maximum payload size, in MB, used in the transform job.
String batchStrategy
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.
To enable the batch strategy, you must set SplitType to Line, RecordIO, or
TFRecord.
Map<K,V> environment
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput transformInput
Describes the dataset to be transformed and the Amazon S3 location where it is stored.
TransformOutput transformOutput
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
TransformResources transformResources
Describes the resources, including ML instance types and ML instance count, to use for the transform job.
Date creationTime
A timestamp that shows when the transform Job was created.
Date transformStartTime
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time
and the value of TransformEndTime.
Date transformEndTime
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.
String labelingJobArn
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
String autoMLJobArn
The Amazon Resource Name (ARN) of the AutoML transform job.
DataProcessing dataProcessing
ExperimentConfig experimentConfig
String trialComponentName
The name of the trial component to describe.
String trialComponentName
The name of the trial component.
String trialComponentArn
The Amazon Resource Name (ARN) of the trial component.
String displayName
The name of the component as displayed. If DisplayName isn't specified,
TrialComponentName is displayed.
TrialComponentSource source
The Amazon Resource Name (ARN) of the source and, optionally, the job type.
TrialComponentStatus status
The status of the component. States include:
InProgress
Completed
Failed
Date startTime
When the component started.
Date endTime
When the component ended.
Date creationTime
When the component was created.
UserContext createdBy
Who created the trial component.
Date lastModifiedTime
When the component was last modified.
UserContext lastModifiedBy
Who last modified the component.
Map<K,V> parameters
The hyperparameters of the component.
Map<K,V> inputArtifacts
The input artifacts of the component.
Map<K,V> outputArtifacts
The output artifacts of the component.
MetadataProperties metadataProperties
List<E> metrics
The metrics for the component.
String lineageGroupArn
The Amazon Resource Name (ARN) of the lineage group.
String trialName
The name of the trial to describe.
String trialName
The name of the trial.
String trialArn
The Amazon Resource Name (ARN) of the trial.
String displayName
The name of the trial as displayed. If DisplayName isn't specified, TrialName is
displayed.
String experimentName
The name of the experiment the trial is part of.
TrialSource source
The Amazon Resource Name (ARN) of the source and, optionally, the job type.
Date creationTime
When the trial was created.
UserContext createdBy
Who created the trial.
Date lastModifiedTime
When the trial was last modified.
UserContext lastModifiedBy
Who last modified the trial.
MetadataProperties metadataProperties
String domainId
The ID of the domain that contains the profile.
String userProfileArn
The user profile Amazon Resource Name (ARN).
String userProfileName
The user profile name.
String homeEfsFileSystemUid
The ID of the user's profile in the Amazon Elastic File System (EFS) volume.
String status
The status.
Date lastModifiedTime
The last modified time.
Date creationTime
The creation time.
String failureReason
The failure reason.
String singleSignOnUserIdentifier
The SSO user identifier.
String singleSignOnUserValue
The SSO user value.
UserSettings userSettings
A collection of settings.
String workforceName
The name of the private workforce whose access you want to restrict. WorkforceName is automatically
set to default when a workforce is created and cannot be modified.
Workforce workforce
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.
String workteamName
The name of the work team to return a description of.
Workteam workteam
A Workteam instance that contains information about the work team.
String deviceFleetArn
Amazon Resource Name (ARN) of the device fleet.
String deviceFleetName
Name of the device fleet.
Date creationTime
Timestamp of when the device fleet was created.
Date lastModifiedTime
Timestamp of when the device fleet was last updated.
String deviceName
The unique identifier of the device.
String deviceArn
Amazon Resource Name (ARN) of the device.
String description
A description of the device.
String deviceFleetName
The name of the fleet the device belongs to.
String iotThingName
The Amazon Web Services Internet of Things (IoT) object thing name associated with the device..
Date registrationTime
The timestamp of the last registration or de-reregistration.
Date latestHeartbeat
The last heartbeat received from the device.
List<E> models
Models on the device.
String agentVersion
Edge Manager agent version.
String domainArn
The domain's Amazon Resource Name (ARN).
String domainId
The domain ID.
String domainName
The domain name.
String status
The status.
Date creationTime
The creation time.
Date lastModifiedTime
The last modified time.
String url
The domain's URL.
List<E> securityGroupIds
The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication
between Domain-level apps and user apps.
RStudioServerProDomainSettings rStudioServerProDomainSettings
A collection of settings that configure the RStudioServerPro Domain-level app.
RStudioServerProDomainSettingsForUpdate rStudioServerProDomainSettingsForUpdate
A collection of RStudioServerPro Domain-level app settings to update.
DriftCheckBias bias
Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
DriftCheckExplainability explainability
Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
DriftCheckModelQuality modelQuality
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
DriftCheckModelDataQuality modelDataQuality
Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
FileSource configFile
The bias config file for a model.
MetricsSource preTrainingConstraints
MetricsSource postTrainingConstraints
MetricsSource constraints
FileSource configFile
The explainability config file for the model.
MetricsSource statistics
MetricsSource constraints
MetricsSource statistics
MetricsSource constraints
String sourceArn
The Amazon Resource Name (ARN) of the source lineage entity of the directed edge.
String destinationArn
The Amazon Resource Name (ARN) of the destination lineage entity of the directed edge.
String associationType
The type of the Association(Edge) between the source and destination. For example ContributedTo,
Produced, or DerivedFrom.
String modelName
The name of the model.
String modelVersion
The model version.
Long offlineDeviceCount
The number of devices that have this model version and do not have a heart beat.
Long connectedDeviceCount
The number of devices that have this model version and have a heart beat.
Long activeDeviceCount
The number of devices that have this model version, a heart beat, and are currently running.
Long samplingDeviceCount
The number of devices with this model version and are producing sample data.
String s3OutputLocation
The Amazon Simple Storage (S3) bucker URI.
String kmsKeyId
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.
String presetDeploymentType
The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
String presetDeploymentConfig
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.
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.
String edgePackagingJobArn
The Amazon Resource Name (ARN) of the edge packaging job.
String edgePackagingJobName
The name of the edge packaging job.
String edgePackagingJobStatus
The status of the edge packaging job.
String compilationJobName
The name of the SageMaker Neo compilation job.
String modelName
The name of the model.
String modelVersion
The version of the model.
Date creationTime
The timestamp of when the job was created.
Date lastModifiedTime
The timestamp of when the edge packaging job was last updated.
String type
The deployment type created by SageMaker Edge Manager. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
String artifact
The Amazon Resource Name (ARN) of the generated deployable resource.
String status
The status of the deployable resource.
String statusMessage
Returns a message describing the status of the deployed resource.
String endpointName
The name of the endpoint.
String endpointArn
The Amazon Resource Name (ARN) of the endpoint.
String endpointConfigName
The endpoint configuration associated with the endpoint.
List<E> productionVariants
A list of the production variants hosted on the endpoint. Each production variant is a model.
DataCaptureConfigSummary dataCaptureConfig
String endpointStatus
The status of the endpoint.
String failureReason
If the endpoint failed, the reason it failed.
Date creationTime
The time that the endpoint was created.
Date lastModifiedTime
The last time the endpoint was modified.
List<E> monitoringSchedules
A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor.
List<E> tags
A list of the tags associated with the endpoint. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
String endpointName
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
String localPath
Path to the filesystem where the endpoint data is available to the container.
String s3InputMode
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.
String s3DataDistributionType
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to
FullyReplicated
String featuresAttribute
The attributes of the input data that are the input features.
String inferenceAttribute
The attribute of the input data that represents the ground truth label.
String probabilityAttribute
In a classification problem, the attribute that represents the class probability.
Double probabilityThresholdAttribute
The threshold for the class probability to be evaluated as a positive result.
String startTimeOffset
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.
String endTimeOffset
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.
String instanceType
The instance types to use for the load test.
String inferenceSpecificationName
The inference specification name in the model package version.
EnvironmentParameterRanges environmentParameterRanges
The parameter you want to benchmark against.
String endpointName
The name of the endpoint made during a recommendation job.
String variantName
The name of the production variant (deployed model) made during a recommendation job.
String instanceType
The instance type recommended by Amazon SageMaker Inference Recommender.
Integer initialInstanceCount
The number of instances recommended to launch initially.
String endpointName
The name of the endpoint.
String endpointArn
The Amazon Resource Name (ARN) of the endpoint.
Date creationTime
A timestamp that shows when the endpoint was created.
Date lastModifiedTime
A timestamp that shows when the endpoint was last modified.
String endpointStatus
The status of the endpoint.
OutOfService: Endpoint is not available to take incoming requests.
Creating: CreateEndpoint is executing.
Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.
SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled
until it has completed. This maintenance operation does not change any customer-specified values such as VPC
config, KMS encryption, model, instance type, or instance count.
RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process
of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an
InService status. This transitional status only applies to an endpoint that has autoscaling enabled
and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call
or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.
InService: Endpoint is available to process incoming requests.
Deleting: DeleteEndpoint is executing.
Failed: Endpoint could not be created, updated, or re-scaled. Use
DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only
operation that can be performed on a failed endpoint.
To get a list of endpoints with a specified status, use the ListEndpointsInput$StatusEquals filter.
String experimentName
The name of the experiment.
String experimentArn
The Amazon Resource Name (ARN) of the experiment.
String displayName
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName
is displayed.
ExperimentSource source
String description
The description of the experiment.
Date creationTime
When the experiment was created.
UserContext createdBy
Who created the experiment.
Date lastModifiedTime
When the experiment was last modified.
UserContext lastModifiedBy
List<E> tags
The list of tags that are associated with the experiment. You can use Search API to search on the tags.
String experimentName
The name of an existing experiment to associate the trial component with.
String trialName
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
String trialComponentDisplayName
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
String experimentArn
The Amazon Resource Name (ARN) of the experiment.
String experimentName
The name of the experiment.
String displayName
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName
is displayed.
ExperimentSource experimentSource
Date creationTime
When the experiment was created.
Date lastModifiedTime
When the experiment was last modified.
MetricsSource report
The explainability report for a model.
String featureGroupArn
The Amazon Resource Name (ARN) of a FeatureGroup.
String featureGroupName
The name of the FeatureGroup.
String recordIdentifierFeatureName
The name of the Feature whose value uniquely identifies a Record defined in the
FeatureGroup FeatureDefinitions.
String eventTimeFeatureName
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.
List<E> featureDefinitions
A list of Features. Each Feature must include a FeatureName and a
FeatureType.
Valid FeatureTypes are Integral, Fractional and String.
FeatureNames cannot be any of the following: is_deleted, write_time,
api_invocation_time.
You can create up to 2,500 FeatureDefinitions per FeatureGroup.
Date creationTime
The time a FeatureGroup was created.
OnlineStoreConfig onlineStoreConfig
OfflineStoreConfig offlineStoreConfig
String roleArn
The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.
String featureGroupStatus
A FeatureGroup status.
OfflineStoreStatus offlineStoreStatus
String failureReason
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.
String description
A free form description of a FeatureGroup.
List<E> tags
Tags used to define a FeatureGroup.
String featureGroupName
The name of FeatureGroup.
String featureGroupArn
Unique identifier for the FeatureGroup.
Date creationTime
A timestamp indicating the time of creation time of the FeatureGroup.
String featureGroupStatus
The status of a FeatureGroup. The status can be any of the following: Creating, Created, CreateFail, Deleting or DetailFail.
OfflineStoreStatus offlineStoreStatus
Notifies you if replicating data into the OfflineStore has failed. Returns either:
Active or Blocked.
String mountPath
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.
Integer defaultUid
The default POSIX user ID (UID). If not specified, defaults to 1000.
Integer defaultGid
The default POSIX group ID (GID). If not specified, defaults to 100.
String fileSystemId
The file system id.
String fileSystemAccessMode
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.
String fileSystemType
The file system type.
String directoryPath
The full path to the directory to associate with the channel.
String name
A resource property name. For example, TrainingJobName. For valid property names, see
SearchRecord. You must specify a valid property for the resource.
String operator
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
The value of Name equals Value.
The value of Name doesn't equal Value.
The Name property exists.
The Name property does not exist.
The value of Name is greater than Value. Not supported for text properties.
The value of Name is greater than or equal to Value. Not supported for text properties.
The value of Name is less than Value. Not supported for text properties.
The value of Name is less than or equal to Value. Not supported for text properties.
The value of Name is one of the comma delimited strings in Value. Only supported for
text properties.
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."
String value
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.
String type
The type of metric with the best result.
String metricName
The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.
Float value
The value of the metric with the best result.
String s3OutputPath
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.
String kmsKeyId
The Amazon Key Management Service (KMS) key ID for server-side encryption.
String flowDefinitionName
The name of the flow definition.
String flowDefinitionArn
The Amazon Resource Name (ARN) of the flow definition.
String flowDefinitionStatus
The status of the flow definition. Valid values:
Date creationTime
The timestamp when SageMaker created the flow definition.
String failureReason
The reason why the flow definition creation failed. A failure reason is returned only when the flow definition
status is Failed.
String deviceFleetName
The name of the fleet.
String deviceFleetArn
The Amazon Resource Name (ARN) of the device.
String deviceFleetName
The name of the fleet.
EdgeOutputConfig outputConfig
The output configuration for storing sample data collected by the fleet.
String description
Description of the fleet.
Date reportGenerated
Timestamp of when the report was generated.
DeviceStats deviceStats
Status of devices.
List<E> agentVersions
The versions of Edge Manager agent deployed on the fleet.
List<E> modelStats
Status of model on device.
String lineageGroupName
The name or Amazon Resource Name (ARN) of the lineage group.
String modelPackageGroupName
The name of the model group for which to get the resource policy.
String resourcePolicy
The resource policy for the model group.
String status
Whether Service Catalog is enabled or disabled in SageMaker.
String resource
The name of the Amazon SageMaker resource to search for.
SuggestionQuery suggestionQuery
Limits the property names that are included in the response.
String repositoryUrl
The URL where the Git repository is located.
String branch
The default branch for the Git repository.
String secretArn
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}
String secretArn
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}
String humanLoopActivationConditions
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 in the Amazon SageMaker Developer Guide.
HumanLoopActivationConditionsConfig humanLoopActivationConditionsConfig
Container structure for defining under what conditions SageMaker creates a human loop.
String workteamArn
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.
String humanTaskUiArn
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.
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.
String taskTitle
A title for the human worker task.
String taskDescription
A description for the human worker task.
Integer taskCount
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.
Integer taskAvailabilityLifetimeInSeconds
The length of time that a task remains available for review by human workers.
Integer taskTimeLimitInSeconds
The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).
List<E> taskKeywords
Keywords used to describe the task so that workers can discover the task.
PublicWorkforceTaskPrice publicWorkforceTaskPrice
String awsManagedHumanLoopRequestSource
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:
String workteamArn
The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
UiConfig uiConfig
Information about the user interface that workers use to complete the labeling task.
String preHumanTaskLambdaArn
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, use
one of the following Amazon SageMaker Ground Truth Lambda function ARNs for PreHumanTaskLambdaArn.
For custom labeling workflows, see Pre-annotation Lambda.
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 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 .
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
List<E> taskKeywords
Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
String taskTitle
A title for the task for your human workers.
String taskDescription
A description of the task for your human workers.
Integer numberOfHumanWorkersPerDataObject
The number of human workers that will label an object.
Integer taskTimeLimitInSeconds
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 the maximum for this parameter depends on the task type you use:
For image and text labeling jobs, the maximum is 8 hours (28,800 seconds).
For 3D point cloud and video frame labeling jobs, the maximum is 30 days (2952,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
Integer taskAvailabilityLifetimeInSeconds
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.
Integer maxConcurrentTaskCount
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.
AnnotationConsolidationConfig annotationConsolidationConfig
Configures how labels are consolidated across human workers.
PublicWorkforceTaskPrice publicWorkforceTaskPrice
The price that you pay for each task performed by an Amazon Mechanical Turk worker.
String trainingImage
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. Amazon SageMaker supports both
registry/repository[:tag] and registry/repository[@digest] image path formats. For more
information, see Using Your Own
Algorithms with Amazon SageMaker.
String trainingInputMode
String algorithmName
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.
List<E> metricDefinitions
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
String name
The name of this hyperparameter. The name must be unique.
String description
A brief description of the hyperparameter.
String type
The type of this hyperparameter. The valid types are Integer, Continuous,
Categorical, and FreeText.
ParameterRange range
The allowed range for this hyperparameter.
Boolean isTunable
Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
Boolean isRequired
Indicates whether this hyperparameter is required.
String defaultValue
The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
String definitionName
The job definition name.
HyperParameterTuningJobObjective tuningObjective
ParameterRanges hyperParameterRanges
Map<K,V> staticHyperParameters
Specifies the values of hyperparameters that do not change for the tuning job.
HyperParameterAlgorithmSpecification algorithmSpecification
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
String roleArn
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
List<E> inputDataConfig
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
VpcConfig vpcConfig
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
OutputDataConfig outputDataConfig
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
ResourceConfig resourceConfig
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes
for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose
File as the TrainingInputMode in the algorithm specification. For distributed training
algorithms, specify an instance count greater than 1.
StoppingCondition stoppingCondition
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, Amazon SageMaker ends the training job. Use this API to cap model training costs.
Boolean enableNetworkIsolation
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
Boolean enableInterContainerTrafficEncryption
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.
Boolean enableManagedSpotTraining
A Boolean indicating whether managed spot training is enabled (True) or not (False).
CheckpointConfig checkpointConfig
RetryStrategy retryStrategy
The number of times to retry the job when the job fails due to an InternalServerError.
String trainingJobDefinitionName
The training job definition name.
String trainingJobName
The name of the training job.
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
String tuningJobName
The HyperParameter tuning job that launched the training job.
Date creationTime
The date and time that the training job was created.
Date trainingStartTime
The date and time that the training job started.
Date trainingEndTime
Specifies the time when the training job ends on training instances. You are billed for the time interval between
the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time
after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job
failure.
String trainingJobStatus
The status of the training job.
Map<K,V> tunedHyperParameters
A list of the hyperparameters for which you specified ranges to search.
String failureReason
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric finalHyperParameterTuningJobObjectiveMetric
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
String objectiveStatus
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.
String strategy
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job
it launches. To use the Bayesian search strategy, set this to Bayesian. To randomly search, set it
to Random. For information about search strategies, see How
Hyperparameter Tuning Works.
HyperParameterTuningJobObjective hyperParameterTuningJobObjective
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
ResourceLimits resourceLimits
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.
ParameterRanges parameterRanges
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
String trainingJobEarlyStoppingType
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be
one of the following values (the default value is OFF):
Training jobs launched by the hyperparameter tuning job do not use early stopping.
Amazon SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.
TuningJobCompletionCriteria tuningJobCompletionCriteria
The tuning job's completion criteria.
String hyperParameterTuningJobName
The name of the tuning job.
String hyperParameterTuningJobArn
The Amazon Resource Name (ARN) of the tuning job.
String hyperParameterTuningJobStatus
The status of the tuning job.
String strategy
Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to use for each iteration. Currently, the only valid value is Bayesian.
Date creationTime
The date and time that the tuning job was created.
Date hyperParameterTuningEndTime
The date and time that the tuning job ended.
Date lastModifiedTime
The date and time that the tuning job was modified.
TrainingJobStatusCounters trainingJobStatusCounters
The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.
ObjectiveStatusCounters objectiveStatusCounters
The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.
ResourceLimits resourceLimits
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.
List<E> parentHyperParameterTuningJobs
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.
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
String warmStartType
Specifies one of the following:
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.
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.
Date creationTime
When the image was created.
String description
The description of the image.
String displayName
The name of the image as displayed.
String failureReason
When a create, update, or delete operation fails, the reason for the failure.
String imageArn
The Amazon Resource Name (ARN) of the image.
String imageName
The name of the image.
String imageStatus
The status of the image.
Date lastModifiedTime
When the image was last modified.
String repositoryAccessMode
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.
RepositoryAuthConfig repositoryAuthConfig
(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.
Date creationTime
When the version was created.
String failureReason
When a create or delete operation fails, the reason for the failure.
String imageArn
The Amazon Resource Name (ARN) of the image the version is based on.
String imageVersionArn
The ARN of the version.
String imageVersionStatus
The status of the version.
Date lastModifiedTime
When the version was last modified.
Integer version
The version number.
String mode
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.
RecommendationMetrics metrics
The metrics used to decide what recommendation to make.
EndpointOutputConfiguration endpointConfiguration
Defines the endpoint configuration parameters.
ModelConfiguration modelConfiguration
Defines the model configuration.
String jobName
The name of the job.
String jobDescription
The job description.
String jobType
The recommendation job type.
String jobArn
The Amazon Resource Name (ARN) of the recommendation job.
String status
The status of the job.
Date creationTime
A timestamp that shows when the job was created.
Date completionTime
A timestamp that shows when the job completed.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
Date lastModifiedTime
A timestamp that shows when the job was last modified.
String failureReason
If the job fails, provides information why the job failed.
List<E> containers
The Amazon ECR registry path of the Docker image that contains the inference code.
List<E> supportedTransformInstanceTypes
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.
List<E> supportedRealtimeInferenceInstanceTypes
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.
List<E> supportedContentTypes
The supported MIME types for the input data.
List<E> supportedResponseMIMETypes
The supported MIME types for the output data.
String s3Uri
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).
String dataInputConfig
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.
TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a
dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input":[1,1024,1024,3]}
If using the CLI, {\"input\":[1,1024,1024,3]}
Examples for two inputs:
If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary
format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last)
format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats
required for the console and CLI are different.
Examples for one input:
If using the console, {"input_1":[1,3,224,224]}
If using the CLI, {\"input_1\":[1,3,224,224]}
Examples for two inputs:
If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
MXNET/ONNX/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
OutputConfig$TargetDevice (ML Model format):
shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}. In addition to
static input shapes, CoreML converter supports Flexible input shapes:
Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific
interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate
all supported input shapes, for example:
{"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
default_shape: Default input shape. You can set a default shape during conversion for both Range
Dimension and Enumerated Shapes. For example
{"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
type: Input type. Allowed values: Image and Tensor. By default, the
converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image.
Image input type requires additional input parameters such as bias and scale.
bias: If the input type is an Image, you need to provide the bias vector.
scale: If the input type is an Image, you need to provide a scale factor.
CoreML ClassifierConfig parameters can be specified using OutputConfig$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.
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 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 . For example:
"DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
"CompilerOptions": {"output_names": ["output_tensor:0"]}
String framework
Identifies the framework in which the model was trained. For example: TENSORFLOW.
String frameworkVersion
Specifies the framework version to use. This API field is only supported for the PyTorch and TensorFlow frameworks.
For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.
String name
The name of the hyperparameter to search.
String minValue
The minimum value of the hyperparameter to search.
String maxValue
The maximum value of the hyperparameter to search.
String scalingType
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
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.
ResourceSpec defaultResourceSpec
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app.
List<E> lifecycleConfigArns
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp.
ResourceSpec defaultResourceSpec
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
List<E> customImages
A list of custom SageMaker images that are configured to run as a KernelGateway app.
List<E> lifecycleConfigArns
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
List<E> kernelSpecs
The specification of the Jupyter kernels in the image.
FileSystemConfig fileSystemConfig
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
Integer totalLabeled
The total number of objects labeled.
Integer humanLabeled
The total number of objects labeled by a human worker.
Integer machineLabeled
The total number of objects labeled by automated data labeling.
Integer failedNonRetryableError
The total number of objects that could not be labeled due to an error.
Integer unlabeled
The total number of objects not yet labeled.
String labelingJobAlgorithmSpecificationArn
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
String initialActiveLearningModelArn
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.
LabelingJobResourceConfig labelingJobResourceConfig
Provides configuration information for a labeling job.
LabelingJobS3DataSource s3DataSource
The Amazon S3 location of the input data objects.
LabelingJobSnsDataSource snsDataSource
An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job.
String labelingJobName
The name of the labeling job that the work team is assigned to.
String jobReferenceCode
A unique identifier for a labeling job. You can use this to refer to a specific labeling job.
String workRequesterAccountId
The Amazon Web Services account ID of the account used to start the labeling job.
Date creationTime
The date and time that the labeling job was created.
LabelCountersForWorkteam labelCounters
Provides information about the progress of a labeling job.
Integer numberOfHumanWorkersPerDataObject
The configured number of workers per data object.
LabelingJobDataSource dataSource
The location of the input data.
LabelingJobDataAttributes dataAttributes
Attributes of the data specified by the customer.
String s3OutputPath
The Amazon S3 location to write output data.
String kmsKeyId
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.
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 in
the Amazon Simple Storage Service Developer Guide.
String snsTopicArn
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.
String volumeKmsKeyId
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.
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"
String manifestS3Uri
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 in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
String snsTopicArn
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.
String labelingJobName
The name of the labeling job.
String labelingJobArn
The Amazon Resource Name (ARN) assigned to the labeling job when it was created.
Date creationTime
The date and time that the job was created (timestamp).
Date lastModifiedTime
The date and time that the job was last modified (timestamp).
String labelingJobStatus
The current status of the labeling job.
LabelCounters labelCounters
Counts showing the progress of the labeling job.
String workteamArn
The Amazon Resource Name (ARN) of the work team assigned to the job.
String preHumanTaskLambdaArn
The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.
String annotationConsolidationLambdaArn
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.
String failureReason
If the LabelingJobStatus field is Failed, this field contains a description of the
error.
LabelingJobOutput labelingJobOutput
The location of the output produced by the labeling job.
LabelingJobInputConfig inputConfig
Input configuration for the labeling job.
String lineageGroupArn
The Amazon Resource Name (ARN) of the lineage group resource.
String lineageGroupName
The name or Amazon Resource Name (ARN) of the lineage group.
String displayName
The display name of the lineage group summary.
Date creationTime
The creation time of the lineage group summary.
Date lastModifiedTime
The last modified time of the lineage group summary.
String sourceUri
A filter that returns only actions with the specified source URI.
String actionType
A filter that returns only actions of the specified type.
Date createdAfter
A filter that returns only actions created on or after the specified time.
Date createdBefore
A filter that returns only actions created on or before the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
String nextToken
If the previous call to ListActions didn't return the full set of actions, the call returns a token
for getting the next set of actions.
Integer maxResults
The maximum number of actions to return in the response. The default value is 10.
Date creationTimeAfter
A filter that returns only algorithms created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only algorithms created before the specified time (timestamp).
Integer maxResults
The maximum number of algorithms to return in the response.
String nameContains
A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.
String nextToken
If the response to a previous ListAlgorithms request was truncated, the response includes a
NextToken. To retrieve the next set of algorithms, use the token in the next request.
String sortBy
The parameter by which to sort the results. The default is CreationTime.
String sortOrder
The sort order for the results. The default is Ascending.
Integer maxResults
The maximum number of AppImageConfigs to return in the response. The default value is 10.
String nextToken
If the previous call to ListImages didn't return the full set of AppImageConfigs, the call returns a
token for getting the next set of AppImageConfigs.
String nameContains
A filter that returns only AppImageConfigs whose name contains the specified string.
Date creationTimeBefore
A filter that returns only AppImageConfigs created on or before the specified time.
Date creationTimeAfter
A filter that returns only AppImageConfigs created on or after the specified time.
Date modifiedTimeBefore
A filter that returns only AppImageConfigs modified on or before the specified time.
Date modifiedTimeAfter
A filter that returns only AppImageConfigs modified on or after the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
String nextToken
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
Integer maxResults
Returns a list up to a specified limit.
String sortOrder
The sort order for the results. The default is Ascending.
String sortBy
The parameter by which to sort the results. The default is CreationTime.
String domainIdEquals
A parameter to search for the domain ID.
String userProfileNameEquals
A parameter to search by user profile name.
String sourceUri
A filter that returns only artifacts with the specified source URI.
String artifactType
A filter that returns only artifacts of the specified type.
Date createdAfter
A filter that returns only artifacts created on or after the specified time.
Date createdBefore
A filter that returns only artifacts created on or before the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
String nextToken
If the previous call to ListArtifacts didn't return the full set of artifacts, the call returns a
token for getting the next set of artifacts.
Integer maxResults
The maximum number of artifacts to return in the response. The default value is 10.
String sourceArn
A filter that returns only associations with the specified source ARN.
String destinationArn
A filter that returns only associations with the specified destination Amazon Resource Name (ARN).
String sourceType
A filter that returns only associations with the specified source type.
String destinationType
A filter that returns only associations with the specified destination type.
String associationType
A filter that returns only associations of the specified type.
Date createdAfter
A filter that returns only associations created on or after the specified time.
Date createdBefore
A filter that returns only associations created on or before the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
String nextToken
If the previous call to ListAssociations didn't return the full set of associations, the call
returns a token for getting the next set of associations.
Integer maxResults
The maximum number of associations to return in the response. The default value is 10.
Date creationTimeAfter
Request a list of jobs, using a filter for time.
Date creationTimeBefore
Request a list of jobs, using a filter for time.
Date lastModifiedTimeAfter
Request a list of jobs, using a filter for time.
Date lastModifiedTimeBefore
Request a list of jobs, using a filter for time.
String nameContains
Request a list of jobs, using a search filter for name.
String statusEquals
Request a list of jobs, using a filter for status.
String sortOrder
The sort order for the results. The default is Descending.
String sortBy
The parameter by which to sort the results. The default is Name.
Integer maxResults
Request a list of jobs up to a specified limit.
String nextToken
If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
String autoMLJobName
List the candidates created for the job by providing the job's name.
String statusEquals
List the candidates for the job and filter by status.
String candidateNameEquals
List the candidates for the job and filter by candidate name.
String sortOrder
The sort order for the results. The default is Ascending.
String sortBy
The parameter by which to sort the results. The default is Descending.
Integer maxResults
List the job's candidates up to a specified limit.
String nextToken
If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
Date creationTimeAfter
A filter that returns only Git repositories that were created after the specified time.
Date creationTimeBefore
A filter that returns only Git repositories that were created before the specified time.
Date lastModifiedTimeAfter
A filter that returns only Git repositories that were last modified after the specified time.
Date lastModifiedTimeBefore
A filter that returns only Git repositories that were last modified before the specified time.
Integer maxResults
The maximum number of Git repositories to return in the response.
String nameContains
A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.
String nextToken
If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a
NextToken. To get the next set of Git repositories, use the token in the next request.
String sortBy
The field to sort results by. The default is Name.
String sortOrder
The sort order for results. The default is Ascending.
List<E> codeRepositorySummaryList
Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository:
Name
Amazon Resource Name (ARN)
Creation time
Last modified time
Configuration information, including the URL location of the repository and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
String nextToken
If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a
NextToken. To get the next set of Git repositories, use the token in the next request.
String nextToken
If the result of the previous ListCompilationJobs request was truncated, the response includes a
NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.
Integer maxResults
The maximum number of model compilation jobs to return in the response.
Date creationTimeAfter
A filter that returns the model compilation jobs that were created after a specified time.
Date creationTimeBefore
A filter that returns the model compilation jobs that were created before a specified time.
Date lastModifiedTimeAfter
A filter that returns the model compilation jobs that were modified after a specified time.
Date lastModifiedTimeBefore
A filter that returns the model compilation jobs that were modified before a specified time.
String nameContains
A filter that returns the model compilation jobs whose name contains a specified string.
String statusEquals
A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.
String sortBy
The field by which to sort results. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
List<E> compilationJobSummaries
An array of CompilationJobSummary objects, each describing a model compilation job.
String nextToken
If the response is truncated, Amazon SageMaker returns this NextToken. To retrieve the next set of
model compilation jobs, use this token in the next request.
String sourceUri
A filter that returns only contexts with the specified source URI.
String contextType
A filter that returns only contexts of the specified type.
Date createdAfter
A filter that returns only contexts created on or after the specified time.
Date createdBefore
A filter that returns only contexts created on or before the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
String nextToken
If the previous call to ListContexts didn't return the full set of contexts, the call returns a
token for getting the next set of contexts.
Integer maxResults
The maximum number of contexts to return in the response. The default value is 10.
String endpointName
A filter that lists the data quality job definitions associated with the specified endpoint.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Descending.
String nextToken
If the result of the previous ListDataQualityJobDefinitions request was truncated, the response
includes a NextToken. To retrieve the next set of transform jobs, use the token in the next
request.>
Integer maxResults
The maximum number of data quality monitoring job definitions to return in the response.
String nameContains
A string in the data quality monitoring job definition name. This filter returns only data quality monitoring job definitions whose name contains the specified string.
Date creationTimeBefore
A filter that returns only data quality monitoring job definitions created before the specified time.
Date creationTimeAfter
A filter that returns only data quality monitoring job definitions created after the specified time.
List<E> jobDefinitionSummaries
A list of data quality monitoring job definitions.
String nextToken
If the result of the previous ListDataQualityJobDefinitions request was truncated, the response
includes a NextToken. To retrieve the next set of data quality monitoring job definitions, use the
token in the next request.
String nextToken
The response from the last list when returning a list large enough to need tokening.
Integer maxResults
The maximum number of results to select.
Date creationTimeAfter
Filter fleets where packaging job was created after specified time.
Date creationTimeBefore
Filter fleets where the edge packaging job was created before specified time.
Date lastModifiedTimeAfter
Select fleets where the job was updated after X
Date lastModifiedTimeBefore
Select fleets where the job was updated before X
String nameContains
Filter for fleets containing this name in their fleet device name.
String sortBy
The column to sort by.
String sortOrder
What direction to sort in.
String nextToken
The response from the last list when returning a list large enough to need tokening.
Integer maxResults
Maximum number of results to select.
Date latestHeartbeatAfter
Select fleets where the job was updated after X
String modelName
A filter that searches devices that contains this name in any of their models.
String deviceFleetName
Filter for fleets containing this name in their device fleet name.
String nextToken
The response from the last list when returning a list large enough to need tokening.
Integer maxResults
Maximum number of results to select.
Date creationTimeAfter
Select jobs where the job was created after specified time.
Date creationTimeBefore
Select jobs where the job was created before specified time.
Date lastModifiedTimeAfter
Select jobs where the job was updated after specified time.
Date lastModifiedTimeBefore
Select jobs where the job was updated before specified time.
String nameContains
Filter for jobs containing this name in their packaging job name.
String modelNameContains
Filter for jobs where the model name contains this string.
String statusEquals
The job status to filter for.
String sortBy
Use to specify what column to sort by.
String sortOrder
What direction to sort by.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Descending.
String nextToken
If the result of the previous ListEndpointConfig request was truncated, the response includes a
NextToken. To retrieve the next set of endpoint configurations, use the token in the next request.
Integer maxResults
The maximum number of training jobs to return in the response.
String nameContains
A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.
Date creationTimeBefore
A filter that returns only endpoint configurations created before the specified time (timestamp).
Date creationTimeAfter
A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).
String sortBy
Sorts the list of results. The default is CreationTime.
String sortOrder
The sort order for results. The default is Descending.
String nextToken
If the result of a ListEndpoints request was truncated, the response includes a
NextToken. To retrieve the next set of endpoints, use the token in the next request.
Integer maxResults
The maximum number of endpoints to return in the response. This value defaults to 10.
String nameContains
A string in endpoint names. This filter returns only endpoints whose name contains the specified string.
Date creationTimeBefore
A filter that returns only endpoints that were created before the specified time (timestamp).
Date creationTimeAfter
A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only endpoints that were modified before the specified timestamp.
Date lastModifiedTimeAfter
A filter that returns only endpoints that were modified after the specified timestamp.
String statusEquals
A filter that returns only endpoints with the specified status.
Date createdAfter
A filter that returns only experiments created after the specified time.
Date createdBefore
A filter that returns only experiments created before the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
String nextToken
If the previous call to ListExperiments didn't return the full set of experiments, the call returns
a token for getting the next set of experiments.
Integer maxResults
The maximum number of experiments to return in the response. The default value is 10.
String nameContains
A string that partially matches one or more FeatureGroups names. Filters FeatureGroups
by name.
String featureGroupStatusEquals
A FeatureGroup status. Filters by FeatureGroup status.
String offlineStoreStatusEquals
An OfflineStore status. Filters by OfflineStore status.
Date creationTimeAfter
Use this parameter to search for FeatureGroupss created after a specific date and time.
Date creationTimeBefore
Use this parameter to search for FeatureGroupss created before a specific date and time.
String sortOrder
The order in which feature groups are listed.
String sortBy
The value on which the feature group list is sorted.
Integer maxResults
The maximum number of results returned by ListFeatureGroups.
String nextToken
A token to resume pagination of ListFeatureGroups results.
Date creationTimeAfter
A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.
Date creationTimeBefore
A filter that returns only flow definitions that were created before the specified timestamp.
String sortOrder
An optional value that specifies whether you want the results sorted in Ascending or
Descending order.
String nextToken
A token to resume pagination.
Integer maxResults
The total number of items to return. If the total number of available items is more than the value specified in
MaxResults, then a NextToken will be provided in the output that you can use to resume
pagination.
Date creationTimeAfter
A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.
Date creationTimeBefore
A filter that returns only human task user interfaces that were created before the specified timestamp.
String sortOrder
An optional value that specifies whether you want the results sorted in Ascending or
Descending order.
String nextToken
A token to resume pagination.
Integer maxResults
The total number of items to return. If the total number of available items is more than the value specified in
MaxResults, then a NextToken will be provided in the output that you can use to resume
pagination.
String nextToken
If the result of the previous ListHyperParameterTuningJobs request was truncated, the response
includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.
Integer maxResults
The maximum number of tuning jobs to return. The default value is 10.
String sortBy
The field to sort results by. The default is Name.
String sortOrder
The sort order for results. The default is Ascending.
String nameContains
A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.
Date creationTimeAfter
A filter that returns only tuning jobs that were created after the specified time.
Date creationTimeBefore
A filter that returns only tuning jobs that were created before the specified time.
Date lastModifiedTimeAfter
A filter that returns only tuning jobs that were modified after the specified time.
Date lastModifiedTimeBefore
A filter that returns only tuning jobs that were modified before the specified time.
String statusEquals
A filter that returns only tuning jobs with the specified status.
List<E> hyperParameterTuningJobSummaries
A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the
ListHyperParameterTuningJobs request returned.
String nextToken
If the result of this ListHyperParameterTuningJobs request was truncated, the response includes a
NextToken. To retrieve the next set of tuning jobs, use the token in the next request.
Date creationTimeAfter
A filter that returns only images created on or after the specified time.
Date creationTimeBefore
A filter that returns only images created on or before the specified time.
Date lastModifiedTimeAfter
A filter that returns only images modified on or after the specified time.
Date lastModifiedTimeBefore
A filter that returns only images modified on or before the specified time.
Integer maxResults
The maximum number of images to return in the response. The default value is 10.
String nameContains
A filter that returns only images whose name contains the specified string.
String nextToken
If the previous call to ListImages didn't return the full set of images, the call returns a token
for getting the next set of images.
String sortBy
The property used to sort results. The default value is CREATION_TIME.
String sortOrder
The sort order. The default value is DESCENDING.
Date creationTimeAfter
A filter that returns only versions created on or after the specified time.
Date creationTimeBefore
A filter that returns only versions created on or before the specified time.
String imageName
The name of the image to list the versions of.
Date lastModifiedTimeAfter
A filter that returns only versions modified on or after the specified time.
Date lastModifiedTimeBefore
A filter that returns only versions modified on or before the specified time.
Integer maxResults
The maximum number of versions to return in the response. The default value is 10.
String nextToken
If the previous call to ListImageVersions didn't return the full set of versions, the call returns a
token for getting the next set of versions.
String sortBy
The property used to sort results. The default value is CREATION_TIME.
String sortOrder
The sort order. The default value is DESCENDING.
Date creationTimeAfter
A filter that returns only jobs created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only jobs created before the specified time (timestamp).
Date lastModifiedTimeAfter
A filter that returns only jobs that were last modified after the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only jobs that were last modified before the specified time (timestamp).
String nameContains
A string in the job name. This filter returns only recommendations whose name contains the specified string.
String statusEquals
A filter that retrieves only inference recommendations jobs with a specific status.
String sortBy
The parameter by which to sort the results.
String sortOrder
The sort order for the results.
String nextToken
If the response to a previous ListInferenceRecommendationsJobsRequest request was truncated, the
response includes a NextToken. To retrieve the next set of recommendations, use the token in the
next request.
Integer maxResults
The maximum number of recommendations to return in the response.
String workteamArn
The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.
Integer maxResults
The maximum number of labeling jobs to return in each page of the response.
String nextToken
If the result of the previous ListLabelingJobsForWorkteam request was truncated, the response
includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
Date creationTimeAfter
A filter that returns only labeling jobs created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only labeling jobs created before the specified time (timestamp).
String jobReferenceCodeContains
A filter the limits jobs to only the ones whose job reference code contains the specified string.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
Date creationTimeAfter
A filter that returns only labeling jobs created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only labeling jobs created before the specified time (timestamp).
Date lastModifiedTimeAfter
A filter that returns only labeling jobs modified after the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only labeling jobs modified before the specified time (timestamp).
Integer maxResults
The maximum number of labeling jobs to return in each page of the response.
String nextToken
If the result of the previous ListLabelingJobs request was truncated, the response includes a
NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
String nameContains
A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
String statusEquals
A filter that retrieves only labeling jobs with a specific status.
Date createdAfter
A timestamp to filter against lineage groups created after a certain point in time.
Date createdBefore
A timestamp to filter against lineage groups created before a certain point in time.
String sortBy
The parameter by which to sort the results. The default is CreationTime.
String sortOrder
The sort order for the results. The default is Ascending.
String nextToken
If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
Integer maxResults
The maximum number of endpoints to return in the response. This value defaults to 10.
String endpointName
Name of the endpoint to monitor for model bias.
String sortBy
Whether to sort results by the Name or CreationTime field. The default is
CreationTime.
String sortOrder
Whether to sort the results in Ascending or Descending order. The default is
Descending.
String nextToken
The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
Integer maxResults
The maximum number of model bias jobs to return in the response. The default value is 10.
String nameContains
Filter for model bias jobs whose name contains a specified string.
Date creationTimeBefore
A filter that returns only model bias jobs created before a specified time.
Date creationTimeAfter
A filter that returns only model bias jobs created after a specified time.
String endpointName
Name of the endpoint to monitor for model explainability.
String sortBy
Whether to sort results by the Name or CreationTime field. The default is
CreationTime.
String sortOrder
Whether to sort the results in Ascending or Descending order. The default is
Descending.
String nextToken
The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
Integer maxResults
The maximum number of jobs to return in the response. The default value is 10.
String nameContains
Filter for model explainability jobs whose name contains a specified string.
Date creationTimeBefore
A filter that returns only model explainability jobs created before a specified time.
Date creationTimeAfter
A filter that returns only model explainability jobs created after a specified time.
ModelMetadataSearchExpression searchExpression
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. Specify the Framework, FrameworkVersion, Domain or Task to filter supported. Filter names and values are case-sensitive.
String nextToken
If the response to a previous ListModelMetadataResponse request was truncated, the response includes
a NextToken. To retrieve the next set of model metadata, use the token in the next request.
Integer maxResults
The maximum number of models to return in the response.
Date creationTimeAfter
A filter that returns only model groups created after the specified time.
Date creationTimeBefore
A filter that returns only model groups created before the specified time.
Integer maxResults
The maximum number of results to return in the response.
String nameContains
A string in the model group name. This filter returns only model groups whose name contains the specified string.
String nextToken
If the result of the previous ListModelPackageGroups request was truncated, the response includes a
NextToken. To retrieve the next set of model groups, use the token in the next request.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
List<E> modelPackageGroupSummaryList
A list of summaries of the model groups in your Amazon Web Services account.
String nextToken
If the response is truncated, SageMaker returns this token. To retrieve the next set of model groups, use it in the subsequent request.
Date creationTimeAfter
A filter that returns only model packages created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only model packages created before the specified time (timestamp).
Integer maxResults
The maximum number of model packages to return in the response.
String nameContains
A string in the model package name. This filter returns only model packages whose name contains the specified string.
String modelApprovalStatus
A filter that returns only the model packages with the specified approval status.
String modelPackageGroupName
A filter that returns only model versions that belong to the specified model group.
String modelPackageType
A filter that returns onlyl the model packages of the specified type. This can be one of the following values.
VERSIONED - List only versioned models.
UNVERSIONED - List only unversioined models.
BOTH - List both versioned and unversioned models.
String nextToken
If the response to a previous ListModelPackages request was truncated, the response includes a
NextToken. To retrieve the next set of model packages, use the token in the next request.
String sortBy
The parameter by which to sort the results. The default is CreationTime.
String sortOrder
The sort order for the results. The default is Ascending.
List<E> modelPackageSummaryList
An array of ModelPackageSummary objects, each of which lists a model package.
String nextToken
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.
String endpointName
A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Descending.
String nextToken
If the result of the previous ListModelQualityJobDefinitions request was truncated, the response
includes a NextToken. To retrieve the next set of model quality monitoring job definitions, use the
token in the next request.
Integer maxResults
The maximum number of results to return in a call to ListModelQualityJobDefinitions.
String nameContains
A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.
Date creationTimeBefore
A filter that returns only model quality monitoring job definitions created before the specified time.
Date creationTimeAfter
A filter that returns only model quality monitoring job definitions created after the specified time.
List<E> jobDefinitionSummaries
A list of summaries of model quality monitoring job definitions.
String nextToken
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model quality monitoring job definitions, use it in the next request.
String sortBy
Sorts the list of results. The default is CreationTime.
String sortOrder
The sort order for results. The default is Descending.
String nextToken
If the response to a previous ListModels request was truncated, the response includes a
NextToken. To retrieve the next set of models, use the token in the next request.
Integer maxResults
The maximum number of models to return in the response.
String nameContains
A string in the model name. This filter returns only models whose name contains the specified string.
Date creationTimeBefore
A filter that returns only models created before the specified time (timestamp).
Date creationTimeAfter
A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).
String monitoringScheduleName
Name of a specific schedule to fetch jobs for.
String endpointName
Name of a specific endpoint to fetch jobs for.
String sortBy
Whether to sort results by Status, CreationTime, ScheduledTime field. The
default is CreationTime.
String sortOrder
Whether to sort the results in Ascending or Descending order. The default is
Descending.
String nextToken
The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
Integer maxResults
The maximum number of jobs to return in the response. The default value is 10.
Date scheduledTimeBefore
Filter for jobs scheduled before a specified time.
Date scheduledTimeAfter
Filter for jobs scheduled after a specified time.
Date creationTimeBefore
A filter that returns only jobs created before a specified time.
Date creationTimeAfter
A filter that returns only jobs created after a specified time.
Date lastModifiedTimeBefore
A filter that returns only jobs modified after a specified time.
Date lastModifiedTimeAfter
A filter that returns only jobs modified before a specified time.
String statusEquals
A filter that retrieves only jobs with a specific status.
String monitoringJobDefinitionName
Gets a list of the monitoring job runs of the specified monitoring job definitions.
String monitoringTypeEquals
A filter that returns only the monitoring job runs of the specified monitoring type.
List<E> monitoringExecutionSummaries
A JSON array in which each element is a summary for a monitoring execution.
String nextToken
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent reques
String endpointName
Name of a specific endpoint to fetch schedules for.
String sortBy
Whether to sort results by Status, CreationTime, ScheduledTime field. The
default is CreationTime.
String sortOrder
Whether to sort the results in Ascending or Descending order. The default is
Descending.
String nextToken
The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
Integer maxResults
The maximum number of jobs to return in the response. The default value is 10.
String nameContains
Filter for monitoring schedules whose name contains a specified string.
Date creationTimeBefore
A filter that returns only monitoring schedules created before a specified time.
Date creationTimeAfter
A filter that returns only monitoring schedules created after a specified time.
Date lastModifiedTimeBefore
A filter that returns only monitoring schedules modified before a specified time.
Date lastModifiedTimeAfter
A filter that returns only monitoring schedules modified after a specified time.
String statusEquals
A filter that returns only monitoring schedules modified before a specified time.
String monitoringJobDefinitionName
Gets a list of the monitoring schedules for the specified monitoring job definition.
String monitoringTypeEquals
A filter that returns only the monitoring schedules for the specified monitoring type.
List<E> monitoringScheduleSummaries
A JSON array in which each element is a summary for a monitoring schedule.
String nextToken
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent request.
String nextToken
If the result of a ListNotebookInstanceLifecycleConfigs request was truncated, the response includes
a NextToken. To get the next set of lifecycle configurations, use the token in the next request.
Integer maxResults
The maximum number of lifecycle configurations to return in the response.
String sortBy
Sorts the list of results. The default is CreationTime.
String sortOrder
The sort order for results.
String nameContains
A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.
Date creationTimeBefore
A filter that returns only lifecycle configurations that were created before the specified time (timestamp).
Date creationTimeAfter
A filter that returns only lifecycle configurations that were created after the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).
Date lastModifiedTimeAfter
A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).
String nextToken
If the response is truncated, Amazon SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request.
List<E> notebookInstanceLifecycleConfigs
An array of NotebookInstanceLifecycleConfiguration objects, each listing a lifecycle configuration.
String nextToken
If the previous call to the ListNotebookInstances is truncated, the response includes a
NextToken. You can use this token in your subsequent ListNotebookInstances request to
fetch the next set of notebook instances.
You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.
Integer maxResults
The maximum number of notebook instances to return.
String sortBy
The field to sort results by. The default is Name.
String sortOrder
The sort order for results.
String nameContains
A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string.
Date creationTimeBefore
A filter that returns only notebook instances that were created before the specified time (timestamp).
Date creationTimeAfter
A filter that returns only notebook instances that were created after the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only notebook instances that were modified before the specified time (timestamp).
Date lastModifiedTimeAfter
A filter that returns only notebook instances that were modified after the specified time (timestamp).
String statusEquals
A filter that returns only notebook instances with the specified status.
String notebookInstanceLifecycleConfigNameContains
A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.
String defaultCodeRepositoryContains
A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.
String additionalCodeRepositoryEquals
A filter that returns only notebook instances with associated with the specified git repository.
String nextToken
If the response to the previous ListNotebookInstances request was truncated, Amazon SageMaker
returns this token. To retrieve the next set of notebook instances, use the token in the next request.
List<E> notebookInstances
An array of NotebookInstanceSummary objects, one for each notebook instance.
String pipelineName
The name of the pipeline.
Date createdAfter
A filter that returns the pipeline executions that were created after a specified time.
Date createdBefore
A filter that returns the pipeline executions that were created before a specified time.
String sortBy
The field by which to sort results. The default is CreatedTime.
String sortOrder
The sort order for results.
String nextToken
If the result of the previous ListPipelineExecutions request was truncated, the response includes a
NextToken. To retrieve the next set of pipeline executions, use the token in the next request.
Integer maxResults
The maximum number of pipeline executions to return in the response.
List<E> pipelineExecutionSummaries
Contains a sorted list of pipeline execution summary objects matching the specified filters. Each run summary includes the Amazon Resource Name (ARN) of the pipeline execution, the run date, and the status. This list can be empty.
String nextToken
If the result of the previous ListPipelineExecutions request was truncated, the response includes a
NextToken. To retrieve the next set of pipeline executions, use the token in the next request.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String nextToken
If the result of the previous ListPipelineExecutionSteps request was truncated, the response
includes a NextToken. To retrieve the next set of pipeline execution steps, use the token in the
next request.
Integer maxResults
The maximum number of pipeline execution steps to return in the response.
String sortOrder
The field by which to sort results. The default is CreatedTime.
List<E> pipelineExecutionSteps
A list of PipeLineExecutionStep objects. Each PipeLineExecutionStep consists of
StepName, StartTime, EndTime, StepStatus, and Metadata. Metadata is an object with properties for each job that
contains relevant information about the job created by the step.
String nextToken
If the result of the previous ListPipelineExecutionSteps request was truncated, the response
includes a NextToken. To retrieve the next set of pipeline execution steps, use the token in the
next request.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String nextToken
If the result of the previous ListPipelineParametersForExecution request was truncated, the response
includes a NextToken. To retrieve the next set of parameters, use the token in the next request.
Integer maxResults
The maximum number of parameters to return in the response.
List<E> pipelineParameters
Contains a list of pipeline parameters. This list can be empty.
String nextToken
If the result of the previous ListPipelineParametersForExecution request was truncated, the response
includes a NextToken. To retrieve the next set of parameters, use the token in the next request.
String pipelineNamePrefix
The prefix of the pipeline name.
Date createdAfter
A filter that returns the pipelines that were created after a specified time.
Date createdBefore
A filter that returns the pipelines that were created before a specified time.
String sortBy
The field by which to sort results. The default is CreatedTime.
String sortOrder
The sort order for results.
String nextToken
If the result of the previous ListPipelines request was truncated, the response includes a
NextToken. To retrieve the next set of pipelines, use the token in the next request.
Integer maxResults
The maximum number of pipelines to return in the response.
List<E> pipelineSummaries
Contains a sorted list of PipelineSummary objects matching the specified filters. Each
PipelineSummary consists of PipelineArn, PipelineName, ExperimentName, PipelineDescription,
CreationTime, LastModifiedTime, LastRunTime, and RoleArn. This list can be empty.
String nextToken
If the result of the previous ListPipelines request was truncated, the response includes a
NextToken. To retrieve the next set of pipelines, use the token in the next request.
Date creationTimeAfter
A filter that returns only processing jobs created after the specified time.
Date creationTimeBefore
A filter that returns only processing jobs created after the specified time.
Date lastModifiedTimeAfter
A filter that returns only processing jobs modified after the specified time.
Date lastModifiedTimeBefore
A filter that returns only processing jobs modified before the specified time.
String nameContains
A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.
String statusEquals
A filter that retrieves only processing jobs with a specific status.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
String nextToken
If the result of the previous ListProcessingJobs request was truncated, the response includes a
NextToken. To retrieve the next set of processing jobs, use the token in the next request.
Integer maxResults
The maximum number of processing jobs to return in the response.
Date creationTimeAfter
A filter that returns the projects that were created after a specified time.
Date creationTimeBefore
A filter that returns the projects that were created before a specified time.
Integer maxResults
The maximum number of projects to return in the response.
String nameContains
A filter that returns the projects whose name contains a specified string.
String nextToken
If the result of the previous ListProjects request was truncated, the response includes a
NextToken. To retrieve the next set of projects, use the token in the next request.
String sortBy
The field by which to sort results. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
List<E> projectSummaryList
A list of summaries of projects.
String nextToken
If the result of the previous ListCompilationJobs request was truncated, the response includes a
NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.
Integer maxResults
The maximum number of Studio Lifecycle Configurations to return in the response. The default value is 10.
String nextToken
If the previous call to ListStudioLifecycleConfigs didn't return the full set of Lifecycle Configurations, the call returns a token for getting the next set of Lifecycle Configurations.
String nameContains
A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.
String appTypeEquals
A parameter to search for the App Type to which the Lifecycle Configuration is attached.
Date creationTimeBefore
A filter that returns only Lifecycle Configurations created on or before the specified time.
Date creationTimeAfter
A filter that returns only Lifecycle Configurations created on or after the specified time.
Date modifiedTimeBefore
A filter that returns only Lifecycle Configurations modified before the specified time.
Date modifiedTimeAfter
A filter that returns only Lifecycle Configurations modified after the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
String nameContains
A string in the work team name. This filter returns only work teams whose name contains the specified string.
String nextToken
If the result of the previous ListSubscribedWorkteams request was truncated, the response includes a
NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
Integer maxResults
The maximum number of work teams to return in each page of the response.
String resourceArn
The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.
String nextToken
If the response to the previous ListTags request is truncated, Amazon SageMaker returns this token.
To retrieve the next set of tags, use it in the subsequent request.
Integer maxResults
Maximum number of tags to return.
String hyperParameterTuningJobName
The name of the tuning job whose training jobs you want to list.
String nextToken
If the result of the previous ListTrainingJobsForHyperParameterTuningJob request was truncated, the
response includes a NextToken. To retrieve the next set of training jobs, use the token in the next
request.
Integer maxResults
The maximum number of training jobs to return. The default value is 10.
String statusEquals
A filter that returns only training jobs with the specified status.
String sortBy
The field to sort results by. The default is Name.
If the value of this field is FinalObjectiveMetricValue, any training jobs that did not return an
objective metric are not listed.
String sortOrder
The sort order for results. The default is Ascending.
List<E> trainingJobSummaries
A list of TrainingJobSummary objects that describe the training jobs that the
ListTrainingJobsForHyperParameterTuningJob request returned.
String nextToken
If the result of this ListTrainingJobsForHyperParameterTuningJob request was truncated, the response
includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.
String nextToken
If the result of the previous ListTrainingJobs request was truncated, the response includes a
NextToken. To retrieve the next set of training jobs, use the token in the next request.
Integer maxResults
The maximum number of training jobs to return in the response.
Date creationTimeAfter
A filter that returns only training jobs created after the specified time (timestamp).
Date creationTimeBefore
A filter that returns only training jobs created before the specified time (timestamp).
Date lastModifiedTimeAfter
A filter that returns only training jobs modified after the specified time (timestamp).
Date lastModifiedTimeBefore
A filter that returns only training jobs modified before the specified time (timestamp).
String nameContains
A string in the training job name. This filter returns only training jobs whose name contains the specified string.
String statusEquals
A filter that retrieves only training jobs with a specific status.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
Date creationTimeAfter
A filter that returns only transform jobs created after the specified time.
Date creationTimeBefore
A filter that returns only transform jobs created before the specified time.
Date lastModifiedTimeAfter
A filter that returns only transform jobs modified after the specified time.
Date lastModifiedTimeBefore
A filter that returns only transform jobs modified before the specified time.
String nameContains
A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.
String statusEquals
A filter that retrieves only transform jobs with a specific status.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Descending.
String nextToken
If the result of the previous ListTransformJobs request was truncated, the response includes a
NextToken. To retrieve the next set of transform jobs, use the token in the next request.
Integer maxResults
The maximum number of transform jobs to return in the response. The default value is 10.
String experimentName
A filter that returns only components that are part of the specified experiment. If you specify
ExperimentName, you can't filter by SourceArn or TrialName.
String trialName
A filter that returns only components that are part of the specified trial. If you specify TrialName
, you can't filter by ExperimentName or SourceArn.
String sourceArn
A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify
SourceArn, you can't filter by ExperimentName or TrialName.
Date createdAfter
A filter that returns only components created after the specified time.
Date createdBefore
A filter that returns only components created before the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
Integer maxResults
The maximum number of components to return in the response. The default value is 10.
String nextToken
If the previous call to ListTrialComponents didn't return the full set of components, the call
returns a token for getting the next set of components.
String experimentName
A filter that returns only trials that are part of the specified experiment.
String trialComponentName
A filter that returns only trials that are associated with the specified trial component.
Date createdAfter
A filter that returns only trials created after the specified time.
Date createdBefore
A filter that returns only trials created before the specified time.
String sortBy
The property used to sort results. The default value is CreationTime.
String sortOrder
The sort order. The default value is Descending.
Integer maxResults
The maximum number of trials to return in the response. The default value is 10.
String nextToken
If the previous call to ListTrials didn't return the full set of trials, the call returns a token
for getting the next set of trials.
String nextToken
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
Integer maxResults
Returns a list up to a specified limit.
String sortOrder
The sort order for the results. The default is Ascending.
String sortBy
The parameter by which to sort the results. The default is CreationTime.
String domainIdEquals
A parameter by which to filter the results.
String userProfileNameContains
A parameter by which to filter the results.
String sortBy
Sort workforces using the workforce name or creation date.
String sortOrder
Sort workforces in ascending or descending order.
String nameContains
A filter you can use to search for workforces using part of the workforce name.
String nextToken
A token to resume pagination.
Integer maxResults
The maximum number of workforces returned in the response.
String sortBy
The field to sort results by. The default is CreationTime.
String sortOrder
The sort order for results. The default is Ascending.
String nameContains
A string in the work team's name. This filter returns only work teams whose name contains the specified string.
String nextToken
If the result of the previous ListWorkteams request was truncated, the response includes a
NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
Integer maxResults
The maximum number of work teams to return in each page of the response.
CognitoMemberDefinition cognitoMemberDefinition
The Amazon Cognito user group that is part of the work team.
OidcMemberDefinition oidcMemberDefinition
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.
String name
The name of the metric.
String regex
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics.
String s3ModelArtifacts
The path of the S3 object that contains the model artifacts. For example,
s3://bucket-name/keynameprefix/model.tar.gz.
String imageUri
The container image to be run by the model bias job.
String configUri
JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters.
Map<K,V> environment
Sets the environment variables in the Docker container.
String baseliningJobName
The name of the baseline model bias job.
MonitoringConstraintsResource constraintsResource
EndpointInput endpointInput
MonitoringGroundTruthS3Input groundTruthS3Input
Location of ground truth labels to use in model bias job.
MetricsSource statistics
Data quality statistics for a model.
MetricsSource constraints
Data quality constraints for a model.
Boolean autoGenerateEndpointName
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.
String endpointName
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.
String endpointName
The name of the endpoint to which the model has been deployed.
If model deployment fails, this field is omitted from the response.
String artifactDigest
Provides a hash value that uniquely identifies the stored model artifacts.
String imageUri
The container image to be run by the model explainability job.
String configUri
JSON formatted S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters.
Map<K,V> environment
Sets the environment variables in the Docker container.
String baseliningJobName
The name of the baseline model explainability job.
MonitoringConstraintsResource constraintsResource
EndpointInput endpointInput
String dataInputConfig
The input configuration object for the model.
String domain
The machine learning domain of the model.
String framework
The machine learning framework of the model.
String task
The machine learning task of the model.
String model
The name of the model.
String frameworkVersion
The framework version of the model.
ModelQuality modelQuality
Metrics that measure the quality of a model.
ModelDataQuality modelDataQuality
Metrics that measure the quality of the input data for a model.
Bias bias
Metrics that measure bais in a model.
Explainability explainability
Metrics that help explain a model.
String modelPackageName
The name of the model.
String modelPackageGroupName
The model group to which the model belongs.
Integer modelPackageVersion
The version number of a versioned model.
String modelPackageArn
The Amazon Resource Name (ARN) of the model package.
String modelPackageDescription
The description of the model package.
Date creationTime
The time that the model package was created.
InferenceSpecification inferenceSpecification
SourceAlgorithmSpecification sourceAlgorithmSpecification
ModelPackageValidationSpecification validationSpecification
String modelPackageStatus
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.
ModelPackageStatusDetails modelPackageStatusDetails
Boolean certifyForMarketplace
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.
String modelApprovalStatus
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.
UserContext createdBy
MetadataProperties metadataProperties
ModelMetrics modelMetrics
Metrics for the model.
Date lastModifiedTime
The last time the model package was modified.
UserContext lastModifiedBy
String approvalDescription
A description provided when the model approval is set.
String domain
The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
String task
The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.
String samplePayloadUrl
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).
List<E> additionalInferenceSpecifications
An array of additional Inference Specification objects.
List<E> tags
A list of the tags associated with the model package. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
Map<K,V> customerMetadataProperties
The metadata properties for the model package.
DriftCheckBaselines driftCheckBaselines
Represents the drift check baselines that can be used when the model monitor is set using the model package.
String containerHostname
The DNS host name for the Docker container.
String image
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference
code must meet Amazon SageMaker requirements. Amazon SageMaker supports both
registry/repository[:tag] and registry/repository[@digest] image path formats. For more
information, see Using Your Own
Algorithms with Amazon SageMaker.
String imageDigest
An MD5 hash of the training algorithm that identifies the Docker image used for training.
String modelDataUrl
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.
String productId
The Amazon Web Services Marketplace product ID of the model package.
Map<K,V> environment
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.
ModelInput modelInput
A structure with Model Input details.
String framework
The machine learning framework of the model package container image.
String frameworkVersion
The framework version of the Model Package Container Image.
String nearestModelName
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.
String modelPackageGroupName
The name of the model group.
String modelPackageGroupArn
The Amazon Resource Name (ARN) of the model group.
String modelPackageGroupDescription
The description for the model group.
Date creationTime
The time that the model group was created.
UserContext createdBy
String modelPackageGroupStatus
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.
List<E> tags
A list of the tags associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
String modelPackageGroupName
The name of the model group.
String modelPackageGroupArn
The Amazon Resource Name (ARN) of the model group.
String modelPackageGroupDescription
A description of the model group.
Date creationTime
The time that the model group was created.
String modelPackageGroupStatus
The status of the model group.
String modelPackageName
The name of the model package.
String modelPackageGroupName
If the model package is a versioned model, the model group that the versioned model belongs to.
Integer modelPackageVersion
If the model package is a versioned model, the version of the model.
String modelPackageArn
The Amazon Resource Name (ARN) of the model package.
String modelPackageDescription
A brief description of the model package.
Date creationTime
A timestamp that shows when the model package was created.
String modelPackageStatus
The overall status of the model package.
String modelApprovalStatus
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.
String profileName
The name of the profile for the model package.
TransformJobDefinition transformJobDefinition
The TransformJobDefinition object that describes the transform job used for the validation of the
model package.
String validationRole
The IAM roles to be used for the validation of the model package.
List<E> validationProfiles
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job
that Amazon SageMaker runs to validate your model package.
MetricsSource statistics
Model quality statistics.
MetricsSource constraints
Model quality constraints.
String imageUri
The address of the container image that the monitoring job runs.
List<E> containerEntrypoint
Specifies the entrypoint for a container that the monitoring job runs.
List<E> containerArguments
An array of arguments for the container used to run the monitoring job.
String recordPreprocessorSourceUri
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.
String postAnalyticsProcessorSourceUri
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
String problemType
The machine learning problem type of the model that the monitoring job monitors.
Map<K,V> environment
Sets the environment variables in the container that the monitoring job runs.
String baseliningJobName
The name of the job that performs baselining for the monitoring job.
MonitoringConstraintsResource constraintsResource
EndpointInput endpointInput
MonitoringGroundTruthS3Input groundTruthS3Input
The ground truth label provided for the model.
String arn
The Amazon Resource Name (ARN) of the created model.
String imageUri
The container image to be run by the monitoring job.
List<E> containerEntrypoint
Specifies the entrypoint for a container used to run the monitoring job.
List<E> containerArguments
An array of arguments for the container used to run the monitoring job.
String recordPreprocessorSourceUri
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.
String postAnalyticsProcessorSourceUri
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
String baseliningJobName
The name of the job that performs baselining for the monitoring job.
MonitoringConstraintsResource constraintsResource
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
MonitoringStatisticsResource statisticsResource
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
Integer instanceCount
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.
String instanceType
The ML compute instance type for the processing job.
Integer volumeSizeInGB
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
String volumeKmsKeyId
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.
String s3Uri
The Amazon S3 URI for the constraints resource.
String monitoringScheduleName
The name of the monitoring schedule.
Date scheduledTime
The time the monitoring job was scheduled.
Date creationTime
The time at which the monitoring job was created.
Date lastModifiedTime
A timestamp that indicates the last time the monitoring job was modified.
String monitoringExecutionStatus
The status of the monitoring job.
String processingJobArn
The Amazon Resource Name (ARN) of the monitoring job.
String endpointName
The name of the endpoint used to run the monitoring job.
String failureReason
Contains the reason a monitoring job failed, if it failed.
String monitoringJobDefinitionName
The name of the monitoring job.
String monitoringType
The type of the monitoring job.
String s3Uri
The address of the Amazon S3 location of the ground truth labels.
EndpointInput endpointInput
The endpoint for a monitoring job.
MonitoringBaselineConfig baselineConfig
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
List<E> monitoringInputs
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
MonitoringOutputConfig monitoringOutputConfig
The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
MonitoringResources monitoringResources
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.
MonitoringAppSpecification monitoringAppSpecification
Configures the monitoring job to run a specified Docker container image.
MonitoringStoppingCondition stoppingCondition
Specifies a time limit for how long the monitoring job is allowed to run.
Map<K,V> environment
Sets the environment variables in the Docker container.
NetworkConfig networkConfig
Specifies networking options for an monitoring job.
String roleArn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
String monitoringJobDefinitionName
The name of the monitoring job.
String monitoringJobDefinitionArn
The Amazon Resource Name (ARN) of the monitoring job.
Date creationTime
The time that the monitoring job was created.
String endpointName
The name of the endpoint that the job monitors.
Boolean enableInterContainerTrafficEncryption
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.
Boolean enableNetworkIsolation
Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
VpcConfig vpcConfig
MonitoringS3Output s3Output
The Amazon S3 storage location where the results of a monitoring job are saved.
List<E> monitoringOutputs
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
String kmsKeyId
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.
MonitoringClusterConfig clusterConfig
The configuration for the cluster resources used to run the processing job.
String s3Uri
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
String localPath
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.
String s3UploadMode
Whether to upload the results of the monitoring job continuously or after the job completes.
String monitoringScheduleArn
The Amazon Resource Name (ARN) of the monitoring schedule.
String monitoringScheduleName
The name of the monitoring schedule.
String monitoringScheduleStatus
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.
String monitoringType
The type of the monitoring job definition to schedule.
String failureReason
If the monitoring schedule failed, the reason it failed.
Date creationTime
The time that the monitoring schedule was created.
Date lastModifiedTime
The last time the monitoring schedule was changed.
MonitoringScheduleConfig monitoringScheduleConfig
String endpointName
The endpoint that hosts the model being monitored.
MonitoringExecutionSummary lastMonitoringExecutionSummary
List<E> tags
A list of the tags associated with the monitoring schedlue. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
ScheduleConfig scheduleConfig
Configures the monitoring schedule.
MonitoringJobDefinition monitoringJobDefinition
Defines the monitoring job.
String monitoringJobDefinitionName
The name of the monitoring job definition to schedule.
String monitoringType
The type of the monitoring job definition to schedule.
String monitoringScheduleName
The name of the monitoring schedule.
String monitoringScheduleArn
The Amazon Resource Name (ARN) of the monitoring schedule.
Date creationTime
The creation time of the monitoring schedule.
Date lastModifiedTime
The last time the monitoring schedule was modified.
String monitoringScheduleStatus
The status of the monitoring schedule.
String endpointName
The name of the endpoint using the monitoring schedule.
String monitoringJobDefinitionName
The name of the monitoring job definition that the schedule is for.
String monitoringType
The type of the monitoring job definition that the schedule is for.
String s3Uri
The Amazon S3 URI for the statistics resource.
Integer maxRuntimeInSeconds
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.
String modelCacheSetting
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.
List<E> securityGroupIds
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.
List<E> subnets
The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.
String nestedPropertyName
The name of the property to use in the nested filters. The value must match a listed property name, such as
InputDataConfig.
List<E> filters
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.
Boolean enableInterContainerTrafficEncryption
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.
Boolean enableNetworkIsolation
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig vpcConfig
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration.
String notebookInstanceLifecycleConfigArn
The Amazon Resource Name (ARN) of the lifecycle configuration.
Date creationTime
A timestamp that tells when the lifecycle configuration was created.
Date lastModifiedTime
A timestamp that tells when the lifecycle configuration was last modified.
String content
A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
String notebookInstanceName
The name of the notebook instance that you want a summary for.
String notebookInstanceArn
The Amazon Resource Name (ARN) of the notebook instance.
String notebookInstanceStatus
The status of the notebook instance.
String url
The URL that you use to connect to the Jupyter instance running in your notebook instance.
String instanceType
The type of ML compute instance that the notebook instance is running on.
Date creationTime
A timestamp that shows when the notebook instance was created.
Date lastModifiedTime
A timestamp that shows when the notebook instance was last modified.
String notebookInstanceLifecycleConfigName
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.
String defaultCodeRepository
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 or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
List<E> additionalCodeRepositories
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 or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
String notificationTopicArn
The ARN for the Amazon SNS topic to which notifications should be published.
Integer succeeded
The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Integer pending
The number of training jobs that are in progress and pending evaluation of their final objective metric.
Integer failed
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.
S3StorageConfig s3StorageConfig
The Amazon Simple Storage (Amazon S3) location of OfflineStore.
Boolean disableGlueTableCreation
Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring
an OfflineStore.
DataCatalogConfig dataCatalogConfig
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
String clientId
The OIDC IdP client ID used to configure your private workforce.
String clientSecret
The OIDC IdP client secret used to configure your private workforce.
String issuer
The OIDC IdP issuer used to configure your private workforce.
String authorizationEndpoint
The OIDC IdP authorization endpoint used to configure your private workforce.
String tokenEndpoint
The OIDC IdP token endpoint used to configure your private workforce.
String userInfoEndpoint
The OIDC IdP user information endpoint used to configure your private workforce.
String logoutEndpoint
The OIDC IdP logout endpoint used to configure your private workforce.
String jwksUri
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
String clientId
The OIDC IdP client ID used to configure your private workforce.
String issuer
The OIDC IdP issuer used to configure your private workforce.
String authorizationEndpoint
The OIDC IdP authorization endpoint used to configure your private workforce.
String tokenEndpoint
The OIDC IdP token endpoint used to configure your private workforce.
String userInfoEndpoint
The OIDC IdP user information endpoint used to configure your private workforce.
String logoutEndpoint
The OIDC IdP logout endpoint used to configure your private workforce.
String jwksUri
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
OnlineStoreSecurityConfig securityConfig
Use to specify KMS Key ID (KMSKeyId) for at-rest encryption of your OnlineStore.
Boolean enableOnlineStore
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.
String kmsKeyId
The ID of the Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either IAM 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 IAM user or IAM role) to all DataPlane operations (PutRecord,
GetRecord, DeleteRecord) must have the following permissions to the
KmsKeyId:
"kms:Decrypt"
String s3OutputLocation
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix.
String targetDevice
Identifies the target device or the machine learning instance that you want to run your model on after the
compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using
TargetPlatform fields. It can be used instead of TargetPlatform.
TargetPlatform targetPlatform
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}
String compilerOptions
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.
CoreML: Compilation for the CoreML OutputConfig$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"]}
String kmsKeyId
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 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
String kmsKeyId
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:
// 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 Amazon SageMaker execution role must include permissions
to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key
for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for
OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only
allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption
to "aws:kms". For more information, see KMS-Managed Encryption
Keys 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 in the Amazon Web Services Key Management Service Developer Guide.
String s3OutputPath
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix.
Integer maxParallelExecutionSteps
The max number of steps that can be executed in parallel.
IntegerParameterRangeSpecification integerParameterRangeSpecification
A IntegerParameterRangeSpecification object that defines the possible values for an integer
hyperparameter.
ContinuousParameterRangeSpecification continuousParameterRangeSpecification
A ContinuousParameterRangeSpecification object that defines the possible values for a continuous
hyperparameter.
CategoricalParameterRangeSpecification categoricalParameterRangeSpecification
A CategoricalParameterRangeSpecification object that defines the possible values for a categorical
hyperparameter.
List<E> integerParameterRanges
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
List<E> continuousParameterRanges
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
List<E> categoricalParameterRanges
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
String hyperParameterTuningJobName
The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
String variantName
The name of the variant.
List<E> deployedImages
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the
inference images deployed on instances of this ProductionVariant.
Float currentWeight
The weight associated with the variant.
Float desiredWeight
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 operation.
Integer currentInstanceCount
The number of instances associated with the variant.
Integer desiredInstanceCount
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 operation.
String instanceType
The type of instances associated with the variant.
String acceleratorType
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.
List<E> variantStatus
The endpoint variant status which describes the current deployment stage status or operational status.
ProductionVariantServerlessConfig currentServerlessConfig
The serverless configuration for the endpoint.
Serverless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
ProductionVariantServerlessConfig desiredServerlessConfig
The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
Serverless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
String pipelineArn
The Amazon Resource Name (ARN) of the pipeline.
String pipelineName
The name of the pipeline.
String pipelineDisplayName
The display name of the pipeline.
String pipelineDescription
The description of the pipeline.
String roleArn
The Amazon Resource Name (ARN) of the role that created the pipeline.
String pipelineStatus
The status of the pipeline.
Date creationTime
The creation time of the pipeline.
Date lastModifiedTime
The time that the pipeline was last modified.
Date lastRunTime
The time when the pipeline was last run.
UserContext createdBy
UserContext lastModifiedBy
ParallelismConfiguration parallelismConfiguration
The parallelism configuration applied to the pipeline.
List<E> tags
A list of tags that apply to the pipeline.
String pipelineArn
The Amazon Resource Name (ARN) of the pipeline that was executed.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String pipelineExecutionDisplayName
The display name of the pipeline execution.
String pipelineExecutionStatus
The status of the pipeline status.
String pipelineExecutionDescription
The description of the pipeline execution.
PipelineExperimentConfig pipelineExperimentConfig
String failureReason
If the execution failed, a message describing why.
Date creationTime
The creation time of the pipeline execution.
Date lastModifiedTime
The time that the pipeline execution was last modified.
UserContext createdBy
UserContext lastModifiedBy
ParallelismConfiguration parallelismConfiguration
The parallelism configuration applied to the pipeline execution.
List<E> pipelineParameters
Contains a list of pipeline parameters. This list can be empty.
String stepName
The name of the step that is executed.
String stepDisplayName
The display name of the step.
String stepDescription
The description of the step.
Date startTime
The time that the step started executing.
Date endTime
The time that the step stopped executing.
String stepStatus
The status of the step execution.
CacheHitResult cacheHitResult
If this pipeline execution step was cached, details on the cache hit.
Integer attemptCount
The current attempt of the execution step. For more information, see Retry Policy for Amazon SageMaker Pipelines steps.
String failureReason
The reason why the step failed execution. This is only returned if the step failed its execution.
PipelineExecutionStepMetadata metadata
Metadata for the step execution.
TrainingJobStepMetadata trainingJob
The Amazon Resource Name (ARN) of the training job that was run by this step execution.
ProcessingJobStepMetadata processingJob
The Amazon Resource Name (ARN) of the processing job that was run by this step execution.
TransformJobStepMetadata transformJob
The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
TuningJobStepMetaData tuningJob
The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
ModelStepMetadata model
The Amazon Resource Name (ARN) of the model that was created by this step execution.
RegisterModelStepMetadata registerModel
The Amazon Resource Name (ARN) of the model package the model was registered to by this step execution.
ConditionStepMetadata condition
The outcome of the condition evaluation that was run by this step execution.
CallbackStepMetadata callback
The URL of the Amazon SQS queue used by this step execution, the pipeline generated token, and a list of output parameters.
LambdaStepMetadata lambda
The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution and a list of output parameters.
QualityCheckStepMetadata qualityCheck
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.
ClarifyCheckStepMetadata clarifyCheck
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.
EMRStepMetadata eMR
The configurations and outcomes of an EMR step execution.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
Date startTime
The start time of the pipeline execution.
String pipelineExecutionStatus
The status of the pipeline execution.
String pipelineExecutionDescription
The description of the pipeline execution.
String pipelineExecutionDisplayName
The display name of the pipeline execution.
String pipelineArn
The Amazon Resource Name (ARN) of the pipeline.
String pipelineName
The name of the pipeline.
String pipelineDisplayName
The display name of the pipeline.
String pipelineDescription
The description of the pipeline.
String roleArn
The Amazon Resource Name (ARN) that the pipeline used to execute.
Date creationTime
The creation time of the pipeline.
Date lastModifiedTime
The time that the pipeline was last modified.
Date lastExecutionTime
The last time that a pipeline execution began.
Integer instanceCount
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.
String instanceType
The ML compute instance type for the processing job.
Integer volumeSizeInGB
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.
String volumeKmsKeyId
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.
For more information about local instance storage encryption, see SSD Instance Store Volumes.
String featureGroupName
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.
String inputName
The name for the processing job input.
Boolean appManaged
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.
ProcessingS3Input s3Input
Configuration for downloading input data from Amazon S3 into the processing container.
DatasetDefinition datasetDefinition
Configuration for a Dataset Definition input.
List<E> processingInputs
List of input configurations for the processing job.
ProcessingOutputConfig processingOutputConfig
String processingJobName
The name of the processing job.
ProcessingResources processingResources
ProcessingStoppingCondition stoppingCondition
AppSpecification appSpecification
Map<K,V> environment
Sets the environment variables in the Docker container.
NetworkConfig networkConfig
String roleArn
The ARN of the role used to create the processing job.
ExperimentConfig experimentConfig
String processingJobArn
The ARN of the processing job.
String processingJobStatus
The status of the processing job.
String exitMessage
A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
String failureReason
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
Date processingEndTime
The time that the processing job ended.
Date processingStartTime
The time that the processing job started.
Date lastModifiedTime
The time the processing job was last modified.
Date creationTime
The time the processing job was created.
String monitoringScheduleArn
The ARN of a monitoring schedule for an endpoint associated with this processing job.
String autoMLJobArn
The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
String trainingJobArn
The ARN of the training job associated with this processing job.
List<E> tags
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
String arn
The Amazon Resource Name (ARN) of the processing job.
String processingJobName
The name of the processing job.
String processingJobArn
The Amazon Resource Name (ARN) of the processing job..
Date creationTime
The time at which the processing job was created.
Date processingEndTime
The time at which the processing job completed.
Date lastModifiedTime
A timestamp that indicates the last time the processing job was modified.
String processingJobStatus
The status of the processing job.
String failureReason
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
String exitMessage
An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
String outputName
The name for the processing job output.
ProcessingS3Output s3Output
Configuration for processing job outputs in Amazon S3.
ProcessingFeatureStoreOutput featureStoreOutput
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only
supported when AppManaged is specified.
Boolean appManaged
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.
List<E> outputs
An array of outputs configuring the data to upload from the processing container.
String kmsKeyId
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.
ProcessingClusterConfig clusterConfig
The configuration for the resources in a cluster used to run the processing job.
String s3Uri
The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
String localPath
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).
String s3DataType
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.
String s3InputMode
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.
String s3DataDistributionType
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.
String s3CompressionType
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.
String s3Uri
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
String localPath
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.
String s3UploadMode
Whether to upload the results of the processing job continuously or after the job completes.
Integer maxRuntimeInSeconds
Specifies the maximum runtime in seconds.
String variantName
The name of the production variant.
String modelName
The name of the model that you want to host. This is the name that you specified when creating the model.
Integer initialInstanceCount
Number of instances to launch initially.
String instanceType
The ML compute instance type.
Float initialVariantWeight
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.
String acceleratorType
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.
ProductionVariantCoreDumpConfig coreDumpConfig
Specifies configuration for a core dump from the model container when the process crashes.
ProductionVariantServerlessConfig serverlessConfig
The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
Serverless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
String destinationS3Uri
The Amazon S3 bucket to send the core dump to.
String kmsKeyId
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon 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 Amazon SageMaker execution role must include permissions
to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key
for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for
OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only
allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption
to "aws:kms". For more information, see KMS-Managed Encryption Keys 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 in the Amazon Web Services Key Management Service Developer Guide.
String status
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.
String statusMessage
A message that describes the status of the production variant.
Date startTime
The start time of the current status change.
String variantName
The name of the variant.
List<E> deployedImages
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the
inference images deployed on instances of this ProductionVariant.
Float currentWeight
The weight associated with the variant.
Float desiredWeight
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
Integer currentInstanceCount
The number of instances associated with the variant.
Integer desiredInstanceCount
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
List<E> variantStatus
The endpoint variant status which describes the current deployment stage status or operational status.
ProductionVariantServerlessConfig currentServerlessConfig
The serverless configuration for the endpoint.
Serverless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
ProductionVariantServerlessConfig desiredServerlessConfig
The serverless configuration requested for the endpoint update.
Serverless Inference is in preview release for Amazon SageMaker and is subject to change. We do not recommend using this feature in production environments.
String s3OutputPath
Path to Amazon S3 storage location for system and framework metrics.
Long profilingIntervalInMilliseconds
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.
Map<K,V> profilingParameters
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.
String s3OutputPath
Path to Amazon S3 storage location for system and framework metrics.
Long profilingIntervalInMilliseconds
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.
Map<K,V> profilingParameters
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.
Boolean disableProfiler
To disable Debugger monitoring and profiling, set to True.
String ruleConfigurationName
The name of the rule configuration. It must be unique relative to other rule configuration names.
String localPath
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
String s3OutputPath
Path to Amazon S3 storage location for rules.
String ruleEvaluatorImage
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
String instanceType
The instance type to deploy a Debugger custom rule for profiling a training job.
Integer volumeSizeInGB
The size, in GB, of the ML storage volume attached to the processing instance.
Map<K,V> ruleParameters
Runtime configuration for rule container.
String ruleConfigurationName
The name of the rule configuration.
String ruleEvaluationJobArn
The Amazon Resource Name (ARN) of the rule evaluation job.
String ruleEvaluationStatus
Status of the rule evaluation.
String statusDetails
Details from the rule evaluation.
Date lastModifiedTime
Timestamp when the rule evaluation status was last modified.
String projectArn
The Amazon Resource Name (ARN) of the project.
String projectName
The name of the project.
String projectId
The ID of the project.
String projectDescription
The description of the project.
ServiceCatalogProvisioningDetails serviceCatalogProvisioningDetails
ServiceCatalogProvisionedProductDetails serviceCatalogProvisionedProductDetails
String projectStatus
The status of the project.
UserContext createdBy
Who created the project.
Date creationTime
A timestamp specifying when the project was created.
List<E> tags
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.
Date lastModifiedTime
A timestamp container for when the project was last modified.
UserContext lastModifiedBy
String projectName
The name of the project.
String projectDescription
The description of the project.
String projectArn
The Amazon Resource Name (ARN) of the project.
String projectId
The ID of the project.
Date creationTime
The time that the project was created.
String projectStatus
The status of the project.
String propertyNameHint
Text that begins a property's name.
String propertyName
A suggested property name based on what you entered in the search textbox in the Amazon SageMaker console.
USD amountInUsd
Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
String modelPackageGroupArn
The Amazon Resource Name (ARN) of the model package group.
String checkType
The type of the Quality check step.
String baselineUsedForDriftCheckStatistics
The Amazon S3 URI of the baseline statistics file used for the drift check.
String baselineUsedForDriftCheckConstraints
The Amazon S3 URI of the baseline constraints file used for the drift check.
String calculatedBaselineStatistics
The Amazon S3 URI of the newly calculated baseline statistics file.
String calculatedBaselineConstraints
The Amazon S3 URI of the newly calculated baseline constraints file.
String modelPackageGroupName
The model package group name.
String violationReport
The Amazon S3 URI of violation report if violations are detected.
String checkJobArn
The Amazon Resource Name (ARN) of the Quality check processing job that was run by this step execution.
Boolean skipCheck
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.
Boolean registerNewBaseline
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.
List<E> types
Filter the lineage entities connected to the StartArn by type. For example: DataSet,
Model, Endpoint, or ModelDeployment.
List<E> lineageTypes
Filter the lineage entities connected to the StartArn(s) by the type of the lineage entity.
Date createdBefore
Filter the lineage entities connected to the StartArn(s) by created date.
Date createdAfter
Filter the lineage entities connected to the StartArn(s) after the create date.
Date modifiedBefore
Filter the lineage entities connected to the StartArn(s) before the last modified date.
Date modifiedAfter
Filter the lineage entities connected to the StartArn(s) after the last modified date.
Map<K,V> properties
Filter the lineage entities connected to the StartArn(s) by a set if property key value pairs. If
multiple pairs are provided, an entity will be included in the results if it matches any of the provided pairs.
List<E> startArns
A list of resource Amazon Resource Name (ARN) that represent the starting point for your lineage query.
String direction
Associations between lineage entities are directed. This parameter determines the direction from the StartArn(s) the query will look.
Boolean includeEdges
Setting this value to True will retrieve not only the entities of interest but also the Associations and
lineage entities on the path. Set to False to only return lineage entities that match your query.
QueryFilters filters
A set of filtering parameters that allow you to specify which entities should be returned.
Properties - Key-value pairs to match on the lineage entities' properties.
LineageTypes - A set of lineage entity types to match on. For example: TrialComponent,
Artifact, or Context.
CreatedBefore - Filter entities created before this date.
ModifiedBefore - Filter entities modified before this date.
ModifiedAfter - Filter entities modified after this date.
Integer maxDepth
The maximum depth in lineage relationships from the StartArns that will be traversed. Depth is a
measure of the number of Associations from the StartArn entity to the matched results.
Integer maxResults
Limits the number of vertices in the results. Use the NextToken in a response to to retrieve the
next page of results.
String nextToken
Limits the number of vertices in the request. Use the NextToken in a response to to retrieve the
next page of results.
List<E> vertices
A list of vertices connected to the start entity(ies) in the lineage graph.
List<E> edges
A list of edges that connect vertices in the response.
String nextToken
Limits the number of vertices in the response. Use the NextToken in a response to to retrieve the
next page of results.
String modelPackageVersionArn
The Amazon Resource Name (ARN) of a versioned model package.
Integer jobDurationInSeconds
Specifies the maximum duration of the job, in seconds.>
TrafficPattern trafficPattern
Specifies the traffic pattern of the job.
RecommendationJobResourceLimit resourceLimit
Defines the resource limit of the job.
List<E> endpointConfigurations
Specifies the endpoint configuration to use for a job.
Integer maxInvocations
The maximum number of requests per minute expected for the endpoint.
List<E> modelLatencyThresholds
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.
Float costPerHour
Defines the cost per hour for the instance.
Float costPerInference
Defines the cost per inference for the instance .
Integer maxInvocations
The expected maximum number of requests per minute for the instance.
Integer modelLatency
The expected model latency at maximum invocation per minute for the instance.
String clusterId
String database
String dbUser
String queryString
String clusterRoleArn
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
String outputS3Uri
The location in Amazon S3 where the Redshift query results are stored.
String kmsKeyId
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
String outputFormat
String outputCompression
String arn
The Amazon Resource Name (ARN) of the model package.
String input
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".
UiTemplate uiTemplate
A Template object containing the worker UI template to render.
RenderableTask task
A RenderableTask object containing a representative task to render.
String roleArn
The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.
String humanTaskUiArn
The HumanTaskUiArn of the worker UI that you want to render. Do not provide a
HumanTaskUiArn if you use the UiTemplate parameter.
See a list of available Human Ui Amazon Resource Names (ARNs) in UiConfig.
String repositoryCredentialsProviderArn
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 in the Amazon Web Services Lambda Developer Guide.
AutoMLJobObjective autoMLJobObjective
String problemType
The problem type.
AutoMLJobCompletionCriteria completionCriteria
String instanceType
The ML compute instance type.
Integer instanceCount
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
Integer volumeSizeInGB
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML
storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When
using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2
storage. You can't request a VolumeSizeInGB greater than the total size of the local instance
storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.
String volumeKmsKeyId
The 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 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.
For more information about local instance storage encryption, see SSD Instance Store Volumes.
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"
String sageMakerImageArn
The ARN of the SageMaker image that the image version belongs to.
String sageMakerImageVersionArn
The ARN of the image version created on the instance.
String instanceType
The instance type that the image version runs on.
String lifecycleConfigArn
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
String homeEfsFileSystem
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.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String clientRequestToken
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
ParallelismConfiguration parallelismConfiguration
This configuration, if specified, overrides the parallelism configuration of the parent pipeline.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
Integer maximumRetryAttempts
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to
STARTING.
String accessStatus
Indicates whether the current user has access to the RStudioServerPro app.
String userGroup
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.
String domainExecutionRoleArn
The ARN of the execution role for the RStudioServerPro Domain-level app.
String rStudioConnectUrl
A URL pointing to an RStudio Connect server.
String rStudioPackageManagerUrl
A URL pointing to an RStudio Package Manager server.
ResourceSpec defaultResourceSpec
String domainExecutionRoleArn
The execution role for the RStudioServerPro Domain-level app.
ResourceSpec defaultResourceSpec
String s3DataType
If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all
objects that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file
containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file
in JSON lines format. This file contains the data you want to use for model training.
AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
String s3Uri
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 Amazon SageMaker uses to
perform tasks on your behalf.
String s3DataDistributionType
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for
model training, specify FullyReplicated.
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model
training, specify ShardedByS3Key. If there are n ML compute instances launched for a training
job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on
each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when
TrainingInputMode is set to File), this copies 1/n of the number of objects.
List<E> attributeNames
A list of one or more attribute names to use that are found in a specified augmented manifest file.
String s3Uri
The S3 URI, or location in Amazon S3, of OfflineStore.
S3 URIs have a format similar to the following: s3://example-bucket/prefix/.
String kmsKeyId
The Amazon Web Services Key Management Service (KMS) key ID 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"
String resolvedOutputS3Uri
The S3 path where offline records are written.
String scheduleExpression
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.
List<E> filters
A list of filter objects.
List<E> nestedFilters
A list of nested filter objects.
List<E> subExpressions
A list of search expression objects.
String operator
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.
TrainingJob trainingJob
The properties of a training job.
Experiment experiment
The properties of an experiment.
Trial trial
The properties of a trial.
TrialComponent trialComponent
The properties of a trial component.
Endpoint endpoint
ModelPackage modelPackage
ModelPackageGroup modelPackageGroup
Pipeline pipeline
PipelineExecution pipelineExecution
FeatureGroup featureGroup
Project project
The properties of a project.
String resource
The name of the Amazon SageMaker resource to search for.
SearchExpression searchExpression
A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must
provide at least one subexpression, filter, or nested filter. The maximum number of recursive
SubExpressions, NestedFilters, and Filters that can be included in a
SearchExpression object is 50.
String sortBy
The name of the resource property used to sort the SearchResults. The default is
LastModifiedTime.
String sortOrder
How SearchResults are ordered. Valid values are Ascending or Descending.
The default is Descending.
String nextToken
If more than MaxResults resources match the specified SearchExpression, the response
includes a NextToken. The NextToken can be passed to the next
SearchRequest to continue retrieving results.
Integer maxResults
The maximum number of results to return.
String status
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
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 - The training job has completed.
Failed - The training job has failed. The reason for the failure is returned in the
FailureReason field of DescribeTrainingJobResponse.
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
Date startTime
A timestamp that shows when the training job transitioned to the current secondary status state.
Date endTime
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.
String statusMessage
A detailed description of the progress within a secondary status.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting 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.
Downloading the training image.
Training image download completed. Training in progress.
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and
SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example,
at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
String callbackToken
The pipeline generated token from the Amazon SQS queue.
String failureReason
A message describing why the step failed.
String clientRequestToken
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String callbackToken
The pipeline generated token from the Amazon SQS queue.
List<E> outputParameters
A list of the output parameters of the callback step.
String clientRequestToken
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String provisionedProductId
The ID of the provisioned product.
String provisionedProductStatusMessage
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.
String productId
The ID of the product to provision.
String provisioningArtifactId
The ID of the provisioning artifact.
String pathId
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.
List<E> provisioningParameters
A list of key value pairs that you specify when you provision a product.
String notebookOutputOption
Whether to include the notebook cell output when sharing the notebook. The default is Disabled.
String s3OutputPath
When NotebookOutputOption is Allowed, the Amazon S3 bucket used to store the shared
notebook snapshots.
String s3KmsKeyId
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.
Long seed
Determines the shuffling order in ShuffleConfig value.
String modelDataUrl
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 algorithm.
String algorithmName
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
List<E> cidrs
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
The following Length Constraints apply to individual CIDR values in the CIDR value list.
String monitoringScheduleName
The name of the schedule to start.
String notebookInstanceName
The name of the notebook instance to start.
String pipelineName
The name of the pipeline.
String pipelineExecutionDisplayName
The display name of the pipeline execution.
List<E> pipelineParameters
Contains a list of pipeline parameters. This list can be empty.
String pipelineExecutionDescription
The description of the pipeline execution.
String clientRequestToken
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
ParallelismConfiguration parallelismConfiguration
This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String autoMLJobName
The name of the object you are requesting.
String compilationJobName
The name of the model compilation job to stop.
String edgePackagingJobName
The name of the edge packaging job.
String hyperParameterTuningJobName
The name of the tuning job to stop.
String jobName
The name of the job you want to stop.
String labelingJobName
The name of the labeling job to stop.
String monitoringScheduleName
The name of the schedule to stop.
String notebookInstanceName
The name of the notebook instance to terminate.
Integer maxRuntimeInSeconds
The maximum length of time, in seconds, that a training or compilation job can run.
For compilation jobs, if the job does not complete during this time, you will receive a TimeOut
error. We recommend starting with 900 seconds and increase as necessary based on your model.
For all other jobs, if the job does not complete during this time, Amazon 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.
Integer maxWaitTimeInSeconds
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, Amazon 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.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String clientRequestToken
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String processingJobName
The name of the processing job to stop.
String trainingJobName
The name of the training job to stop.
String transformJobName
The name of the batch transform job to stop.
String studioLifecycleConfigArn
The Amazon Resource Name (ARN) of the Lifecycle Configuration.
String studioLifecycleConfigName
The name of the Studio Lifecycle Configuration.
Date creationTime
The creation time of the Studio Lifecycle Configuration.
Date lastModifiedTime
This value is equivalent to CreationTime because Studio Lifecycle Configurations are immutable.
String studioLifecycleConfigAppType
The App type to which the Lifecycle Configuration is attached.
String workteamArn
The Amazon Resource Name (ARN) of the vendor that you have subscribed.
String marketplaceTitle
The title of the service provided by the vendor in the Amazon Marketplace.
String sellerName
The name of the vendor in the Amazon Marketplace.
String marketplaceDescription
The description of the vendor from the Amazon Marketplace.
String listingId
Marketplace product listing ID.
PropertyNameQuery propertyNameQuery
Defines a property name hint. Only property names that begin with the specified hint are included in the response.
String os
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}
String arch
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.
String accelerator
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
ResourceSpec defaultResourceSpec
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
String type
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.
Integer waitIntervalInSeconds
The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.
CapacitySize canarySize
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.
CapacitySize linearStepSize
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.
String trainingJobName
The name of the training job.
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
String tuningJobArn
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
String labelingJobArn
The Amazon Resource Name (ARN) of the labeling job.
String autoMLJobArn
The Amazon Resource Name (ARN) of the job.
ModelArtifacts modelArtifacts
Information about the Amazon S3 location that is configured for storing model artifacts.
String trainingJobStatus
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.
String secondaryStatus
Provides detailed information about the state of the training job. For detailed information about the secondary
status of the training job, see StatusMessage under SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
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 - The training job has completed.
Failed - The training job has failed. The reason for the failure is returned in the
FailureReason field of DescribeTrainingJobResponse.
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
String failureReason
If the training job failed, the reason it failed.
Map<K,V> hyperParameters
Algorithm-specific parameters.
AlgorithmSpecification algorithmSpecification
Information about the algorithm used for training, and algorithm metadata.
String roleArn
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
List<E> inputDataConfig
An array of Channel objects that describes each data input channel.
OutputDataConfig outputDataConfig
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
ResourceConfig resourceConfig
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
VpcConfig vpcConfig
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
StoppingCondition stoppingCondition
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, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination
for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of
training are not lost.
Date creationTime
A timestamp that indicates when the training job was created.
Date trainingStartTime
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.
Date trainingEndTime
Indicates the time when the training job ends on training instances. You are billed for the time interval between
the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time
after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job
failure.
Date lastModifiedTime
A timestamp that indicates when the status of the training job was last modified.
List<E> secondaryStatusTransitions
A history of all of the secondary statuses that the training job has transitioned through.
List<E> finalMetricDataList
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.
Boolean enableNetworkIsolation
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.
Boolean enableInterContainerTrafficEncryption
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.
Boolean enableManagedSpotTraining
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.
CheckpointConfig checkpointConfig
Integer trainingTimeInSeconds
The training time in seconds.
Integer billableTimeInSeconds
The billable time in seconds.
DebugHookConfig debugHookConfig
ExperimentConfig experimentConfig
List<E> debugRuleConfigurations
Information about the debug rule configuration.
TensorBoardOutputConfig tensorBoardOutputConfig
List<E> debugRuleEvaluationStatuses
Information about the evaluation status of the rules for the training job.
Map<K,V> environment
The environment variables to set in the Docker container.
RetryStrategy retryStrategy
The number of times to retry the job when the job fails due to an InternalServerError.
List<E> tags
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.
String trainingInputMode
Map<K,V> hyperParameters
The hyperparameters used for the training job.
List<E> inputDataConfig
An array of Channel objects, each of which specifies an input source.
OutputDataConfig outputDataConfig
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
ResourceConfig resourceConfig
The resources, including the ML compute instances and ML storage volumes, to use for model training.
StoppingCondition stoppingCondition
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, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
Integer completed
The number of completed training jobs launched by the hyperparameter tuning job.
Integer inProgress
The number of in-progress training jobs launched by a hyperparameter tuning job.
Integer retryableError
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.
Integer nonRetryableError
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.
Integer stopped
The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
String arn
The Amazon Resource Name (ARN) of the training job that was run by this step execution.
String trainingJobName
The name of the training job that you want a summary for.
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
Date creationTime
A timestamp that shows when the training job was created.
Date trainingEndTime
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).
Date lastModifiedTime
Timestamp when the training job was last modified.
String trainingJobStatus
The status of the training job.
String trainingImage
The Amazon ECR registry path of the Docker image that contains the training algorithm.
String trainingImageDigest
An MD5 hash of the training algorithm that identifies the Docker image used for training.
List<E> supportedHyperParameters
A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This
is required if the algorithm supports automatic model tuning.>
List<E> supportedTrainingInstanceTypes
A list of the instance types that this algorithm can use for training.
Boolean supportsDistributedTraining
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
List<E> metricDefinitions
A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
List<E> trainingChannels
A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
List<E> supportedTuningJobObjectiveMetrics
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
TransformS3DataSource s3DataSource
The S3 location of the data source that is associated with a channel.
TransformDataSource dataSource
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
String contentType
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.
String compressionType
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.
String splitType
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 in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the
TensorFlow documentation.
String transformJobName
The name of the transform job.
String transformJobArn
The Amazon Resource Name (ARN) of the transform job.
String transformJobStatus
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.
String failureReason
If the transform job failed, the reason it failed.
String modelName
The name of the model associated with the transform job.
Integer maxConcurrentTransforms
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.
ModelClientConfig modelClientConfig
Integer maxPayloadInMB
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.
String batchStrategy
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.
Map<K,V> environment
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput transformInput
TransformOutput transformOutput
TransformResources transformResources
Date creationTime
A timestamp that shows when the transform Job was created.
Date transformStartTime
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time
and the value of TransformEndTime.
Date transformEndTime
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.
String labelingJobArn
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
String autoMLJobArn
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing dataProcessing
ExperimentConfig experimentConfig
List<E> tags
A list of tags associated with the transform job.
Integer maxConcurrentTransforms
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
Integer maxPayloadInMB
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
String batchStrategy
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.
Map<K,V> environment
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput transformInput
A description of the input source and the way the transform job consumes it.
TransformOutput transformOutput
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
TransformResources transformResources
Identifies the ML compute instances for the transform job.
String arn
The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
String transformJobName
The name of the transform job.
String transformJobArn
The Amazon Resource Name (ARN) of the transform job.
Date creationTime
A timestamp that shows when the transform Job was created.
Date transformEndTime
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.
Date lastModifiedTime
Indicates when the transform job was last modified.
String transformJobStatus
The status of the transform job.
String failureReason
If the transform job failed, the reason it failed.
String s3OutputPath
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.
String accept
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.
String assembleWith
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.
String kmsKeyId
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 in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
String instanceType
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.largeinstance types.
Integer instanceCount
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value
greater than 1. The default value is 1.
String volumeKmsKeyId
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.
For more information about local instance storage encryption, see SSD Instance Store Volumes.
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
String s3DataType
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
String s3Uri
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.
String trialName
The name of the trial.
String trialArn
The Amazon Resource Name (ARN) of the trial.
String displayName
The name of the trial as displayed. If DisplayName isn't specified, TrialName is
displayed.
String experimentName
The name of the experiment the trial is part of.
TrialSource source
Date creationTime
When the trial was created.
UserContext createdBy
Who created the trial.
Date lastModifiedTime
Who last modified the trial.
UserContext lastModifiedBy
MetadataProperties metadataProperties
List<E> tags
The list of tags that are associated with the trial. You can use Search API to search on the tags.
List<E> trialComponentSummaries
A list of the components associated with the trial. For each component, a summary of the component's properties is included.
String trialComponentName
The name of the trial component.
String displayName
The name of the component as displayed. If DisplayName isn't specified,
TrialComponentName is displayed.
String trialComponentArn
The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource source
The Amazon Resource Name (ARN) and job type of the source of the component.
TrialComponentStatus status
Date startTime
When the component started.
Date endTime
When the component ended.
Date creationTime
When the component was created.
UserContext createdBy
Who created the trial component.
Date lastModifiedTime
When the component was last modified.
UserContext lastModifiedBy
Map<K,V> parameters
The hyperparameters of the component.
Map<K,V> inputArtifacts
The input artifacts of the component.
Map<K,V> outputArtifacts
The output artifacts of the component.
List<E> metrics
The metrics for the component.
MetadataProperties metadataProperties
TrialComponentSourceDetail sourceDetail
Details of the source of the component.
String lineageGroupArn
The Amazon Resource Name (ARN) of the lineage group resource.
List<E> tags
The list of tags that are associated with the component. You can use Search API to search on the tags.
List<E> parents
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.
String mediaType
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.
String value
The location of the artifact.
String metricName
The name of the metric.
String sourceArn
The Amazon Resource Name (ARN) of the source.
Date timeStamp
When the metric was last updated.
Double max
The maximum value of the metric.
Double min
The minimum value of the metric.
Double last
The most recent value of the metric.
Integer count
The number of samples used to generate the metric.
Double avg
The average value of the metric.
Double stdDev
The standard deviation of the metric.
String stringValue
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify
the NumberValue parameter.
Double numberValue
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the
StringValue parameter.
String trialComponentName
The name of the trial component.
String trialComponentArn
The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource trialComponentSource
Date creationTime
When the component was created.
UserContext createdBy
String sourceArn
The Amazon Resource Name (ARN) of the source.
TrainingJob trainingJob
Information about a training job that's the source of a trial component.
ProcessingJob processingJob
Information about a processing job that's the source of a trial component.
TransformJob transformJob
Information about a transform job that's the source of a trial component.
String trialComponentName
The name of the trial component.
String trialComponentArn
The ARN of the trial component.
String displayName
The name of the component as displayed. If DisplayName isn't specified,
TrialComponentName is displayed.
TrialComponentSource trialComponentSource
TrialComponentStatus status
The status of the component. States include:
InProgress
Completed
Failed
Date startTime
When the component started.
Date endTime
When the component ended.
Date creationTime
When the component was created.
UserContext createdBy
Who created the trial component.
Date lastModifiedTime
When the component was last modified.
UserContext lastModifiedBy
Who last modified the component.
String trialArn
The Amazon Resource Name (ARN) of the trial.
String trialName
The name of the trial.
String displayName
The name of the trial as displayed. If DisplayName isn't specified, TrialName is
displayed.
TrialSource trialSource
Date creationTime
When the trial was created.
Date lastModifiedTime
When the trial was last modified.
Float targetObjectiveMetricValue
The value of the objective metric.
String arn
The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
String uiTemplateS3Uri
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.
String humanTaskUiArn
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
String content
The content of the Liquid template for the worker user interface.
String actionName
The name of the action to update.
String description
The new description for the action.
String status
The new status for the action.
Map<K,V> properties
The new list of properties. Overwrites the current property list.
List<E> propertiesToRemove
A list of properties to remove.
String actionArn
The Amazon Resource Name (ARN) of the action.
String appImageConfigName
The name of the AppImageConfig to update.
KernelGatewayImageConfig kernelGatewayImageConfig
The new KernelGateway app to run on the image.
String appImageConfigArn
The Amazon Resource Name (ARN) for the AppImageConfig.
String artifactArn
The Amazon Resource Name (ARN) of the artifact to update.
String artifactName
The new name for the artifact.
Map<K,V> properties
The new list of properties. Overwrites the current property list.
List<E> propertiesToRemove
A list of properties to remove.
String artifactArn
The Amazon Resource Name (ARN) of the artifact.
String codeRepositoryName
The name of the Git repository to update.
GitConfigForUpdate gitConfig
The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the Amazon Web
Services Secrets Manager secret that contains the credentials used to access the repository. The secret must have
a staging label of AWSCURRENT and must be in the following format:
{"username": UserName, "password": Password}
String codeRepositoryArn
The ARN of the Git repository.
String contextName
The name of the context to update.
String description
The new description for the context.
Map<K,V> properties
The new list of properties. Overwrites the current property list.
List<E> propertiesToRemove
A list of properties to remove.
String contextArn
The Amazon Resource Name (ARN) of the context.
String deviceFleetName
The name of the fleet.
String roleArn
The Amazon Resource Name (ARN) of the device.
String description
Description of the fleet.
EdgeOutputConfig outputConfig
Output configuration for storing sample data collected by the fleet.
Boolean enableIotRoleAlias
Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}".
For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
String domainId
The ID of the domain to be updated.
UserSettings defaultUserSettings
A collection of settings.
DomainSettingsForUpdate domainSettingsForUpdate
A collection of DomainSettings configuration values to update.
String domainArn
The Amazon Resource Name (ARN) of the domain.
String endpointName
The name of the endpoint whose configuration you want to update.
String endpointConfigName
The name of the new endpoint configuration.
Boolean retainAllVariantProperties
When updating endpoint resources, enables or disables the retention of variant properties,
such as the instance count or the variant weight. To retain the variant properties of an endpoint when updating
it, set RetainAllVariantProperties to true. To use the variant properties specified in
a new EndpointConfig call when updating an endpoint, set RetainAllVariantProperties to
false. The default is false.
List<E> excludeRetainedVariantProperties
When you are updating endpoint resources with UpdateEndpointInput$RetainAllVariantProperties, whose value
is set to true, ExcludeRetainedVariantProperties specifies the list of type
VariantProperty to override with the values provided by EndpointConfig. If you don't specify
a value for ExcludeAllVariantProperties, no variant properties are overridden.
DeploymentConfig deploymentConfig
The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.
Boolean retainDeploymentConfig
Specifies whether to reuse the last deployment configuration. The default value is false (the configuration is not reused).
String endpointArn
The Amazon Resource Name (ARN) of the endpoint.
String endpointArn
The Amazon Resource Name (ARN) of the updated endpoint.
String experimentName
The name of the experiment to update.
String displayName
The name of the experiment as displayed. The name doesn't need to be unique. If DisplayName isn't
specified, ExperimentName is displayed.
String description
The description of the experiment.
String experimentArn
The Amazon Resource Name (ARN) of the experiment.
List<E> deleteProperties
A list of properties to delete. Only the Description and DisplayName properties can be
deleted.
String description
The new description for the image.
String displayName
The new display name for the image.
String imageName
The name of the image to update.
String roleArn
The new Amazon Resource Name (ARN) for the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
String imageArn
The Amazon Resource Name (ARN) of the image.
String modelPackageArn
The Amazon Resource Name (ARN) of the model package.
String modelApprovalStatus
The approval status of the model.
String approvalDescription
A description for the approval status of the model.
Map<K,V> customerMetadataProperties
The metadata properties associated with the model package versions.
List<E> customerMetadataPropertiesToRemove
The metadata properties associated with the model package versions to remove.
List<E> additionalInferenceSpecificationsToAdd
An array of additional Inference Specification objects to be added to the existing array additional Inference Specification. Total number of additional Inference Specifications can not exceed 15. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
String modelPackageArn
The Amazon Resource Name (ARN) of the model.
String monitoringScheduleName
The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
MonitoringScheduleConfig monitoringScheduleConfig
The configuration object that specifies the monitoring schedule and defines the monitoring job.
String monitoringScheduleArn
The Amazon Resource Name (ARN) of the monitoring schedule.
String notebookInstanceLifecycleConfigName
The name of the lifecycle configuration.
List<E> onCreate
The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
List<E> onStart
The shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
String notebookInstanceName
The name of the notebook instance to update.
String instanceType
The Amazon ML compute instance type.
String roleArn
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. For more information, see Amazon SageMaker Roles.
To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole
permission.
String lifecycleConfigName
The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
Boolean disassociateLifecycleConfig
Set to true to remove the notebook instance lifecycle configuration currently associated with the
notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated
with the notebook instance when you call this method, it does not throw an error.
Integer volumeSizeInGB
The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so Amazon SageMaker can't determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can't decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.
String defaultCodeRepository
The Git repository to associate 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 or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
List<E> additionalCodeRepositories
An array of up to three Git repositories to associate 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 or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
List<E> acceleratorTypes
A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
Boolean disassociateAcceleratorTypes
A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.
Boolean disassociateDefaultCodeRepository
The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
Boolean disassociateAdditionalCodeRepositories
A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
String rootAccess
Whether root access is enabled or disabled for users of the notebook instance. The default value is
Enabled.
If you set this to Disabled, users don't have root access on the notebook instance, but lifecycle
configuration scripts still run with root permissions.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the pipeline execution.
String pipelineExecutionDescription
The description of the pipeline execution.
String pipelineExecutionDisplayName
The display name of the pipeline execution.
ParallelismConfiguration parallelismConfiguration
This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.
String pipelineExecutionArn
The Amazon Resource Name (ARN) of the updated pipeline execution.
String pipelineName
The name of the pipeline to update.
String pipelineDisplayName
The display name of the pipeline.
String pipelineDefinition
The JSON pipeline definition.
PipelineDefinitionS3Location pipelineDefinitionS3Location
The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.
String pipelineDescription
The description of the pipeline.
String roleArn
The Amazon Resource Name (ARN) that the pipeline uses to execute.
ParallelismConfiguration parallelismConfiguration
If specified, it applies to all executions of this pipeline by default.
String pipelineArn
The Amazon Resource Name (ARN) of the updated pipeline.
String projectName
The name of the project.
String projectDescription
The description for the project.
ServiceCatalogProvisioningUpdateDetails serviceCatalogProvisioningUpdateDetails
The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.
List<E> tags
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.
String projectArn
The Amazon Resource Name (ARN) of the project.
String trainingJobName
The name of a training job to update the Debugger profiling configuration.
ProfilerConfigForUpdate profilerConfig
Configuration information for Debugger system monitoring, framework profiling, and storage paths.
List<E> profilerRuleConfigurations
Configuration information for Debugger rules for profiling system and framework metrics.
String trainingJobArn
The Amazon Resource Name (ARN) of the training job.
String trialComponentName
The name of the component to update.
String displayName
The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't
specified, TrialComponentName is displayed.
TrialComponentStatus status
The new status of the component.
Date startTime
When the component started.
Date endTime
When the component ended.
Map<K,V> parameters
Replaces all of the component's hyperparameters with the specified hyperparameters.
List<E> parametersToRemove
The hyperparameters to remove from the component.
Map<K,V> inputArtifacts
Replaces all of the component's input artifacts with the specified artifacts.
List<E> inputArtifactsToRemove
The input artifacts to remove from the component.
Map<K,V> outputArtifacts
Replaces all of the component's output artifacts with the specified artifacts.
List<E> outputArtifactsToRemove
The output artifacts to remove from the component.
String trialComponentArn
The Amazon Resource Name (ARN) of the trial component.
String trialArn
The Amazon Resource Name (ARN) of the trial.
String domainId
The domain ID.
String userProfileName
The user profile name.
UserSettings userSettings
A collection of settings.
String userProfileArn
The user profile Amazon Resource Name (ARN).
String workforceName
The name of the private workforce that you want to update. You can find your workforce name by using the operation.
SourceIpConfig sourceIpConfig
A list of one to ten worker IP address ranges (CIDRs) that can be used to access tasks assigned to this workforce.
Maximum: Ten CIDR values
OidcConfig oidcConfig
Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.
Workforce workforce
A single private workforce. 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.
String workteamName
The name of the work team to update.
List<E> memberDefinitions
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. You should not provide input for both of these
parameters in a single request.
For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups
within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that
make up the member definition must have the same ClientId and UserPool values. To add a
Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more
information about user pools, see Amazon Cognito
User Pools.
For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private
work team in OidcMemberDefinition by listing those groups in Groups. Be aware that user
groups that are already in the work team must also be listed in Groups when you make this request to
remain on the work team. If you do not include these user groups, they will no longer be associated with the work
team you update.
String description
An updated description for the work team.
NotificationConfiguration notificationConfiguration
Configures SNS topic notifications for available or expiring work items
Workteam workteam
A Workteam object that describes the updated work team.
String executionRole
The execution role for the user.
List<E> securityGroups
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.
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.
SharingSettings sharingSettings
Specifies options for sharing SageMaker Studio notebooks.
JupyterServerAppSettings jupyterServerAppSettings
The Jupyter server's app settings.
KernelGatewayAppSettings kernelGatewayAppSettings
The kernel gateway app settings.
TensorBoardAppSettings tensorBoardAppSettings
The TensorBoard app settings.
RStudioServerProAppSettings rStudioServerProAppSettings
A collection of settings that configure user interaction with the RStudioServerPro app.
RSessionAppSettings rSessionAppSettings
A collection of settings that configure the RSessionGateway app.
String variantPropertyType
The type of variant property. The supported values are:
DesiredInstanceCount: Overrides the existing variant instance counts using the
ProductionVariant$InitialInstanceCount values in the CreateEndpointConfigInput$ProductionVariants.
DesiredWeight: Overrides the existing variant weights using the
ProductionVariant$InitialVariantWeight values in the CreateEndpointConfigInput$ProductionVariants.
DataCaptureConfig: (Not currently supported.)
List<E> securityGroupIds
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in
the Subnets field.
List<E> subnets
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.
String workforceName
The name of the private workforce.
String workforceArn
The Amazon Resource Name (ARN) of the private workforce.
Date lastUpdatedDate
The most recent date that was used to successfully add one or more IP address ranges (CIDRs) to a private workforce's allow list.
SourceIpConfig sourceIpConfig
A list of one to ten IP address ranges (CIDRs) to be added to the workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
String subDomain
The subdomain for your OIDC Identity Provider.
CognitoConfig cognitoConfig
The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
OidcConfigForResponse oidcConfig
The configuration of an OIDC Identity Provider (IdP) private workforce.
Date createDate
The date that the workforce is created.
String workteamName
The name of the work team.
List<E> memberDefinitions
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.
String workteamArn
The Amazon Resource Name (ARN) that identifies the work team.
String workforceArn
The Amazon Resource Name (ARN) of the workforce.
List<E> productListingIds
The Amazon Marketplace identifier for a vendor's work team.
String description
A description of the work team.
String subDomain
The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
Date createDate
The date and time that the work team was created (timestamp).
Date lastUpdatedDate
The date and time that the work team was last updated (timestamp).
NotificationConfiguration notificationConfiguration
Configures SNS notifications of available or expiring work items for work teams.
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